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A Determination of the Accuracy of Cone Beam Computed Tomography and Digital Orthopantomography for the
Determination of Bone Quantity in the Mandibular Ramus
by
Dr Robin Gallardi
A thesis submitted in conformity with the requirements for the degree of Masters of Science
Faculty of Dentistry University of Toronto
© Copyright by Dr Robin Gallardi 2013
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A Determination of the Accuracy of Cone Beam Computed
Tomography and Digital Orthopantomography for the
Determination of Bone Quantity in the Mandibular Ramus
Robin L Gallardi
Master of Science
Graduate Department of Dentistry University of Toronto
2013
Abstract
Objective: The purpose of this study was to compare the accuracy of cone beam CT (CBCT)
imaging with digital orthopantomograms for determining bone quantity in the mandibular ramus.
Methods: Twenty-nine cadaveric mandibles marked bilaterally with three fiducial markers were
imaged using both CBCT and digital orthopantomography. After sectioning, four cross sectional
measurements were made on the specimens and on the CBCT images. Two corresponding
linear measurements were made on the orthopantomograms. Statistical analysis was used to
compare the CBCT and orthopantomogram measurements with measurements from the
anatomic specimens. Results: CBCT measurements were found to significantly differ from
those made on the anatomic specimens (P<0.05). Linear measurements from the
orthopantomograms varied by 15.9 percent compared to the anatomic specimens. Conclusion:
CBCT and orthopantomogram measurements were significantly different from those of the
anatomic specimens suggesting inaccuracies in the radiographic technology or a lack of
precision in landmark identification.
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Acknowledgements
I wish to express my deepest gratitude to Dr. Cameron Clokie for all your guidance and
patience throughout the last four years. I could not have done this without your
continued support and encouragement.
I would like to thank my advisory committee members Dr. Ernie Lam, Dr. Mike Wiley
and Dr. Howard Holmes for your constructive revisions and continued support.
A special thank you for Joseph Hasso and Fouad Ebrahim for your tremendous
contribution to this thesis. Your hard work and eagerness to learn is contagious and I
wish you both amazing success in your dental careers.
I wish to thank my family who have supported and encouraged me throughout my
training. It is because of your love and support that I have been able to succeed and
reach the goals that I have set. My accomplishments in life are the result of the values
and morals you have taught.
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Table of Contents
Abstract ii
Acknowledgements iii
Table of Contents iv
List of Tables vii
List of Figures viii
List of Abbreviations xii
Chapter 1: Introduction 1
1.1 Bone Quality and Quantity 1
1.2 Bone Grafting 4
1.3 Autogenous Bone Grafts 4
1.4 Intraoral Graft Harvest 7
1.5 Ramus Graft Harvest 8
1.6 Orthopantomograms in Pre-Surgical Planning 15
1.7 Computed Tomography 18
1.8 Cone Beam Computed Tomography 20
1.9 Cone Beam Computed Tomography and Implant Therapy 20
1.10Cone Beam Computed Tomography Technology 22
1.11Limitations of Cone Beam Computed Tomography 27
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1.12 Cone Beam Computed Tomography in Clinical Practice 28
Chapter 2: Statement of Problem 31
Chapter 3: Objectives and Hypotheses 32
3.1 Objectives 32
3.2 Hypotheses 33
Chapter 4: Significance 35
Chapter 5: Materials and Methods 36
5.1 Anatomic Specimen Preparation 36
5.2 Cone Beam Computed Tomography Images 37
5.3 Orthopantomography Images 38
5.4 Anatomic Measurements 38
5.5 Cone Beam Computed Tomography Measurements 39
5.6 Orthopantomogram Measurements 39
5.7 Data Analysis 40
Chapter 6: Results 42
6.1 Anatomic Data 42
6.2 Inter- and Intra-observer Variability 44
6.3 Comparision of Orthopantomogram and Cone Beam Computed
Tomography Measures to Anatomic Measures 50
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Chapter 7: Discussion 54
7.1 Cadaveric Specimens 54
7.2 Anatomic Gold Standards and Surgical Planning 55
7.3 IAN Canal Identification 59
7.4 Distortion in Orthopantomography 59
7.5 Cone Beam Computed Tomography and Pre-Surgical Planning 63
7.6 Landmark Identification 64
7.7 Image Quality with Cone Beam Computed Tomography 71
7.71 Image contrast and FOV 71
7.72 Noise 72
7.73 Artifacts 74
7.74 Summary 79
7.8 Access to Radiographic Modalities 80
7.9 Clinical Applications in Pre-Surgical Planning 81
Chapter 8: Conclusion 83
Chapter 9: Future Direction 84
Chapter 10: References 85
Appendices 115
Appendix I 115
Appendix II 116
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List of Tables
Table 1 Cadaveric measurements in millimeters of the mean distances
and standard deviations from the alveolar crest to IAN canal,
buccal cortex to IAN canal, average mandible width and height
at the following anatomic locations: ascending ramus,
second molar and first molar sites for all of the cadaveric
mandibles. 46
Table 2 Cadaveric measurements in millimeters. The mean distance
and standard deviations from the buccal cortex to the IAN
canal at various vertical positions of the IAN canal. 47
Table 3 Mean and Standard Deviation (+/- SD) of percent error of 51
measurements from 29 mandibles from CBCT
and Orthopantomogram images compared to anatomic
measures.
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List of Figures
Figure 1 Classification of Bone Quality, type D1 to D4. 2
Figure 2 Caywood and Howell classification of alveolar bone loss. 4
Figure 3 Intra-oral harvest sites of the mandible. Area A is a
symphyseal graft, Area B is a mid-body graft and Area C
is a ramus graft. 6
Figure 4 Outline of a graft from the external oblique ridge. 9
Figure 5 Clinical photo of the outline of a ramus harvest site. 10
Figure 6 Landmark measurement of a ramus graft. Sg, sigmoid
notch; Ct , tip of coronoid process; Cf, anterior point of
coronoid process; Mc, mandibular canal; R, 3 mm posterior
to distal root of third molar; a, point 3mm anterior to beginning
of the mandibular canal; b, point 3 mm anterior to the
mandibular canal; 1, anterior side length of the ascending
ramus; 2, posterior side length of the anterior part of
the ascending ramus; 3, upper horizontal side length
of the ramus; 4, lower side length of the anterior
ascending ramus. 13
Figure 7 Various ramus harvest techniques to maximize bone
quantity. 14
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Figure 8 Sequence of CBCT image acquisition to surgical guide
fabrication in implant pre-surgical planning. 21
Figure 9 A CBCT machine for an office setting. 22
Figure 10 CBCT fan shaped beam. 23
Figure 11 X-ray beam projection scheme comparing acquisition
geometry of conventional or “fan” beam (right) and “cone”
beam (left) imaging geometry and resultant image production. 28
Figure 12 Cadaveric mandible with fiducial markers at the first molar,
second molar and ascending ramus sites. 37
Figure 13 A cross-section of a cadaveric mandible is displayed
on the left. The schematic on the right demonstrates the
four measurements that were made at each section: the
distance from the buccal cortex to IAN canal(black), mandibular
width(red), mandibular height(yellow) and the distance from
the alveolar crest to the IAN canal (blue). 39
Figure 14 A cros-section of a CBCT image is displayed on the left. The
schematic on the right demonstrates the four measurements
that were made at each section; the distance from the
buccal cortex to the IAN canal (black), mandibular width (red),
mandibular height (yellow) and the distance from the alveolar
crest to the IAN canal (blue). 41
Figure 15 An orthopantomogram image. The yellow line represents
the measurement from the alveolar crest to the inferior
cortex and the blue line represents the measurement from
the alveolar crest to the superior cortex of IAN canal. Both
measurements were made at each pin location. 42
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Figure 16 Boxplots showing the percent error of measurements of the
buccal cortex to IAN canal, made on 29 mandibles comparing
dental students and oral surgeons. Boxes enclose the middle
50% of observations. Vertical lines extend to include
approximately 90% of observations. Circles and asterisks denote
outlying and extreme data points. 48
Figure 17 Boxplots showing the percent error of measurements of
mandibular width made on 29 mandibles comparing dental
students and oral surgeons. Boxes enclose the middle 50%
of observations. Vertical lines extend to include approximately
90% of observations. Circles and asterisks denote outlying
and extreme data points. 49
Figure 18 Boxplots showing the percent error of measurements of
mandibular height made on 29 mandibles comparing dental
students and oral surgeons. Boxes enclose the middle 50%
of observations. Vertical lines extend to include approximately
90% of observations. Circles and asterisks denote outlying
and extreme data points. 50
Figure 19 Boxplots showing the percent error of measurements of the
alveolar crest to IAN canal made on 29 mandibles comparing
dental students and oral surgeons. Boxes enclose the middle
50% of observations. Vertical lines extend to include approximately
90% of observations. Circles and asterisks denote outlying and
extreme data points. 51
Figure 20 Boxplots showing differences between replicate measurements
based on CBCT and Orthopantomogram images of 29 mandibles.
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Boxes enclose the middle 50% of observations. Vertical lines extend
to include approximately 90% of observations. Circles and
asterisks denote outlying and extreme data points. 52
Figure 21 Boxplots showing the percent error of measurements based
on Orthopantomogram images of 29 mandibles. Boxes enclose
the middle 50% of observations. Vertical lines extend to include
approximately 90% of observations. Circles and asterisks denote
outlying and extreme data points. 55
Figure 22 Boxplots showing the percent error of measurements based
on CBCT images of 29 mandibles. Boxes enclose the middle
50% of observations. Vertical lines extend to include approximately
90% of observations. Circles and asterisks denote outlying and
extreme data points. 56
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Abbreviations
IAN Inferior alveolar nerve
CBCT Cone Beam Computed Tomography
CT Computed Tomography
2D Two Dimensional
3D Three Dimensional
FOV Field of View
mm Millimeters
cm Centimeters
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Chapter 1
Introduction
Dental implants are becoming a popular choice for the replacement of missing
teeth. With an aging population and ease of access to this technology, more and more
patients are choosing dental implants as a restorative option. Successful outcomes in
implant dentistry are dependent on the integration of the implant with the surrounding
bone.
1.1 Bone Quality and Quantity
Bone availability, as determined by the quality and quantity of cortical and medullary
bone, and the locations of adjacent anatomic structures, is an important determinant of
implant success. When planning for implant placement, each site must be carefully
assessed for the amount of available bone. Once this has been determined one can
select the appropriate implant size so that it may be positioned in the optimal bucco-
lingual and mesio-distal orientation at the proposed site. It can therefore be appreciated
that without an adequate knowledge of bone quantity and quality, the success of implant
therapy can be negatively affected.
Determination of bone quality at a potential implant site is a critical step in predictable
outcomes in implant therapy. Several classifications are available to assist clinicians in
determining bone quality (Misch, 1997; Seibert, 1983). The most frequently utilized
system classifies bone into four categories (Figure 1). D1 bone is composed almost
entirely of cortical bone and is more commonly identified in the anterior mandible. D2
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bone is composed of a thick crestal layer of cortical bone surrounding dense trabecular
bone. This bone type can be found commonly in the anterior and posterior mandible.
D3 bone is composed of a porous crestal layer of cortical bone with fine trabecular bone
beneath. This bone type is found primarily in the maxilla. Finally, D4 bone consists of
fine trabecular bone with only a thin layer of cortical bone. When present, this is almost
entirely found in the posterior maxilla (Misch, 1989). It is the thickness of the cortical
bone and the density of the surrounding cancellous bone that determines the suitability
of a site for dental implant placement. It would therefore stand to reason that sites
having D4 bone will provide the greatest challenge for implant osseointegration, while
those sites having D2 bone would be superior (Wang and Al-Shammari, 2002).
Figure 1. Classification of Bone Quality, Types D1 to D4 (Misch, 1989).
The placement of endosseus dental implants requires adequate bone quantity both in
height (vertically) and width (horizontally). Frequently, patients having had long
standing edentulism, demonstrate less than adequate amounts of bone (i.e. quantity) for
implant placement. Once teeth are lost or removed, resorption of the alveolar ridges
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occurs in a relatively predictable pattern and rate. Tallgren (1972) observed that
although the greatest proportion of bone loss occurred within the first year following
tooth extraction, the process continued at a slower rate over 25 years. The rate of
bone loss was noted to be four times faster in the mandible than in the maxillae
(Tallgren, 1972). Investigations by Cawood and Howell (1988) quantified bone loss
horizontally and vertically and showed variations with different locations within the jaws
(Figure 2). The maxillae and anterior mandible demonstrated patterns of loss in both
alveolar process bone height (vertical) and width (horizontal). In the posterior mandible,
loss of the alveolar process was mainly in height.
Frequently, the pattern and amount of alveolar ridge resorption interferes with the ability
to proceed to dental implant placement without adjunctive grafting to augment the
alveolar ridge (Jensen and Sindet-Pedersen, 1991). The goal of alveolar bone grafting
is to establish a ridge height and width that enables stable placement of a dental implant
that will eventually form the foundation upon which a prosthesis will be delivered to
restore patient function.
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Figure 2. Caywood and Howell classification of alveolar bone loss (Cawood and Howell,
1988)
1.2 Bone Grafting
Numerous options for bone augmentation are available for alveolar ridge grafting.
Xenogeneic bioimplants, allogeneic and alloplastic implants, and autogenous bone
grafts all been extensively studied in the literature as options for augmenting deficient
alveolar sites (Becker et al., 1994). Xenogeneic bioimplants are materials that have
been obtained from a member of a non-human living source such as bovine (cow). In
contrast, allogeneic implants are tissues obtained from one human and then transferred
to another human while autogenous grafts involve using bone obtained from the same
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human individual that will be receiving the graft. Alloplastic biomaterials are
synthetically made implantable bone substitutes.
The morphology and nature of a bony defect are important considerations in the
selection of the appropriate type of bone graft material and grafting technique. Bone
grafts obtained directly from the patient (autogenous) and transferred to another site are
thought to consistently provide predictable outcomes and as such, are considered the
“gold standard” in alveolar ridge augmentation (Hammack and Enneking, 1960;
Lundgren et al., 1999; Triplett and Schow, 1996).
1.3 Autogenous Bone Grafts
Branemark was the first to described the use of autogenous bone grafting to augment
deficient alveolar ridges for endosseus dental implant placement (Branemark et al.,
1975). Autogenous bone grafts can be harvested from a large number of anatomic
sites such as the illiac crest (anterior and posterior), tibia, calvarium, rib, ramus,
tuberosity and zygoma. Both gnathic and extragnathic origins for autogenic graft
material have been described for use in ridge augmentation. The choice of donor site
largely depends on the size of the defect and the type of bone desired. For large
defects or for reconstruction of areas requiring bridging of a bone defect, autogenous
grafts are frequently harvested from the anterior or posterior iliac crest (Tolman, 1995).
Alveolar ridge reconstruction typically requires less bone volume, and as a result
gnathic (intraoral) sources for bone harvest have been used extensively (Figure 3).
Autogenous grafts can be harvested in both block and particulate forms. Block grafts
can be used to reconstruct large bony defects or can be used to augment the alveolar
ridge width as an onlay graft. Particulate forms can be used for ridge augmentation
procedures such as sinus lifts and when combined with a mesh framework can also be
used to bridge small bony defects.
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Once a graft is placed in the recipient site, complete stabilization is necessary to allow
for successful graft healing. Any movement of the graft material may result in failure of
the grafted bone to integrate with the recipient bone (Raghoebar et al., 2006). A large
number of techniques for securing the graft to the recipient site have been described in
the literature (Burger et al., 2011; Degidi et al., 2003; Louis, 2010; Quereshy et al.,
2010). Membranes, titanium and resorbable mesh have been used to stabilized
particulate grafts while fixation screws and plates have been traditionally used for block
stabilization (Beckers and Freitag, 1980; Her et al., 2012).
Figure 3. Intra-oral harvest sites of the mandible. Area A is a symphyseal graft, Area B
is a mid-body graft and Area C is a ramus graft (Kosaka et al., 2004).
Both cortical and cancellous bone can be used for ridge augmentation. Autogenous
bone grafts can be composed completely of cortical bone, cancellous bone or a
combination of both. Depending on the site of bone harvest, variable amounts of each
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bone type will be obtained. Most intraoral harvest sites will provide primarily cortical
bone, whereas extraoral sites such as the anterior or posterior iliac crest will be a
source of both cortical and cancellous bone.
Bone quality plays an important role in implant stability both at the time of implant
placement and over the long-term in the final implant reconstruction phase. Studies
have demonstrated that cortical thickness plays a greater role in primary implant
stability than increases in implant length (Miyamoto et al., 2005). The amount of time
required for a bone graft to integrate at the recipient site is also dependent on the
quality of bone grafted. Cortical bone requires more time as it has a prolonged healing
phase when compared to cancellous bone (Marx, 2007). The quality of the bone
grafted also influences the amount of bone resorption that will occur following graft
placement. Investigators have demonstrated that cortical ramus grafts have a
volumetric resorption rate of approximately 17.5% (Proussaefs et al., 2002). Misch
(2000) suggested that despite the prolonged healing phase, cortical bone grafts have
been found to exhibit minimal resorption and consistent gains in bone volume when
compared to cancellous grafts. It is generally accepted that the use of cortical bone for
alveolar ridge grafting provides predicable results and will allow for optimal outcomes
during implant placement.
Autogenous bone can be harvested from osseous structures formed either by
membraneous (i.e. derived from mesenchyme cells) or endochondral (i.e. derived from
a cartilaginous model) ossification. Examples of membraneous harvest sites include
the calvarium and ramus, where endochondral sites commonly used include the tibia,
the iliac crest and the rib. Investigators have suggested that corticocancellous block
grafts harvested from a membranous donor site showed decreased rates of resorption
compared with those of endochondral donor sites (Misch, 1997; Smith and Abramson,
1974). Early revascularization of the membranous grafts, biochemical similarity and
increased inductive capacity are thought to be the reasoning behind such differences
(Zins and Whitaker, 1979).
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1.4 Intraoral Graft Harvest
Numerous intraoral sites have been described in the literature for harvesting of bone in
order to augment deficient alveolar ridges (Hoppenreijs et al., 1992; Misch, 1996;
Tolstunov, 2009), most of which provide a source of high quality cortical bone. Those
sites most commonly used include, the symphysis, the maxillary tuberosity, the ramus
region and the retromolar trigone. Not withstanding the biological advantages, there are
several operative advantages of using an oral donor site. First, both bone harvest and
grafting can be accomplished in a single surgical site, and second, the procedure can
be performed with the use of local anesthesia without or with some form of sedation.
With the limited availability of operating room time, the ability to harvest bone using local
anesthesia negates the need for a general anesthetic team. Third, the close proximity
of donor and recipient sites reduces operative and anesthesia time, making it a simple
straight forward outpatient procedure. And finally, there can be a significant reduction in
post-operative morbidity given the ease of harvest from a single surgical site (Jensen
and Sindet-Pedersen, 1991; Misch, 1999; Sindet-Pedersen and Enemark, 1990).
1.5 Ramus Graft Harvest
Misch (1996) was the first to describe a technique for bone harvest from the mandible in
the area of the external oblique ridge (Figure 4). Use of the retromolar region has since
been reported for alveolar grafting (Buser et al., 1995; Misch, 1997), sinus grafting
(Wheeler et al., 1996; Wood and Moore, 1988), orthognathic surgery (Braun and
Sotereanos, 1984) and reconstruction after tumor resection (Muto and Kanazawa,
1997). Specifically, this donor area has been used extensively for mandibular
reconstruction (Muto and Kanazawa, 1997), and the lateral plate of the mandibular body
has been used in the repair of complex orbital fractures (Laskin and Edwards, 1977).
Being composed primarily of high quality cortical bone, the ramus region is a preferred
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site of graft harvesting for ridge augmentation prior to implant placement (Jensen and
Sindet-Pedersen, 1991; Sindet-Pedersen and Enemark, 1990).
Figure 4. Outline of a graft from the external oblique ridge (Martini et al., 2001).
The anatomical limits of the ramus graft harvest include the coronoid process of the
mandible posteriorly, the molar teeth anteriorly, the inferior alveolar nerve (IAN) canal
inferiorly and the thickness of the mandible buccal-lingually. The technique for ramus
graft harvest as originally described by Misch (1996) begins with an incision in the
buccal vestibule medial to the external oblique ridge. The incision extends from the
ascending ramus to the mid-molar region, no higher than the level of the occlusal plane
to minimize chances of severing the buccal artery. Once the lateral aspect of the ramus
is exposed, a posterior vertical osteotomy is placed in the area of the ascending ramus
perpendicular to the external oblique ridge (Figure 5). The anterior osteotomy is
positioned in the body in the second molar region. The length of the graft is determined
by the requirements of the recipient site. A superior osteotomy joins both the posterior
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and anterior cuts, and the inferior osteotomy is performed to complete this step in the
harvest procedure. Ideally the position of an inferior osteotomy should remain above
the IAN canal but can be placed below the canal provided care is taken during
procurement of the graft. Chisels are used to gently harvest the bone paying close
attention to ensure protection of the IAN (Misch, 1996).
Figure 5. Clinical photo of the outline of a ramus harvest site (arrow demarcates the
posterior vertical osteotomy).
Harvesting bone from the ramus region is not without concern, and as such, careful
case selection must be undertaken when choosing this surgical procedure. The
potential for negative outcomes range from wound dehiscence to sensory deficits and
jaw fracture (Honig, 1996; Khoury, 1999; von Arx and Kurt, 1998). A study comparing
donor site complications using different harvest sites, determined that the harvest of
bone from the ramus was associated with the lowest percentage of complications
(Scheerlinck et al., 2013). The literature suggests that injury to the inferior alveolar
nerve (IAN) is rarely reported as a complication with ramus grafts. Leong et al. (2010)
reported no incidence of nerve injury and Nkenke et al. (2002) encouraged the use of
the ramus for harvest due to “low strain on the patient and minimal complications”.
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However, the morbidity associated with nerve injuries is significant and techniques to
reduce this risk are essential. A clinician must accurately quantify the amount of ramus
bone available for harvest and the proximity of the IAN canal.
During ramus graft harvest, nerve damage is more likely to occur when the IAN canal is
closer to the buccal cortex near the position of the osteotomies. The path of the IAN
canal is often used as a guide to the osteotomy positioning in pre-surgical planning
(Rachel et al. 1986). Anterior osteotomies are frequently made in the third or second
molar region due to the thickness of medullary bone in this area (Rachel et al. 1986).
Despite numerous descriptions of the anatomic course of the IAN, there is no true
consensus on its path and pattern of distribution in the region of the posterior mandible.
Oliver (1928) first described two typical patterns for the course of the IAN. The first
pattern consists of a single nerve trunk with branches to the individual teeth while the
second pattern is that of a plexus of branches (Oliver, 1928). Further studies classified
the IAN distribution into three separate patterns (Carter and Keen, 1971). More
recently, the first 3-dimensional (3D) reconstructions of the IAN canal position were
created (Kieser et al., 2004). These investigators found sixty-nine percent of
neurovascular bundles were positioned in the middle or lower third of the body of the
mandible. This study did not, however, investigate the proximity of the nerve canals to
the buccal cortex. In a cadaveric study, a close analysis of the IAN canal position,
demonstrated significant heterogeneity among the cadaveric mandibles (Verdugo et al.,
2009).
The amount of buccal bone found between the cortex and the IAN canal is a critical
measurement when considering harvesting bone from the ramus region. As ramus bone
availability is difficult to assess pre-operatively there is the potential risk of damage to
the IAN during harvest. Several anatomic studies have attempted to determine the
average thickness of cortical bone at various sites in the posterior mandible. Cadaveric
studies have demonstrated buccal bone thickness in the retromolar area to be
1.98±0.81 mm to 2.06±0.41 mm (Katranji et al., 2007). Rajchel et al. (1986) determined
the average distance of the IAN to the buccal cortex in the retromolar region to be
3.4±0.9 mm. The data also demonstrated that the position of the canal was closer to
the buccal cortex in the third molar region (Rajchel et al., 1986).
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There have been a limited number of studies directed towards the quantification of bone
in the ramus region. Earlier descriptions of harvest techniques, claim a graft thickness of
4.0 mm can easily be obtained (Misch, 2000). Recent studies present significant
evidence that ramus grafts should be harvested with a thickness of no greater than 3.0
mm (Leong et al., 2010). Many believe that if the ramus width is less than 10 mm then
other harvest sites should be investigated.
One must also take into consideration the vertical height of bone above the IAN canal.
Investigators demonstrated that a vertical cut of 10 mm can be safely performed without
injury to the neurovascular bundle (Smith et al., 1991b). Others studies have since
demonstrated that 10 mm of bone height is frequently not available above the IAN canal
(Nkenke et al., 2002). Misch (2000) described a typical graft size of 15 mm in height
with lengths of up to 40 mm for areas requiring multiple implants. Cadaveric studies by
Gungormus and Yavuz (2002) found that the average length of the various arms of the
osteotomies to be 33.17 mm (superior osteotomy), 37.60 mm (inferior osteotomy), 9.15
mm (anterior osteotomy) and 22.48 mm (posterior osteotomy) (Figure 6). In a similar
study, intra-surgical quantification of graft volumes in 10 patients found the average
volume of bone to be 2.5 mL (Verdugo et al., 2009). While the potential to harvest such
large grafts may exist, there are limitations to access of the entire ascending ramus
from an intraoral approach.
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Figure 6. Landmark measurement of a ramus graft. Sg, sigmoid notch; Ct , tip of
coronoid process; Cf, anterior point of cornoid process; Mc, mandibular canal; R, 3 mm
posterior to distal root of third molar; a, 3mm anterior to beginning of the mandibular
canal; b, point 3 mm anterior to the mandibular canal; 1, anterior side length of the
ascending ramus; 2, posterior side length of the anterior part of the ascending ramus; 3,
upper horizontal side length of the ramus; 4, lower side length of the anterior ascending
ramus (Gungormus and Yavuz, 2002)
Several technique modifications have been reported to maximize the amount of bone
harvest. These include harvesting the full height of the vertical mandible and a J-
shaped graft that extends over the oblique ridge (Figure 7) (Clavero and Lundgren,
2003; Moghadam, 2009). Modifications in harvesting techniques reported produced
ramus bone volumes comparable to those that could be harvested from the mandibular
symphysis (Clavero and Lundgren, 2003). It was suggested that unlike the symphyseal
region, the amount of bone harvested from the ramus is not directly proportional to
patient morbidity. In certain cases, the use of bilateral ramus grafts can alleviate the
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need for larger grafts from a single site or from extra-oral sites like the ilium (Khoury,
1999; Wood and Moore, 1988).
Figure 7. Various ramus harvest techniques to maximize bone quantity (Clavero and
Lundgren, 2003).
It is difficult to accurately quantify the amount of bone available at harvest. Clinical
examination and sounding of bone combined with radiographs has historically been
used for pre-operative treatment planning. Clinical examination involves measurement
of the alveolar width and determination of osseus ridge shape and height in the region
of the proposed implants. Bone sounding using a periodontal probe or a similar device
can be used to directly measure the alveolar bone level and to measure the thickness of
the ridge through the overlying gingiva. Intra-oral radiography can be used as an
adjunct and more recently medical computed tomography (CT) and cone beam
computed tomography (CBCT) have been utilized in more complex clinical scenarios
(Lofthag-Hansen et al., 2009; Makris et al., 2010; Verdugo et al., 2009).
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1.6 Orthopantomograms in Pre-surgical Planning
Panoramic radiography (orthopantomograpy) has been the standard for imaging in the
pre-surgical planning for intraoral bone harvest. Numata and Paatero (1948) were the
first to describe the concept of panoramic radiography involving two adjacent disks
rotating at the same speed but in opposite directions, while an x-ray beam passes
through their center of rotation. Unlike the initial technology, newer machines use a
continuously moving center of rotation, which is generally located near the lingual
surface of the mandible. Some of the more contemporary designs can now vary the
shape of the center of rotation to better conform to the patient’s anatomy. The image
itself is created from a focal trough or image plane. Structures within this layer are well
defined and those objects lying outside this layer are blurred, magnified and/or
foreshortened. The position of any desired anatomic structure in relation to the focal
trough is critical. When landmarks of interest are positioned lingual to the focal tough,
they will appear elongated. Alternatively, structures that lie buccal to this image layer
will appear smaller or narrower. Improper patient positioning can lead to distortion of
the area of interest on the final images (White and Pharoah, 2009).
The introduction of digital panoramic radiography offers several advantages to
conventional techniques. These include faster processing time, elimination of a
darkroom with chemical processing, and a variety of image manipulation tools. The
digital images are captured using either a charge-coupled device (CCD) or a phosphor
imaging plate.
Charge-coupled devices capture the image in an incremental manner that converts the
analog x-ray signal to a digital one, which is delivered to a computer. As the x-rays
strike the detector, x-ray information is converted to visible light in a scintillator, and this
light is collected by a fibre optic plate which, in turn, converts the light to electrons. The
electrons are then captured by the charge-coupled device itself, and this information is
relayed to a computer. Digital images are composed of elements termed pixels that are
arranged in a two-dimensional grid or matrix. Each pixel has a dimension, an intensity
16
value and a specific coordinate that identifies its location in the matrix. The absorption of
electrons by a CCD pixel element generates a small amount of electrical energy
(quantization). The value assigned during quantization represents a shade of gray that
is displayed on the computer screen (Angelopoulos et al., 2008). Contemporary digital
radiographic images can have an intensity value of 12 or more (i.e. 212 or 4096 shades
of gray) (Hatcher and Aboudara, 2004).
Similar to film based panoramic imaging systems, storage phosphor imaging systems
use standard cassettes without intensifying screens, but the traditional film is replaced
with a phosphor plate. The x-ray energy is captured by a phosphor layer, and a latent
image is created similar to conventional films (Hildebolt et al., 2000). A laser scans the
plate, and the energy stored is released. An analog-to-digital converter produces an
electrical signal and assigns a number to the intensity of that signal (Angelopoulos et
al., 2008). Again, this number represents pixel intensity and an image in grey scale is
created (Parks and Williamson, 2002). The scanning process is slightly more time
consuming compared to the charge-coupled device, and the phosphor plates require
exposure to visible light after image acquisition to erase the latent image and prepare
them for reuse.
Orthopantomography while used extensively for pre-surgical planning, can only provide
two-dimensional (2D) anatomical information. Determination of bone quantity using
orthopantomograms with radiologic markers has been used historically (Bartling et al.,
1999). Buccal-lingual width of the alveolar ridge and buccal-lingual positioning of the
IAN canal cannot be assessed (Schwarz et al., 1987). When horizontal distances are
critical for treatment decisions, supplementary intraoral radiographs will need to be
obtained. Despite these limitations, orthopantomography has been used for measuring
the height of residual bone (Tal and Moses, 1991; Vazquez et al., 2008) and after
adjusting for patient positioning, measurements are deemed sufficiently accurate for the
determination of vertical dimensions (Frei et al., 2004).
In the pre-operative stages, orthopantomograms have been fundamental in assessing
the location of certain landmarks specifically, the IAN and the relationship of the nerve
to the roots of teeth and the alveolar ridge crest. Clinicians routinely use the findings on
17
these images to identify cases with the potential for risk of injury to the IAN. Some have
suggested that panoramic images may be more reliable in excluding a surgical risk in
the absence of radiographic evidence than in confirming the location of a particular
landmark (Atieh, 2010).
The diagnostic accuracy and validity for landmark identification of 2D films can be
underestimated due to projection errors and overlapping of structures (Elefteriadis and
Athanasiou, 1996). Superimpositions of bilateral landmarks may detract from the true
anatomy of the patient (Dudhia et al., 2011). Images can be complicated by overlap of
soft tissues, adjacent air spaces, and by “ghost” images of the spine and mandible
(Rushton and Horner, 1996).
Magnification of objects can occur in both horizontal and vertical dimensions with
orthopantomography. The magnification varies with both position and object depth
(Tronje et al, 1985), and the degree of distortion makes direct measurements inaccurate
(Tammisalo et al., 1992). More specifically, these inherent distortions can lead to
considerable variability in the dimensions and magnitude of angular measurements
(Dudhia et al., 2011). Traditional orthopantomograms have shown extreme variability
with errors in measurement of up to 7.5 mm or 27% distortion (Sonic et al, 1994). These
distortions have been confirmed by other investigators who found measurements
performed using an orthopantomogram are overestimated (Georgescu et al., 2010).
Evaluation of accuracy using cadaveric skulls determined magnifications of 18 to 21% in
all vertical measures (Larheim and Svanaes, 1986), with magnification ratios of 1.09 to
1.28 (Yim et al., 2011). Significant differences in magnification occur depending on the
type of equipment used and the location of the desired landmarks during image
acquisition. Adopting a standard magnification ratio for evaluation during treatment
planning is difficult and should be avoided (Yim et al, 2011).
It has been suggested that safety zones of 2 to 6 mm will assist clinicians to overcome
misrepresentations regarding the exact position of a landmark (Worthington, 2004). The
use of such wide margins of error are not clinically acceptable, and could demonstrate a
lack of precision of the instrument. The shortcomings of 2D imaging, even when
combined with clinical qualitative interpretations can lead to suboptimal treatment
18
outcomes. As a result, in more complex surgical procedures the findings on 2D imaging
may warrant further investigation with 3D imaging techniques if there are questionable
issues that arise (Monaco et al., 2004). The application of a more discriminating and
effective imaging modality may be necessary when complex treatment planning
requires further precision (Mah et al., 2003).
1.7 Computed Tomography
Advances in computed tomographic imaging have made it a more important modality for
dentists. CT has always been an essential diagnostic tool in maxillofacial surgery and is
now revolutionizing pre-surgical treatment planning. The popularity in craniofacial
imaging has increased dramatically since the development of higher resolution third
generation helical CT scanners. In basic terms, a CT scanner consists of a fan shaped
x-ray beam which passes through a subject to a series of detectors located on the
opposite side. Images are captured electronically as the linear array of solid-state
detectors and the x-ray source rotate about the patient. The detectors measure the
number of photons exiting the patient and use this information to formulate an image.
In earlier designs, both the x-ray tube and the detector row formed a continuous ring
around the supine patient, who was slowly moved forward a few millimeters at the
completion of each rotation. Newer technology introduced in 1989 allowed for scanners
to acquire image data continuously in a helical or spiral manner. Furthermore,
contemporary systems incorporate multiple detector rows (4, 8, 12, 16, 32 and 64) so
that multiple slices can be acquired with a single rotation of the radiation source. The
photons recorded by the detectors represent the absorption characteristics of all the
elements of the patient in the path of the beam. Computer algorithms use this
information to create cross sectional images.
Image reconstruction is complex and a large number of one-dimensional projections
create a single image. The CT image is a matrix of 3D blocks called voxels, which are
19
the smallest elements of the 3D image volume. A voxel can be compared with the pixel
of 2D images, but with the added dimension of depth. The size of each voxel is
determined by its height, width and thickness, and each pixel is assigned a Hounsfield
number or unit which, represents a tissue’s ability to attenuate the x-ray beam when
compared with water. Medical CT voxels are anisotropic in dimension, meaning their in-
and out-of-plane dimensions are different, the latter being determined by the thickness
of adjacent slices. Today, helical CT scanners are typically standard of care but more
recently multidetector helical CT scanners with many detector rows have been
introduced. With the newest generation of machines, images are captured quickly with
a reduction in patient acquisition time and motion artifact, but at an increased radiation
dose cost to the patient.
There are several disadvantages to this technology, namely limited access (because
these systems are found in hospital settings) and increased radiation dose to the
patient. Radiation doses from medical grade CT scanners are in the range of 830 to
1263 micro-Sieverts, µSv (Mah et al., 2003). When compared to conventional
orthopantomogram radiographs with exposures in the range of 14 to 24 µSv, exposure
doses are significantly higher (Ludlow et al., 2008; Mah et al., 2003). Alternative CT
protocols have been developed in an attempt to reduce exposure without significant
loss of image quality (Hagtvedt et al., 2003).
The advantages of CT over conventional projection radiography include higher contrast
images and the elimination of superimposition of structures in an area of interest. Also,
the ability to select specific slices or regions of an image and the ability to view
landmarks in three dimensions provides more diagnostic information and improved
planning capabilities. Medical grade CT also has the option of the use of contrast
agents to improve visualization of soft tissues and soft tissues pathology.
20
1.8 Cone Beam Computed Tomography
Although developed in late 1990s, cone beam computed tomography has only recently
become available for use by the oral and maxillofacial surgeon. Originally developed for
angiography in 1982, cone beam systems have subsequently been adapted for use in
maxillofacial imaging. Use of this technology in the maxillofacial region was first
reported by Mozzo et al. (1998), and the first commercially available CBCT system was
the NewTom 9000. Since this time, applications in the maxillofacial region have
expanded to include, the evaluation of pathological lesions, orthognathic surgery
planning, implant pre-surgical planning, guided implant surgery and pre-prosthetic
grafting.
Guidelines for preoperative radiographic planning for implant placement, were published
by the American Academy of Oral and Maxillofacial Radiology in 2000 and 2012, and by
the European Association for Osseointegration (Harris et al., 2002; Harris et al., 2012).
CBCT was a relatively new and unexplored technology, and as such these guidelines
did not include the use of this radiographic technique. More recently, investigations
have suggested that CBCT should be considered as a “standard of practice” in implant
pre-surgical treatment planning (Lofthag-Hansen et al., 2009).
1.9 Cone Beam Computed Tomography Technology
CBCT systems can be divided into those capable of imaging a large portion of the
maxillofacial and cranial complex and those that acquire image volumes less that the
size of the entire head. The former are termed large field of view (FOV) systems while
the latter are termed small FOV systems. Hybrid digital panoramic/CBCT units are
available with separate sensors for both large and small FOV systems. Some systems
21
can even provide a 2D digital cephalogram option. The remainder of this review will
primarily focus on a discussion of the large FOV craniofacial machines.
CBCT 3D volumes can be acquired with the patient in a standing, sitting or supine
position. Seated units are the most popular, being comparable in size to a conventional
orthopantomogram radiographic machine, and can similarly be installed in a clinical
office setting (Figure 9). Images are captured using a rotating solid-state flat panel or
image intensifier detector and an x-ray source that are mounted in parallel on a rotating
gantry. The CBCT x-ray beam produces a cone-shaped beam with a circular projection
that falls onto the detector. The cone shaped x-ray beam and the digital detector move
synchronously and in the same direction (Figure 10). Cone beam technology utilizes
less electrical energy and uses the x-ray energy much more efficiently (Sukovic, 2003).
The time for image acquisition is rapid with an exposure as low as 9.6 seconds.
Figure 8. A CBCT machine for an office setting (IlumaTM GE Healthcare).
22
Figure 9. CBCT fan shaped beam (Kau et al., 2005).
Unlike medical CT scanners, CBCT systems reconstruct an entire image volume from a
single rotation of the gantry and not from individual slices (Figure 11). After a complete
revolution around the subject, data can be collected either for the entire maxillofacial
region or for limited areas of interest (Danforth et al., 2003). This technology allows for a
high spatial resolution and a field of view in excess of 40 cm for medical CBCT
purposes and up to 23 cm in diameter anterior-posterior and mesio-distally for dental
purposes (Jaffray and Siewerdsen, 2000).
A single rotation produces 100 to 600 individual frames each with more than one million
pixels and up to 16 bits of data assigned to each pixel. The information obtained is then
used in a process called primary reconstruction to create a volumetric data set. The
initial images are corrected for inherent pixel imperfections and uneven exposure, and
then formatted on a computer. The computer uses the voxel data to reconstruct the
X-Ray Source
Detector
23
volume using a rendering algorithm (Scarfe and Farman, 2008). All available voxels are
compiled into a single volume for visualization.
Figure 10. X-ray beam projection scheme comparing acquisition geometry of
conventional or “fan” beam (right) and “cone” beam (left) imaging geometry and
resultant image production(Scarfe and Farman, 2008).
CBCT voxels are isotropic; that is, their dimensions are equal in the x, y and z planes.
Theoretically, isotropic voxels should yield more accurate measurements in all
dimensions (Scarfe et al., 2006). Some CBCT voxel sizes may be as low as 0.076
mm. In contrast, most medical CT units have voxel sizes of between 0.5 and 1 mm
(Pinsky et al., 2006). The use of megapixel solid-state detectors provides sub millimeter
pixel resolution. Literature exists suggesting that the level of resolution of CBCT images
is higher than that of medical CT (Naitoh et al., 2004). However, a recent investigation
24
found limited differences between both imaging modalities for the depiction of fine
anatomical structures in the mandible (Naitoh et al., 2010).
Patient radiation doses vary between different types of CBCT units, but in general, are
lower than those for medical CT (Tsiklakis et al., 2005). It should be noted that
adjustments in collimation, beam geometry and most importantly FOV size can all affect
the absorbed dose distribution, and hence, the overall radiation dose to the patient. The
effective dose can vary even within the same machine depending on the technique and
parameters that are used. Current guidelines regarding the set up of radiation detectors
and the geometrical calculation of true dose exposure have yet to be agreed upon (De
Vos et al., 2009).
The effective radiation dose from a CBCT scan of the maxillomandibular volume has
been measured to be in the range of 53 to 1073 µSv, depending on the type, model and
imaging protocol used (Jadu et al., 2010). When the radiation dose provided by CBCT is
compared to those of panoramic machines, exposure times can be up to 44 times
greater. However, when compared with medical CT scans for common oral and
maxillofacial radiographic imaging tasks, CBCT has been recommended as a dose-
sparing alternative technique. When one compares the effective dose for a medium
FOV dental CBCT scan, the medical grade CT provide a 1.5 to 12.3 times greater dose
(Ludlow and Ivanovic, 2008). This makes the diagnostic benefit of a medical grade CT a
trade off, as the patient exposure doses can be higher.
In 2007, the ICRP updated the method for calculating effective dose on the basis of the
latest available scientific information regarding the biology and physics of radiation
exposure (Streffer, 2007). Utilizing the 2007 ICRP guidelines, researchers assessed
the risk associated with dental radiography and found it to be 32 to 422% higher than
that estimated according to the 1990 ICRP guidelines (Ludlow et al., 2008). The results
reflect newly available cancer incidence and mortality data, whereas the 1990 ICRP
guidelines were based solely upon mortality data (Ludlow et al., 2008).
Current guidelines published by major scientific societies recommend compliance with
principles of justification and optimization in the prescription of any radiographic imaging
25
study (Harris et al., 2012; Ludlow et al., 2006). Finding a balance between the ideal
diagnostic modality and reduction of patient risk can be challenging for the clinician in
complex surgical cases.
1.11 Limitations of Cone Beam Computed Tomography
While there are many ideal features of CBCT images, there are still limitations to the
technology that can compromise diagnostic image quality (Chan et al., 2010).
Investigators have found that medical CT technology is superior to CBCT in imaging
cortical bone (Loubele et al., 2007). The beam projection geometry of both CBCT and
medical CT combined with the image reconstruction processes can produce varying
types of artifacts. Those artifacts most commonly in found in these 3D imaging
techniques can result from partial volume averaging, undersampling, cone beam effect
and scatter.
Partial volume averaging occurs when voxel size is greater than the spatial resolution of
the object to be imaged. The voxels that are created are not representative of the
tissue, but represent a weighted average of the different CT values contained within the
area (Scarfe and Farman, 2008). This can lead to two or more adjacent tissues of
differing attenuation being averaged together to produce a single voxel value. Definition
between these areas becomes blurred and indiscernible. Partial volume averaging
artifacts often occur in surfaces that are rapidly changing such as along a bony edge or
margin.
Undersampling occurs when too little data is provided for reconstruction leading to the
creation of images with poor signal-to-noise ratios (Scarfe and Farman, 2008). Although
this effect may not severely degrade the images, when resolution of fine detail is
important, undersampling artifacts may negatively impact image quality. By maintaining
the number of basis projection images, this effect can be reduced (Schulze et al., 2004)
The cone beam effect is a potential source of image artifact especially in the peripheral
26
portions of the scan volume. As the number of sections acquired per rotation increases,
the x-ray beam becomes more cone-shaped (Barrett and Keat, 2004). The total amount
of information for peripheral structures is reduced because the outer row detector pixels
record less attenuation. More information is recorded for objects projected onto the
more central detector pixels. The cone beam effect will degrade image quality and
results in image distortion, streaking artifacts, and greater peripheral noise (Schulze et
al., 2004). Clinically, it can be reduced by positioning the region of interest adjacent to
the horizontal plane of the x-ray beam and also collimation of the beam to an
appropriate field of view.
Due to the nature of the cone shaped beam, a potentially large area of interest is
irradiated and this will lead to scattered radiation. The amount of scatter depends on
the size of the field and thickness of the object. The scatter radiation is still processed
by the detector but does not represent actual attenuation of the beam. Scatter creates
noise that degrades the image quality and reduces contrast. Compared with medical
CT, CBCT can have up to 15 times higher scatter levels (Siewerdsen and Jaffray,
2000). The presence of certain metallic structures can affect the quality of the final
CBCT image, leading to increased scatter and streaking artifacts across the film. These
artifacts represent more than a nuisance in that they can affect how a scanner interprets
and reconstructs the surrounding data (Zhang et al., 2007).
1.10 Cone Beam Computed Tomography and Implant Therapy
A distinct advantage of CBCT technology is the ability to plan implant therapy virtually,
with the use of specifically designed 3D software. In the last decade, several computer
based software programs for pre-surgical implant planning have been developed that
utilize images derived from CBCT. Using these programs, clinicians have the ability to
select and virtually “try in” implants of different diameters and lengths in order to select
the implant best suited for a given location (Chan et al., 2010; Jeffcoat et al., 1991).
Using these virtual images, implants can be displaced, rotated and tilted on any axis,
27
and their positions can then be evaluated in a 3-dimensional space. This pre-operative
planning allows for optimal 3D diagnosis and accurate transfer of virtual implant
positions to the corresponding anatomical sites in the patient (Chan et al., 2010). In
2000, rapid prototype medical modeling manufactured from CBCT data became
available to the dental profession. With this advancement, information from CBCT
images could be transferred directly to the patient via a prefabricated surgical stent
(Figure 8). It has been suggested that CBCT guided surgery is superior to non-CT
guided surgery due to its potential to eliminate errors with manual placement (Veyre-
Goulet et al., 2008). The use of software systems with CBCT imaging has become one
of the primary tools used for dental pre-surgical implant treatment planning. Not only
can one select for a particular implant size and length, but alveolar ridge height and
width as well as the proximity of adjacent anatomic structures can be determined.
Areas of inadequate ridge height or width can be identified and then considered for
ridge augmentation procedures.
Having the ability to locate vital anatomic structures in the proximity of the surgical site
can reduce the risk of injury to those structures during the operative procedure.
Specifically, the identification and localization of the IAN canal has been the focus of a
multitude of radiographic studies. A recent cadaveric study by Kamburoglu et al.
(2009), determined that measurements taken from CBCT images were very accurate
when compared with those found when digital calipers were used to measure the actual
dimensions of an anatomic specimen. It was found that CBCT was a useful
preoperative diagnostic tool for identification of the neurovascular bundle (Kamburoglu
et al., 2009b).
According to Widmann and Bale (2006), long-term clinical studies are necessary to
confirm the value of this technology and to justify the additional radiation dose, effort
and costs. Other studies have found that CT guided surgery is not always accurate as
one might expect, with differences of 1 to 2 mm between the planned and actual
placement positions being reported (Drago et al., 2011). Consequently, clinicians must
still be careful to use their expertise and clinical skills when placing implants utilizing 3D
technology and adjunctive computer software.
28
Figure 11. Sequence of CBCT image acquisition to surgical guide fabrication in implant
pre-surgical planning (Jeffcoat et al., 1991).
1.12 Cone Beam Computed Tomography in Clinical Practice
Since the introduction of CBCT in clinical practice, bias still exists towards conventional
films due to the fact that most clinicians are well acquainted with the use of these
imaging methods. Accuracy in the determination of bone thickness and identification of
anatomic landmarks using CBCT is still not certain. When used in combination,
CBCT Image Acquisition
Regions of interest De7ined
Virtual Implant Placement (Dimension, angulation, position)
Treatment Plan Finalized
Surgical Guide Fabrication and Implant Placement
29
traditional 2D images and CBCT together provide increased information and improved
detail.
The accuracy of landmark identification is essential to treatment planning and for the
prevention of unnecessary morbidity during routine surgical procedures. Specifically, a
particular craniofacial landmark must be easily identifiable with a high degree of
precision and accuracy. Landmark identification and concise measurements will be
made difficult if the image quality and contrast are poor. The variables that have
significant influence on the quality of a CBCT image include voxel size, number of gray
levels, signal, and noise. In general, the best quality image is composed of small
voxels, a large number of gray levels, high signal, and low noise (Hatcher, 2012). In
vivo, patient related factors that can reduce image quality include increased soft tissue
thickness and patient movement (Pinsky et al., 2006). As a result of these potential
inherent limitations and patient related factors, the routine use of CBCT technology to
improve landmark identification has yet to be determined and its utilization for everyday
pre-surgical planning as a replacement to traditional 2D films is promising but still ill
defined.
The literature has documented numerous studies comparing CBCT and traditional
radiographic techniques but few compare these modalities in the pre-surgical planning
phase. For the harvesting of a ramus graft and in dental implant planning, identification
of the IAN is critical and the use of radiographic adjuncts such as CBCT are essential to
optimize results. Pawelik et al. (2002) found CBCT images offered a significantly
clearer perception of both the spatial resolution and delineation of the IAN canal when
compared to conventional 2D panoramic images. However, the conventional panoramic
images did score higher overall in visual grading scores of all anatomic landmarks
studied (Pawelzik et al., 2002). In a similar investigation, CBCT images were found to
be superior to panoramic images for identification of the IAN canal (Angelopoulos et al.,
2008) while noting that the posterior third of the canal was always better depicted
irrespective of the imaging modality used (Angelopoulos et al., 2008). A study
comparing CBCT with digital orthopantomograms for linear measures of root resorption,
found panoramic images to be less reliable than CBCT in terms of measurement
accuracy and agreement between observers (Alqerban et al., 2009). Similarly, the
30
accuracy of determination of linear root angulation was found to be less reliable on
digital orthopanotomograms when compared to CBCT images, particularly in the
anterior maxilla (Peck et al., 2007).
Given the number and multiplicity of issues, CBCT images may not necessarily be an
ideal replacement for orthopantomogram images in pre-surgical dental implant or graft
harvest planning. Only more recently have studies investigated whether CBCT
measures accurately reflect clinical findings in a given area of interest. The literature in
this area has demonstrated a wide range of results. CBCT has been shown to be
accurate to within 0.1 to 0.2 mm for measures over long distances between anatomic
landmarks, however, the literature is equivocal for linear accuracy of objects in close
proximity (Molen, 2010). Lascala et al. (2004) used a series of metal spherical markers
to identify measured distances. They found that CBCT imaging underestimated
anatomic linear measurements. In a similar study, Fuhrmann et al. (1995) found that
CBCT overestimated measurements by 0.2 mm. This overestimation increased to 2.2
mm for all intra-bony measurements (Fuhrmann et al., 1995). Conversely, Hilgers et al.
(2005) found no statistical difference between iCat CBCT measurements and those
made on anatomic specimens, and concluded that 3D reconstructions from CBCT
acquired images provided accurate and reliable linear measurements. Similarly, data
comparisons by Baumgaertel et al. (2009) demonstrated reliable and accurate results
with CBCT images having a slight tendency to underestimate the actual measures.
Several studies examining the accuracy of measurements between reference points
comparing CBCT machines and an anatomic gold standard, found mean deviations to
be insignificant (Kamburoglu et al., 2009b; Stratemann et al., 2008). Another
investigation assessed and quantified the accuracy of linear measurements provided by
CBCT with those of dry skulls. This report concluded that CBCT images provided
reliable information and could be used for pre-operative implant planning in the posterior
maxilla (Veyre-Goulet et al., 2008). The wide range of results documented when direct
comparisons are made between measures taken from anatomic specimens and those
taken from CBCT images, suggests that further inquires are warranted to better define
the limitations of this technology.
31
Chapter 2
Statement of Problem
It has been stated that the use of CBCT may provide improved surgical
outcomes by converting a clinical scenario of suspected high surgical risk to one with a
low likelihood of complications (Susarla and Dodson, 2007). By improving our
understanding and therefore reducing the risk of injury to vital anatomic structures, we
should be able to improve patient safety and acceptance rates of complicated surgical
procedures for both patients, and the treating clinicians. Pre-operative evaluation prior
to ramus graft harvest requires intimate knowledge of adjacent and related anatomic
structures, specifically the location of IAN, to reduce post-operative morbidity. The
emergence of CBCT for craniofacial imaging has lead to the evaluation of this
technology when considering bone graft harvest from this anatomic region. The ability of
this modality to further reduce patient risk and therefore improve outcomes has yet to be
determined and requires investigations comparing it to conventional radiographic
techniques. Determination of the utility of CBCT as an alternative to or replacement of
traditional radiography with respect to accuracy for pre-surgical planning was the
purpose of this investigation.
32
Chapter 3
Objectives and Hypotheses
3.1 Objectives
1. To establish anatomic measurements for buccal bone thickness in the molar and
retromolar region of the mandible and to use this information to determine
average bone thickness available for harvesting.
2. To determine the position of the inferior alveolar nerve canal both in a superior-
inferior and buccal-lingual direction.
3. To compare measurements made on CBCT and digital orthopantomogram
images with those of anatomic specimens with respect to the following:
mandibular height, mandibular width, distance from the alveolar crest to the
inferior alveolar nerve canal and the distance from the buccal cortex to the
inferior alveolar nerve canal.
4. To determine the accuracy of measurements made from CBCT and digital
orthopantomograms with those of anatomic specimens.
33
3.2 Hypotheses
Hypothesis #1
H0: The thickness of buccal bone does not vary with location in the posterior mandible.
H1: The greatest thickness of buccal bone can be found in the retromolar region of the
posterior mandible.
Hypothesis #2
H0: The position of the IAN nerve in a superior-inferior direction does not correlate with
a specific thickness of buccal bone.
H1: A superiorly positioned IAN canal coincides with a greater thickness of buccal bone.
Hypothesis #3
H0: Measurements made on digital orthopantomograms accurately reflect those made
on anatomic specimens.
H1: Measurements made on digital orthopantomograms overestimate the true anatomic
measures.
34
Hypothesis #4
H0: Measurements made on CBCT images accurately reflect those made on anatomic
specimens.
H1: Measurements made on CBCT images underestimate the true anatomic gold
measures.
35
Chapter 4
Significance
Typically an appreciation of the anatomic relationships in the posterior mandible
is established in the pre-surgical planning phase using various radiographic and clinical
techniques. A better understanding of the anatomic relationships of the IAN canal
within the posterior aspect of the mandible and the thickness of buccal bone will help to
establish a ramus graft harvest design that maximizes bone quantity while at the same
time reduces the risk to vital anatomic structures. By studying the relationship of the
superior-inferior position of the IAN canal and the associated quantity of buccal bone
may allow clinicians to use the position of the canal on an orthopantomogram image to
deduce the quantity of buccal bone available for harvest. The precision and accuracy of
linear measurements using both CBCT and digital orthopantomograms were compared
to cadaveric anatomic specimens to ascertain the most suitable diagnostic technique for
pre-operative surgical treatment planning.
36
Chapter 5
Materials and Methods
5.1 Anatomic Specimen Preparation
This study was conducted according to the ethical principles on human
experimentation and was approved by the University of Toronto Health Sciences
Research Ethics Board. Twenty-nine formalin fixed mandibles from twenty-nine human
cadavers were obtained from the Division of Anatomy at the University of Toronto. The
mandibles were dissected free of all soft tissues. Of the twenty-nine mandibles, six
mandibles were edentulous and twenty-three were dentate. No demographic
information was obtained on the human remains and the cadavers were not identified
by age, sex or ethnicity.
Twenty-four gauge straight stainless steel fiducial markers were milled to precise
lengths of 10 mm. A slot was prepared in the buccal cortical bone on the right and left
sides of the mandibles using a #4 round bur and a surgical handpiece. Each 10 mm
metal marker was attached with orthodontic wax to the following locations: 1) ascending
ramus; 2) site of the mandibular second molar or approximate site in the edentulous
mandibles; 3) site of mandibular first molar or approximate site in the edentulous
mandibles (Figure 12). The markers were oriented perpendicular to the mandibular
plane. Each marker was labeled sequentially A to F beginning on the right side of
mandible with marker A corresponding to the location of the ascending ramus on the
right side and marker F corresponding to the location of the ascending ramus on the left
side.
37
Figure 12. Cadaveric mandible with fiducial markers at the first molar, second molar and
ascending ramus sites.
5.2 Cone Beam Computed Tomography Images
Cone beam computed tomograms were obtained using the CB MercuRay system
(Hitachi Medical Corporation, Tokyo, Japan). Two technologists following a
standardized protocol with parameter settings of a 9” field of view, 80 kVp and 10 mA
made all images. Each mandible was placed in a 6” x 12” plexiglass cylinder filled with
water to simulate attenuation by the soft tissues normally present. Each mandible was
aligned with respect to the mid-sagittal positioning laser of the CBCT unit. A lateral
anterior-posterior positioning laser was used to adjust table height till the laser was
centered on the mid-ramus region. Images were captured using a flat panal detector.
Primary reconstruction of the data using 1 mm axial slices was performed automatically
after acquisition taking approximately 60 s. CB Works v2 software program (Cybermed,
Seoul, Korea) was used to reconstruct the cone beam volumes.
38
5.3 Orthopantomography Images
The orthopantomogram images were made using a Kodak 9000 3D Imaging Unit
(Carestream Kodak, Rochester, NY, USA). The occlusal plane of the mandible was
oriented horizontally, and the midline was centered corresponding to the midsagittal
laser of the unit. The mandibles were held in place with a prefabricated jig to ensure
reproducible positioning between specimens and no movement during the exposure.
The images were acquired at 62 kVp, 2 mA with a 15 s exposure time and were
captured digitally with a charged coupled device.
5.4 Anatomic Measurements
After the completion of all image acquisitions, each mandible was sectioned vertically
using a band saw (Butcher Boy, Model 8A20, Ayshire Scotland, UK) in the Division of
Anatomy at the University of Toronto. Sectioning was performed at each of the fiducial
markers dividing the mandibles into 6 posterior segments and one anterior segment.
The following linear distances were measured on the medial surfaces of the vertical cuts
in millimeters: 1) distance from the buccal cortex to the buccal cortex of the IAN canal;
2) horizontal width of alveolar crest buccal to lingual at the widest point; 3) vertical
dimension from alveolar crest to inferior cortex; and 4) distance from alveolar crest to
superior cortex of the IAN canal (Figure 13). All measurements were made by six
independent observers (three senior dental students and three senior oral surgery
residents) using a high precision digital microcaliper, calibrated to the nearest 0.1 mm
(Salvin Dental, Charlotte, NC, U.S.A.). To establish the intraobserver agreement, one
observer performed the anatomic measurements twice, one week apart.
39
Figure 13. A cross-section of a cadaveric mandible is displayed on the left. The
schematic on the right demonstrates the four measurements that were made at each
section: the distance from the buccal cortex to IAN canal (black), mandibular width
(red), mandibular height( yellow) and the distance from the alveolar crest to the IAN
canal (blue).
5.5 Cone Beam Computed Tomography Measurements
The cone beam CT images were uploaded into E-Film 2 X 2.1.2 (Merge Healthcare,
Chicago, IL,USA) and then subsequently transferred into the program CB Works 2.1.2.
Coronal reconstructions were used to perform four measurements at the site of each of
the six fiducial markers. Six independent observers (three senior dental students and
three senior oral surgery residents) measured 4 linear distances in millimeters: 1)
40
distance from the buccal cortex to the cortex of the IAN canal; 2) horizontal width of
alveolar crest buccal to lingual at the widest point; 3) vertical dimension from alveolar
crest to inferior cortex; and 4) distance from alveolar crest to the superior cortex of the
IAN canal (Figure 14). All measurements were made to the nearest 0.1 mm. To test for
intra-observer agreement, one observer performed all measurements twice, one week
apart.
Each investigator was trained to manipulate the software and perform measurements
using the software enhancement tools according to their own preference. Training was
performed until the examiner felt comfortable with the use of the program. All
measurements were performed under standardized conditions in a radiology reading
room with dimmed light using Dell (Dell Corporation, Round Rock, TX, USA) 21 and 23
inch Ultrasharp monitors. All of the data collected was complied into an Excel
spreadsheet (Microsoft, Palo Alto, CA, USA) for analysis.
41
Figure 14. A cross-section from a CBCT image is displayed on the left. The schematic
on the right demonstrates the measurements made at each of the sections: the distance
of the buccal cortex to the IAN canal (black), the mandibular width (red), the mandibular
height (yellow) and the distance from the alveolar crest to the IAN canal (blue).
5.6 Orthopantomogram Measurements
The digital images were uploaded into Axium Dental Software (Exan, Coquitlam, BC).
At the site of each fiducial marker, six independent investigators (three senior dental
students and three senior oral surgery residents) measured 3 linear distances in
millimeters: 1) distance from alveolar crest to superior cortex of IAN canal; 2) distance
from the alveolar crest to inferior cortex; and 3) length of the fiducial marker (Figure 15).
All measurements were made parallel to the fiducial markers, to the nearest 0.1 mm.
To test for intra-observer agreement, one observer performed all measurements twice,
one week apart. Each investigator was trained to manipulate the software and perform
42
the measurements using the software enhancement tools according to their own
preference. Training was performed until the examiner felt comfortable with the use of
the program. All measurements were performed under standardized conditions in a
radiology reading room with dimmed light using Dell 21 and 23-inch Ultrasharp
monitors. All of the data collected was complied into an Excel Spreadsheet (Microsoft,
Palo Alto, CA, USA) for analysis.
Figure 15. An orthopantomogram image. The yellow line represents the measurement
from the alveolar crest to the inferior cortex and the blue line represents the
measurement from the alveolar crest to the superior cortex of IAN canal. Both
measurements were made at each pin location.
43
5.7 Data Analysis
The mean distance from the buccal cortex to the IAN canal and the alveolar crest to the
IAN canal was calculated from the anatomic measurements at each of the fiducial
markers. The mean height of the mandible from alveolar crest to inferior cortex and
average width buccal to lingual was calculated from the anatomic measurements at
each fiducial marker site. Intra-examiner reliability was assessed using intra-class
correlation coefficients for all repeated measures. Systematic errors of the inter-
examiner analysis were assessed using paired t-tests. Differences between groups
were calculated for each of the measures. A level of significance was set to P<0.05.
The ratio of measured length to actual length for each fiducial marker was used to
calculate vertical magnification at each site. This magnification factor was then applied
to the panoramic linear measurements at each site to establish the actual length in
millimeters.
The data was analyzed using SAS statistical software (SAS 9.13, SAS Institute, Cary,
NC, USA). Repeated measures models were used to compare mean percent error
between the data of interest. Mixed models were used to analyze the data. All outcomes
were log transformed to meet the assumptions of the models (log percent error).
The outcome variables were defined as percent error, as one of the objectives of this
study was not only in the actual measurements of the mandibles (anatomic measures)
but also in the error (or variance) in measurements made by CBCT and the
orthopantomograms. For each of the four measurements, the true lengths were defined
as the overall average of all of the anatomical measurements. The percent error was
the deviation of a single measurement from the true anatomical measure expressed as
a percentage.
For measurement i then,
Percent error i = ((xi – x ¯ ) / x ¯ )
44
This provided the percent error in measurements made by CBCT and
orthopantomograms, and this result could be equated to the accuracy of the
measurements.
45
Chapter 6
Results
6.1 Anatomic data
The measurements collected from the cadaveric mandibles were used to
calculate anatomic averages for each of the measured distances. The average distance
of the IAN canal to the alveolar crest, buccal cortex to IAN canal, average mandibular
width and mandibular height in the following areas; ascending ramus, second molar and
first molar sites, are displayed in Table 1.
46
Anatomic Location
Alveolar Crest to IAN Canal (mm)
Buccal Cortex to IAN Canal
(mm)
Mandibular Width (mm)
Mandibular Height (mm)
Ascending Ramus
13.7±3.4 3.2±1.5 10.4±2.2 31.5±4.4
Site of Second Molar
13.2±5.3 5.3±1.7 13.1±2.5 22.8±5.1
Site of First Molar
11.9±4.8 5.6±1.6 12.1±2.6 20.6±6.5
Table 1. Cadaveric measurements in millimeters of the mean distances and standard
deviations from alveolar crest to IAN canal, buccal cortex to IAN canal, average
mandible width and height at the following anatomic locations: ascending ramus,
second molar and first molar sites for all of the cadaveric mandibles.
The greatest thickness of buccal bone is located in the area of the first molar while in
the area of the ascending ramus the amount of bone buccal to the IAN canal is the
narrowest. Irrespective of the location in the posterior mandible, the IAN canal on
average can be found approximately 12.3±4.5 mm from the height of the alveolar crest.
The mandibular width ranges from 10.4±2.2 to 13.1±2.5mm. The mandibular width is
greatest in the area of the second molar and is 13.1±2.5 mm. The mandibular height
ranges from 20.6±6.5 mm in the first molar region to 31.5±4.4 mm in the area of the
ascending ramus. The overall average height of the mandible is 24± 5.3mm.
47
Alveolar Crest to IAN Canal Buccal Cortex to IAN Canal
0-10 mm 4.2±1.5
11-15 mm 4.9±1.6
16-20 mm 4.8±1.4
Table 2. Cadaveric measurements in millimeters. The mean distance and standard
deviations from the buccal cortex to the IAN canal at various vertical positions of the
IAN canal.
In attempts to determine if a relationship existed between the vertical positioning of the
IAN canal and the thickness of bone from the buccal cortex to the canal, mean and
standard deviation of buccal bone thickness was calculated at various vertical positions
of the IAN canal (Table 2). The average distance from the buccal cortex to the IAN
canal is 4.2±1.5 mm when the distance of the IAN canal to the alveolar crest is between
0-10 mm. The average buccal bone thickness is 4.9±1.6 mm when the average
distance of the IAN canal to the alveolar crest is between 11-15 mm and when the
position of the IAN canal from alveolar crest is between 16 to 20 mm, the average
buccal bone thickness is 4.8±1.4 mm. Irrespective of the position of the IAN canal the
average buccal bone thickness is approximately 4mm.
6.2 Inter and Intra-observer Variability
To test for inter-observer variability paired t-tests were used to compare differences
between the two groups for each of the measures. None of the differences were
statistically significant (P>0.73). Figures 16 to 19 are box plots comparing the two
48
observer groups. The individual mean and standard deviations for each measurement
location for each group of observers are displayed in Appendices I and II.
Figure 16. Boxplots showing the percent error of measurements of the buccal cortex to
IAN canal, made on 29 mandibles comparing dental students and oral surgeons. Boxes
enclose the middle 50% of observations. Vertical lines extend to include approximately
90% of observations. Circles and asterisks denote outlying and extreme data points.
49
Figure 17. Boxplots showing the percent error of measurements of mandibular width
made on 29 mandibles comparing dental students and oral surgeons. Boxes enclose
the middle 50% of observations. Vertical lines extend to include approximately 90% of
observations. Circles and asterisks denote outlying and extreme data points.
50
Figure 18. Boxplots showing the percent error of measurements of mandibular height
made on 29 mandibles comparing dental students and oral surgeons. Boxes enclose
the middle 50% of observations. Vertical lines extend to include approximately 90% of
observations. Circles and asterisks denote outlying and extreme data points.
51
Figure 19. Boxplots showing the percent error of measurements of the alveolar crest to
IAN canal made on 29 mandibles comparing dental students and oral surgeons. Boxes
enclose the middle 50% of observations. Vertical lines extend to include approximately
90% of observations. Circles and asterisks denote outlying and extreme data points.
Intra-observer variability was assessed against both radiographic method and measure
location in the model. Three-way and all two-way interactions were not significant
(P>0.05) and hence differences between replicate measures are not statistically
significant. Figure 20 displays the results for replicate measures of CBCT and
Orthopantomogram measurements.
52
Figure 20. Boxplots showing differences between replicate measurements based on
CBCT and Orthopantomogram images of 29 mandibles. Boxes enclose the middle 50%
of observations. Vertical lines extend to include approximately 90% of observations.
Circles and asterisks denote outlying and extreme data points.
53
6.3 Comparision of Orthopantomogram and Cone Beam Computed
Tomography Measures to Anatomic Measures
Analysis of the orthopantomogram measures compared with the anatomic standards
demonstrated an average difference of 2.6 ± 0.8 mm or 15.9% (P<0.05) at all three
sites: the ascending ramus, area of the second molar and area of the first molar.
Measures were overestimated 91% of the time compared to the anatomic
measurements.
The percent error for the measurements from the orthopantomograms are shown in
Figure 21. The mandibular height was on average less than that found with the
anatomic measures and the distance of the alveolar crest to the IAN canal was on
average greater than (with a larger standard deviation of 23.7) those of the anatomic
measures.
Analysis of the CBCT measures compared with the anatomic measures demonstrated
an average difference of 2.9 ± 0.5 mm or 24.9%(P<0.05). Forty-eight percent of
measures were less than their anatomic equivalents and 50% were greater than the
anatomic measurements. Only 2% of the measures were equal. The mean percent
difference for each individual measurement at each pin location, are displayed in
Appendices I and II.
The percent error of measurements based on the CBCT images are displayed in Figure
22. Mandibular height and distance from the alveolar crest to the IAN canal were on
average less than the anatomic measures with a similar percent error of 7.0 and 7.2
respectively. The distance of the buccal cortex to the IAN canal was generally greater
than the anatomic measures with a large standard deviation of 26.6. The measures of
mandibular width were on average greater than the anatomic measures and had the
largest percent error of all measurements at 27.9. The largest standard deviations were
found for the measures of the alveolar crest to the IAN canal and the buccal cortex to
the IAN canal at 38.5 and 26.6 respectively.
54
The percent error and standard deviations of the CBCT and Orthopantomogram
measures differed significantly from the anatomic standards P< 0.01 and P< 0.05. This
is shown in Table 3. The average for each measurement at each pin location and the
mean percent difference of the Orthopantomogram measures and CBCT measures
from the anatomic measures for each observer group independently, are displayed in
Appendices I and II. Appendix I displays the results for the Oral Surgery observers and
Appendix II displays the results the Dental Student observers.
MEASURE CBCT ORTHOPANTOMOGRAM
Buccal cortex to IAN Canal 7.8 (26.6) **
Mandible Width 27.9 (16.5) **
Mandible Height -7.0 (8.6) ** -2.9 (5.1) *
Alveolar crest to IAN canal -7.2 (38.5)* 8.2 (23.7) **
* *mean is significantly different from zero P<0.01
* mean is significantly different from zero 0.01<P<0.05
Table 3. Mean and Standard Deviation (+/- SD) of percent error of mandible
measurements from 29 mandibles from CBCT and Orthopantomogram images
compared to anatomic measures.
55
Figure 21. Boxplots showing the percent error of measurements based on
Orthopantomogram images of 29 mandibles. Boxes enclose the middle 50% of
observations. Vertical lines extend to include approximately 90% of observations.
Circles and asterisks denote outlying and extreme data points.
56
Figure 22. Boxplots showing the percent error of measurements based on CBCT
images of 29 mandibles. Boxes enclose the middle 50% of observations. Vertical lines
extend to include approximately 90% of observations. Circles and asterisks denote
outlying and extreme data points
57
Chapter 7
Discussion
A number of radiographic modalities are available for clinicians to use for pre-
surgical planning. Several studies have addressed the reproducibility, validity and
accuracy of available radiographic techniques (Brown et al., 2009; Cavalcanti et al.,
2004; Chien et al., 2009; Greiner et al., 2007; Titiz et al., 2012). The ideal choice would
be one which provides accurate information at a low cost with limited patient risk.
7.1 Cadaveric Specimens
In order to validate new craniofacial imaging modalities, the use of dry human skulls
have traditionally been used. Cadaveric specimens have the advantage of allowing for
direct anthropometric measurements. These can then be used to evaluate the accuracy
of various imaging techniques (Cavalcanti et al., 1999; Hildebolt et al., 1990; Lascala et
al., 2004). Most studies testing geometrical precision of 3D data sets have been
performed on prepared bone (Cavalcanti et al., 2004; Liang et al., 2010; Periago et al.,
2008; Suomalainen et al., 2008), physical test cadavers and measurement phantoms
(Eggers et al., 2008). In order to compare the results of this study to others, similar
parameters were used to determine the accuracy of the imaging modalities to the
anatomical specimens. When evaluating the intra- and inter-observer variability no
statistically significant differences were seen. This supports the use of direct cadaveric
measurements as a reliable standard from which to compare the CBCT and
orthopantomogram measures. As measures were collected from both the right and left
sides of the mandibles, ample data decreased the risk that individual outliers could have
biased the results.
58
Some have speculated that the use of dry skulls might affect the translation of study
findings to clinical applications. Previous research has suggested that clinically the soft
tissue drape may negatively affect image quality and the accuracy of 3D volume images
(Kwong et al., 2008; Periago et al., 2008). This is interesting in that discrepancies were
observed in this study in the absence of soft tissues. The suggestion is that clinically the
presence soft tissues may further increase such distortions, which could affect any final
diagnostic measures. As cadaveric specimens were used, the effects of embalming
may also need to be considered. However, studies have shown no apparent qualitative
effects on resolution and contrast compared with ante-mortem scans for gross anatomic
features (Chew et al., 2006). It has still been recommended that additional studies be
performed to determine the effects of post-mortem processing on visualization of
microstructures.
7.2 Anatomic Gold Standards and Surgical Planning
Averages obtained in this investigation from analysis of the anatomic data sets were
consistent with previous anatomic studies found in the literature. The IAN canal was
found to be 10 to 15 mm inferior to the alveolar crest in the majority of the mandibles.
These findings are in agreement with those of Rajchel et al. (1986). An attempt was
made to establish a relationship between the superior-inferior positioning of the IAN
canal and the thickness of bone from the buccal cortex to the canal. The specimens
evaluated for this investigation demonstrated that irrespective of the position of the IAN,
the average distance of the buccal cortex to the lateral cortex of the IAN canal ranged
from 4.2±1.5 mm to 4.9±1.4 mm in the posterior mandible. Variations in canal position
on 2D radiographic imaging techniques like orthopantomograms, will therefore not
provide any additional diagnostic information in regards to the quantity of buccal bone.
The limits of harvesting bone from the ramus area are dictated by clinical access,
presence and position of molar teeth and the location of the IAN canal. A common
treatment planning error is to overestimate the quantity of bone available for harvesting.
59
It is crucial to obtain accurate measurements of the distance from the buccal cortex to
the IAN canal in order to maximize harvest quantity, while at the same time protecting
the neurovascular bundle. Although the position of the canal is variable, anatomic
averages are helpful when surgical planning. Past studies have found the mean vertical
distance between the superior edge of IAN canal and the external oblique ridge to be
7.0 mm in the second molar region and 11.0 mm in the third molar region (Rajchel et al.,
1986). As a result, it has been suggested that the maximum size of block that can be
harvested is 40.0 mm in length, 10.0 mm in height and 4.0 mm in thickness (Misch,
1997; 2000). The technique originally described used the course of the IAN canal as a
guide for the position of the osteotomies and hence the size of the final graft (Misch
1996). The rationale for this approach is that the thickness of the medullary bone
between the IAN canal and the buccal cortex was considered to be greatest in the areas
of the proposed osteotomies (Misch, 1996; 1997).
Despite the variability in the IAN canal position, the greatest thickness of buccal bone
has been reported in the area distal to the second molar (Rajchel et al., 1986).
Specifically, it was determined that the thickness of bone in the area of the second
molar was 4.0 mm and in the retromolar area it was 3.0 mm. As a result of these
findings it was suggested that the anterior vertical osteotomy for a ramus graft be
placed in the region of the second molar (Smith et al., 1991a). In a similar study, buccal
bone was found to be the narrowest in the area immediately posterior to the third molar
with an average thickness of 1.9 mm±0.3 mm (Verdugo et al., 2009). Similarly in this
study, the thickness of the buccal bone in area of the ascending ramus was measured
to be 3.2 mm and 5.2 and 5.5 mm respectively in the second and first molar regions.
These results are similar to the findings of Levine and colleagues who found the
average thickness of buccal bone in the first molar region to be 5.0 mm and the IAN
position to be 4.9 mm from the buccal cortex (Levine et al., 2007). Graft harvest is
generally taken from the area distal to the third molar and anterior to the ascending
ramus. These results would suggest that the thickness of buccal bone is least in the
region distal to the third molar, which is where the graft is generally harvested. As a
greater dimension of bone has been identified in the area of the first and second molars,
one might consider modifying traditional harvest techniques to extend anteriorly into
60
these regions of the mandible. Several investigators have suggested this alternative
harvest technique as a superior approach (Leong et al., 2010). From the results of
Leong and colleagues, it was inferred that a safe thickness to harvest in the molar
region would be 2.5 to 3.0 mm. However, the IAN was exposed in all 34 cases with no
permanent damage being reported (Leong et al., 2010). The results of the current study
would support this approach and would suggest limiting graft harvest thickness to 3.0
mm distal to the third molar while allowing an increased graft thickness of 4.0 to 5.0 mm
in the first and second molar areas.
In the edentulous mandible, the expected resorption of the alveolar crest leads to less
bone height over the IAN canal. As such, this could mean the potential of increased
injury to the neurovascular bundle when procuring a ramus bone graft (Leong et al.,
2010). When considering the use of the ramus as a graft harvest technique for an
edentulous patient, there are those who suggest that only the anterior part of the
ascending ramus be used for alveolar reconstruction (Muto and Kanazawa, 1997).
Pre-operative planning on a case-by-case basis is required to determine the safest
location for the placement of the desired osteotomies to reduce surgical morbidity.
Based on prior studies the ramus could provide grafts of up to a length of 30.0 mm
(Smith et al., 1991b). It has been reported that a graft size of 37.6 mm in length by
22.48 mm in width with a 9.1 mm thickness could be obtained using a significant portion
of the anterior part of ascending ramus (Gungormus and Yavuz, 2002). However, when
each individual case was carefully scrutinized, it was determined that the thickness and
morphology of the grafts was not homologous. As demonstrated the site of the anterior
ramus can therefore be a source of a large quantity of cortical bone but use of the entire
area is often limited by inadequate intraoral access.
Studies have also demonstrated that the average width of the mandible is 14 mm in the
area from the second molar to the ascending ramus. Some have suggested that the
average width of the mandible may be is a predictor for the thickness of bone available
for harvest. Knowledge of the average width of the mandible in the retromolar area is
not sufficient to predict the available thickness of bone that can be obtained from a graft
harvest. Coupling this information with an understanding of the buccal positioning of the
61
IAN canal is essential. Several studies have tried to correlate mandibular width with
IAN canal position (Rajchel et al., 1986). It has been suggested that if the ramus is less
than 10 mm wide in the retromolar region, then an alternative donor sites should be
considered (Misch, 2000). The results of this investigation determined that the mean
width of the mandible is 11.9 mm, which suggests that width’s greater than 10 mm are
not always present. If a clinician uses the 10 mm width as a guideline to the suitability
of a ramus graft harvest, as often as not this technique will be abandoned. In these
instances modifying the procurement technique may be a more appropriate strategy for
patients having limited mandibular width.
Another area for harvest is the inferior mandible. Clavero and Lundgren (2003)
modified Misch’s technique by harvesting bone along the inferior mandibular body in a
location below the IAN canal. They found that a greater volume of could be harvested
from this location without the increase in sensory morbidity that can be seen when
grafts are harvested from the anterior mandible (Clavero and Lundgren, 2003). Using
the lateral plate of the mandible from the retromolar region to 3 mm distal to mental
foramen, the average bone size that can be harvested was reported at 15 mm in width
by 30 mm in length (Li and Schwartz, 1996). This report recommended that the
thickness of the block harvested remain less than 3.0 mm in order to reduce injury to
the neurovascular bundle.
Soehardi and colleagues (2009) described a surgical technique that involved bone
harvest from the horizontal ramus region for pre-prosthetic surgery. Ninety seven
percent of patients were satisfied with the results, with only two suffering from
temporary hypoesthesia of IAN (Soehardi et al., 2009). Partial cortical bone harvesting
as described by Hwang et al. (2008), minimized invasion into the marrow space with the
intention of reducing nerve damage. They suggested that only a block of 25 mm in
length by 15 mm in width with a maximum thickness of up to 3.5 mm should be
harvested (Hwang et al., 2008). In contrast, the present anatomic study demonstrated
that thicknesses of 3.5 mm may be all of the available buccal bone as opposed to the
partial thickness graft as described by Hwang et al (2008).
62
7.3 Inferior Alveolar Nerve Canal Identification
Recognition of the cortical boundaries of the IAN canal can be challenging and
frequently the appearance of this landmark is not clear. Various explanations have been
given to explain this limitation: 1) the mandibular neurovascular bundle is not always
surrounded by an ossified canal; 2) the cortical margins surrounding the IAN might have
“burned out”; and 3) the resolution might be insufficient to clearly demarcate the
cortication (Liang et al., 2001; Miller et al., 1990).
Several studies in the literature have stressed that the canal is not always surrounded
by a corticated border making it difficult to locate (BouSerhal et al., 2002; Carter and
Keen, 1971; Stella and Tharanon, 1990). The proximity of the canal to the cortical bone
in the projected plane, and the variation in radiodensity (compactness and thickness) of
closely apposed anatomic structures will affect identification of the cortical boundaries of
the canal (Mehra and Pai, 2012). One explanation for difficulty in localization of this
landmark is a dependence upon the geometry of the radiographic projection used
during image acquisition. The angulation of the x-ray beam in panoramic radiography
allows for a parallel projection of the beam on the canal walls, potentially improving
visualization of this landmark when compared to other radiographic techniques (Mraiwa
et al., 2003). In a study evaluating the ease of identification of the IAN canal using
panoramic radiographs, results demonstrated that the canal was found to be identifiable
in almost all cases but the majority of time it lacked a corticated border (Mehra and Pai,
2012). Clinicians must be aware that visibility of the IAN on 2D images is technically
dependent on both the projection geometry and the amount of cortication of the canal
walls (Dharmar, 1997).
There is a significant amount of heterogeneity in the positioning of the mandibular canal
from the lingula to the mental foramen. When this landmark cannot be identified in an
image, clinicians often use their knowledge of the average position of the canal as a
means of identifying the location. Anatomic averages can be helpful while at the same
63
time reliance on averages alone can lead to inaccurate localization of the neurovascular
bundle due to the true lack of homogeneity in the population.
7.4 Distortions in Orthopantomography
Pre-operative surgical planning requires not only an understanding of the relative
positioning of anatomic landmarks, but depends on accurate correlation of this
information with measurements from various radiographic modalities. Once an
understanding of the distances from the operative area to vital anatomic structures has
been determined, the final surgical plan can be established. Traditional image analysis
has been limited to linear and angular measurements between landmarks
superimposed on a 2D image. Orthopantomography remains a first line diagnostic tool
for pre-prosthetic surgical planning, providing information on overall jaw shape, position
of the maxillary sinuses, nasal cavity as well as the position of the IAN canal and mental
foramina. Harvesting of bone from the region of the mandibular ramus requires an
appreciation of the location of the IAN canal and for this purpose linear measurements
are made on panoramic images and represent the first step in the treatment planning
and ultimately in the design of the graft.
The image created by an orthopantomogram is created by linking the rotation of an x-
ray beam and a detector around a patient’s head. Objects outside of the field of view or
center plane are reproduced with distortions. Objects outside the plane that are closer
to the rotational center will be magnified. Due to the image distortion produced in
panoramic images, (Tronje et al., 1981a; b) it must be cautiously used when measuring
bone, especially when attempting to determine bone height above the IAN canal
(Batenburg et al., 1997; Bolin et al., 1996; Lindh et al., 1995). Tronje et al. (1981)
suggested that when greater accuracy is required, measurements on
orthopantomograms are not recommended. Panoramic image distortion can be caused
by inappropriate head position, location of area of interest in relation to the focal trough,
overlapping landmarks and by the type of orthopantomogram equipment which is
64
utilized.
The fact that panoramic images have inherent magnification is generally appreciated.
To determine the exact magnification in a particular area, reference objects with known
dimensions are placed in situ when taking an image (McDavid et al., 1993; Stramotas et
al., 2002). A true magnification can then calculated from the ratio of the projected to true
length of the reference object (Schulze et al., 2000). This resultant magnification must
be taken into consideration when orthopantomograms are used for surgical planning.
The standard of care has been the use of radiologic markers with these images from
which an accurate determination of all measurements, including that of bone height
above the IAN canal can be obtained.
Objects are seen to be enlarged by 15 to 25% in panoramic images and further
distortion occurs with poor patient positioning (Sanderink et al., 1991). A review of 210
implants placed in 80 patients found an average horizontal magnification of 1.20 to 1.32
and an average vertical magnification of 1.23 and 1.31 (Choi et al., 2004). A similar
study investigated the enlargement ratios of implants placed in an edentulous mandible
and it was determined that the mean vertical enlargement in the lateral region of the
mandible was in the range of 1.21 to 1.26 (Gomez-Roman et al., 1999). Others have
found that vertical measures on panoramic images were 2.4 mm greater (12%
magnification) than those of the anatomic specimens (Laster et al., 2005). Vasquez et
al. (2011) found that the magnification factor was constant both in the premolar
(1.28±0.01) and molar regions (1.27±0.01). Differences between the sites were not
found to be statistically significant. (Vazquez et al., 2011). When metal balls were used
for calibration on panoramic radiographs, examinations reported a mean magnification
factor of 1.27±0.03 (range 1.23 to 1.31) in the premolar region, and 1.26±0.03 (range
1.23 to 1.30) in the molar region (Schropp et al., 2009).
Clinically magnification is not easy to predict. Despite the available aides the clinician
can still have difficulty positioning the patient and the area of interest accurately in the
focal trough of the x-ray beam. Standardization of patient head position in the machine
is almost impossible. Freedman and Matteson believe that when a patient’s teeth are
carefully positioned to ensure that they are within the focal trough the result is negligible
65
magnification (Freedman and Matteson, 1977). Variations in sizes and shapes of
patients, makes positioning all aspects of the alveolus within the focal trough very
challenging. Inherent magnification will exist in certain regions of the film. Even small
variations in horizontal positioning can cause image distortions. However, several
investigators believe that if patient positioning is appropriately adjusted, that vertical
dimensions can be sufficiently accurate for measuring the height of residual alveolar
bone prior to implant placement (Frei et al., 2004; Larheim and Svanaes, 1986). Devlin
and Yuan (2013) attempted to determine how image magnification and distortion are
influenced by object size and position. They were able to establish that only certain
places in focal trough achieve zero magnification. Only one type of panoramic machine
was used and they surmised that the trajectory of the moving center of the rotation is
machine specific and as a result, universal standards of magnification could not be
determined (Devlin and Yuan, 2013).
Modifications in object position during image acquisition can further increase distortion
and blurring due to the interposition and overlap of osseous structures, teeth, filling
materials and soft tissues. Fine anatomic structures can become difficult to discern.
Asymmetry of the mandible and alveolus can lead also to increased difficulty in
landmark identification on orthopantomograms that is unrelated to patient positioning.
Correction for these distortions can be challenging if the true cause cannot be
determined or if the asymmetry is not appreciated (Farman 1999).
While vertical measures on orthopantomograms present certain challenges, horizontal
measurements have limited accuracy (Gomez-Roman et al., 1999; Tronje et al., 1981a;
Yeo et al., 2002). Mesio-distal dimensions can become distorted on panoramic
radiographs,(BouSerhal et al., 2002) and it is understood that the distortion in a
horizontal plane follows no consistent pattern and varies widely with location within the
jaw (Kim et al., 2011). Despite the inability to obtain information in a mesial-distal
dimension on panoramic images, it has been deemed to be a safe method of evaluating
posterior mandibular implant placement. In a review of 2584 implant placements in the
posterior mandible, no permanent sensory disturbances were reported with the use of
panoramic images in pre-surgical planning (Vazquez et al., 2008). The results of the
present investigation suggested an overestimation of linear measures in panoramic
66
images when compared to those of the anatomic specimens. If a clinician fails to
consider these potential variations between the actual dimensions and those seen on
the radiographic images, the result could be implant placement or surgical osteotomy
placement near the IAN leading to sensory injury. This is especially important if
orthopantomography is the sole imaging modality being used in the pre-prosthetic
planning phase.
7.5 Cone Beam Computed Tomography and Pre-Surgical Planning
The introduction of CBCT imaging to surgical implant treatment planning has allowed
clinicians to make measurements in dimensions not previously available. These images
are thought to have less distortion with less superimposition as seen with 2D
radiographic imaging. The ability to remove overlapping structural landmarks with
CBCT by selecting for only a specific image layer is to be a distinct advantage over
orthopantomograms particularly in critical areas such as the posterior mandible. The
main advantage for the use of 3D imaging in implant dentistry, is to provide an
assessment of bone height and width of the alveolar crest and the spatial relationship of
the IAN canal or maxillary sinus (Frederiksen, 1995). Today, the accuracy in placing
implants or surgical osteotomies in close proximity to vital structures is more frequently
dependent on the diagnostic measures taken from these imaging modalities.
There is a large body of literature on the accuracy of measurements made on CBCT
images, the results of which are variable. Several investigators have reported that linear
measures made on CBCT are sufficiently accurate for use in pre-surgical planning. A
study evaluating the accuracy of linear measures of bony defects on CBCT images,
found minor differences between the CBCT and the anatomic measures (0.01 to 0.27
mm for width and height) and deemed CBCT to be an accurate diagnostic tool (Pinsky
et al., 2006). Others have discovered larger differences (2.0 mm with a mean percent
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error of 2.31%) between CBCT and anatomic specimens. Interestingly, although the
error appeared higher, once again those that reported considered the results to be
clinically acceptable for craniofacial studies (Periago et al., 2008). Moreira et al. 2009
and Stratemann et al. (2008), determined that CBCT images demonstrated a high level
of accuracy with respect to linear distances when compared to physical measures made
at the time of surgery. They found errors in precision that ranged from 0.01to 0.19% and
having means that varied from 0.04 to 0.31 mm. The differences identified in this study
were considered not to be of clinical significance (Moreira et al., 2009; Stratemann et
al., 2008). CBCT cephalograms were found to be more accurate compared to traditional
lateral cephalograms for linear measures in a sagittal plane (Moshiri et al., 2007).
Timock et al. (2011) found inter-rater reliability with an absolute difference equivalent to
0.3 mm when comparing buccal bone thickness on CBCT images versus anatomic gold
standards. Overall measurement accuracy showed mean differences to be nearly zero
with no trend to overestimate or underestimate the linear distances (Timock et al.,
2011). In a study by Suomalainen and collegues, linear measures made on CBCT
images of both height and width, were found to be reliable and accurate when
compared with those of the cadaveric mandibles. They concluded that CBCT should be
considered as a reliable tool for implant planning (Suomalainen et al., 2008).
Results presented in this investigation found statistically significant differences when the
CBCT linear measures were compared with those measures from the anatomic
specimens. Several studies have demonstrated similar results regarding accuracy and
reduced image quality with CBCT. One group reported that CBCT measurements were
consistently less than those taken from the corresponding direct anatomic specimens,
particularly over longer dimensions (30 to 100mm). Differences were reported in the
range of 3.43 to 6.59 mm less than their anatomic counterparts (Lascala et al., 2004).
Similarly, Baumgaertel et al. (2009), demonstrated a trend for underestimating CBCT
measurements when compared to anatomic equivalents, particularly when calculating
multiple measures. In opposition to the results previously discussed by Moshiri et al.
(2007), a more recent study demonstrated statistically significant measurement errors
when comparing cephalometric landmarks on CBCT cephalograms to anatomic
specimens. The significance became even greater when several measurements were
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combined (Berco et al., 2009). Of further interest, several studies have shown that the
smaller the distance measured, the greater the inter-examiner variability and the greater
the deviation from the measurements taken from the anatomic equivalents (Cavalcanti
et al., 2004; Periago et al., 2008). These discrepancies from the true anatomic findings
can lead to inaccurate treatment planning when CBCT is used. Careful attention has to
be made when solely relying on CBCT technology for surgical planning, as the potential
for inaccuracy exists. Clinically, measures with even a 5 to10% potential error could be
of great significance in any pre-surgical planning especially when considering implant
placement or ramus graft harvest in the posterior mandible (Dalessandri et al., 2012).
7.6 Landmark Identification
When significant discrepancies exist between measurements made on CBCT images
and corresponding anatomic specimens, consideration must be given to the accuracy of
anatomic landmark identification as a cause for this variance. Inconsistency in landmark
identification has been suggested as the main source of errors in inaccurate
measurements. Measurement errors as a consequence of inappropriate landmark
identification can lead to unexpected surgical complications. The clinical significance of
such errors depends on the level of precision required for the surgical procedure.
Most research examining the accuracy of landmark identification, focuses on variability
in discerning cephalometric landmarks. Van Vlijmen et al. (2010) reported the
reproducibility of landmarks on conventional cephalograms was higher compared with
those of CBCT images of the same skull. Investigations by Chang and others, found
landmark identification errors to be higher with CBCT for certain anatomic points, while
other landmarks were more easily identified using this imaging modality. This was
attributed to specific characteristics of the landmark itself and differences in contrast
(Chang et al., 2011). Several additional reports confirm that variability in landmark
identification follows characteristic patterns and is directly associated with measurement
inaccuracies (Chen et al., 2004; Kamoen et al., 2001; Lou et al., 2007).
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Landmark variability can be structure or image related. The shape and position of the
landmark in relation to any surrounding anatomic boundaries can affect the ease with
which certain structures are identified (Chen et al., 2004; Lou et al., 2007). Landmarks
found on anatomically formed edges or crests are easier to identify when compared with
those on curves with wide radii (Baumrind and Frantz, 1971). Medelnik and colleagues,
found similar results, stating any landmark on a curve or prominence will have a greater
standard deviation and hence any measurements made involving such landmarks will
also have greater inaccuracies (Medelnik et al., 2011). Bilateral landmarks have been
found to be more easily identified on CBCT as opposed to traditional cephalograms
(Ludlow et al., 2009). Delmare et al. (2010) attempted to reduce the superimposition of
bilateral landmarks by adjusting image acquisition parameters. The results suggested
that there was no benefit of this approach for landmark identification and they surmised
that position and shape of the structure itself was most likely the cause for the
inconsistencies (Delamare et al., 2010).
Similarly, the inaccuracies found in the measures of the present study could be due to
an inability to consistently identify the start and end points of the linear measures. As
the difficulty in identification of a landmark increases, the standard deviations of
repeated measures involving a given landmark increase as well. Measures involving
the IAN canal demonstrated the largest standard deviations. These results suggest that
the location IAN canal is more difficult to identify with consistency, leading to a higher
variability in the final measures irrespective of the observer and their level of training.
When examining CBCT images, Ludlow et al. (2009) found standard deviations for
repeated measures between landmarks in the posterior mandible to be greater than in
any other maxillofacial region. This variability was deemed largely due to an increased
difficulty in landmark identification (Ludlow et al., 2009). Other investigators found high
variability and large standard deviations when landmarking and measuring small
periodontal defects on CBCT images (Grimard et al., 2009).
Several studies have used radiopaque markers to aid in landmark identification
(Lagravere et al., 2008; Matteson et al., 1989). With the use of titanium markers on
cadaveric mandibles, one group evaluated the accuracy of linear measurements in
CBCT, and found a mean measurement error less than 1.0 mm (Lagravere et al., 2008).
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Matteson et al. (1989) similarly found improved accuracy of 3D images with use of
metallic markers as landmarks. Radiolucent markers are much more difficult to identify
and are the reason many studies on accuracy rely on the use of a radiopaque or
metallic landmarks (Berco et al., 2009). The accuracy of the aforementioned results
may be a reflection of the ease of identification of the measurement start and end
points. The presence of an easily identifiable metallic marker can provide for a more
consistent location from which to begin the measures. Although this approach does
demonstrate accuracy of the technology, it does not simulate a true clinical environment
where precision can be influenced by the ease or difficulty in true landmark identification
(Kragskov et al., 1997).
Landmarks that provide good contrast, such as tooth enamel are more easily detected
as compared to landmarks at the junction of tissues with similar radiopacities. Smaller
structures can be more difficult to visualize and those that have a grey scale similar to
the surrounding anatomy can be difficult to discern from the neighboring landmarks. The
accuracy of identifying the intersection between two materials of different densities is
limited by the size of each voxel in the image (Leung et al., 2010). In a study of CBCT
imaging, identification of the cemeto-enamel junction (CEJ) was found to be more
accurate than that of a bony margin (Leung et al., 2010). The CEJ is the junction
between the enamel and the cementum, two tissues with differing densities. Large
standard deviations were found for the measures of mandibular height in the present
study. One would expect less variability in this measure due to an increased likelihood
of identifying the crest and the inferior cortex due to the increased density of these
landmarks. This variability may be attributed to fact that images of mandibles without
soft tissues can suffer burn out of thin anatomical structures. This is termed burn out
effect. One such area is the alveolar crest, making the exact identification of this
relatively dense landmark, potentially impossible (Liang et al., 2001).
The accuracy in determining the junction between two tissues with similar opacities is
limited not only by voxel size but also by the physical spatial resolution of the image.
The measure of how closely individual lines can be resolved in an image, or the ability
to differentiate between two objects in close proximity, is termed spatial resolution.
Spatial resolution depends on the properties of the system creating the image and not
71
simply on the voxel size. Ideally, spatial resolution would be equal to the voxel size in an
image, however, obtaining this level resolution can be difficult due to noise and other
image artifacts.
Landmarks or anatomic structures with dimensions that fall below the threshold for
resolution, may not be detected by the machine and therefore may be missed. Such
landmarks may not be visible on the final image and any measures involving these
landmarks will be inaccurate. Leung et al. (2010) found CBCT images had a higher
false negative rate for the detection of bony dehisences and attributed the results to a
thickness of bone that was less than the detectable resolution of the machine. Low
spatial resolution might explain the difficulty in distinguishing the interface between the
bony landmarks in the present study. The fact that the IAN canal and the surrounding
bone have similar radiopacities can make locating this landmark less reliable. A lack of
cortication and medullary bone with large trabeculations can also contribute to difficulty
in the identification of the IAN canal.
Despite the provision of the third dimension, the spatial resolution of CBCT images is
approximately 1.2 to 6.5 line pairs per mm-1 and is inferior to both conventional films
(approximately 20 lp mm-1) and digital images (ranging from 8 to 20 lp mm-1) (Scarfe et
al., 2010). Ballrick and colleagues demonstrated that the average image resolution for
the clear separation of 4 lines on a CBCT (with a flat panel detector) was 0.622 mm for
a 6 cm FOV and 0.860 mm for a 13 cm FOV(Ballrick et al., 2008). Others demonstrated
that the resolution of a CBCT with an image intensifier detector and charge-coupled
device was about 0.6 mm for a 25 cm FOV (Bab et al., 2001). The superior spatial
resolution of plain films may suggest that these modalities are a better choice when
surgical planning requires accurate measures of fine anatomic structures. Other
investigations have suggested that CBCT offers comparable or somewhat superior
spatial resolution in comparison to medical CT images, but noted that specifically soft-
tissue contrast resolution is reduced (Xu et al., 2012). Studies by Yu et al. (2010)
demonstrated that compared to medical CT, CBCT has higher spatial resolution only if
high-resolution modes are used. The use of high settings can improve image resolution
but can lead to clinically unacceptable image noise (Yu et al., 2010). (Bab et al., 2001;
Ballrick et al., 2008). Maximizing resolution while minimizing noise can be a difficult
72
balance, and can lead to sacrifices in overall diagnostic image clarity.
An important consideration is the distinction between measurement accuracy and
spatial resolution. Investigators have suggested that studies reporting measurements
should include resolution and voxel size (Molen, 2010). The accuracy and precision of
any measurements are ultimately limited by the spatial resolution of the scan (Pinsky et
al., 2006). Measurements that approach the spatial resolution of the image may be less
consistent than measures that largely exceed it. This may explain the inaccuracies and
variability presented in this paper, associated with the smaller measures from the buccal
cortex to IAN canal.
Landmark identification in 3D is more time consuming than in conventional 2D imaging,
requiring identification in coronal, sagittal and axial views (Ludlow and Ivanovic, 2008).
Some landmarks may be easily identified in one or two planes, but with more difficulty in
the third plane. Results of studies by several investigators have established that the
reliability of landmark identification differed within the three planes of CBCT images
being more or less reliable in certain planes (de Oliveira et al., 2009; Kusnoto et al.,
1999). Examination in the three planes of space takes additional time and leaves room
for introduction of errors. In attempts to reduce the effects of this variable, the use of
fiducial markers in the present study identified a consistent slice where measurements
were to be taken by each of the observers. It has been stated that measurement
accuracy depends on the landmark itself and the direction or orientation of that
landmark within the image slice (Medelnik et al., 2011). When conducting an evaluation
of the suitability of a 3D imaging modality for measuring distances and angles, it is
essential to first check the reproducibility and reliability of the measured landmarks in all
axes (x, y and z-axes).
The data set generated from 3D image acquisition can be accessed in many different
ways and the operator can visualize the images through series of sections. 3D images
can be reformatted into 2D images allowing the viewer to scroll through in many planes
and directions depending on the section thickness. The selection of processing
parameters including cross-sectional slice thickness and interslice interval, are chosen
by the operator and depend on the imaging study being performed. Thinner slices may
73
aide in identification of fine anatomic landmarks whereas thicker more widely spaced
sections may be sufficient for gross diagnosing (Chadwick and Lam, 2010). Recent
investigations have found that slice thickness affects the appearance of CBCT
reconstructed images and statistically significant differences in bone height measures
were found when only slice thickness was varied (Chadwick and Lam, 2010). It is
suggested that small interslice intervals may introduce noise that can affect the
accuracy of identifying areas like fine boney crests leading to inaccurate information.
This can be especially vital when quantification is necessary.
Variability in locating landmarks will always be a limitation whenever human-based
systems of identification are utilized. It should be assumed that human based
approaches will likely never be capable of reproducing precise anatomic
measurements. In contemporary clinical practice, one should always consider that
operator measurements are subjective and error is part of the process (Kamoen et al.,
2001). The acceptable degree of error depends on the type and complexity of the
treatment procedures (de Oliveira et al., 2009). Variables, including individual
perception and lack of professional training, may have an influence on the magnitude of
error in landmark identification. Therefore there must be a minimum acceptable level of
variability and tolerable margins of error must be discerned. Mean measurement errors
of 0.1 to 4.0 mm were found in an investigation by Lagravere et al. (2010). They stated
that differences up to 1.0 to 2.0 mm are clinically acceptable, but that any measures
with differences greater than 2.0 mm should be used with caution (Lagravere et al.,
2010). Reliability studies on evaluating cephalometric landmark identification in CBCT
images considered differences from the anatomic specimens below 1.0 mm to be
precise (Chen et al., 2000; Richardson, 1981). Chien et al. (2009) found dispersion
errors of less than 1.0 mm for mean estimates of differing landmarks comparing 2D and
3D imaging. These differences were considered to be clinically insignificant, and
suggested that errors in 3D be resolved through better manipulation of the image layers
or by increasing observer experience (Chien et al., 2009).
Variability in study methods, the use of different radiographic machines and a variety of
measures provides for a large body of literature on radiographic measurement
accuracy. As a result, there is a wide range in differences from the anatomic standards,
74
which makes determination of a universal margin of error nearly impossible. In the past,
a safety limit of 2 mm above the IAN canal was considered to be sufficient in implant
surgery to avoid nerve injury (Bartling et al., 1999). Others have stated that in the
preoperative evaluation for implants, measures are considered to be acceptable within
an error range of 1.0 to 2.0 mm (Kamburoglu et al., 2009b; Nasel et al., 1999). Results
of this study have found a discrepancy of 2.4 to 2.9 mm between measures made on
CBCT generated images, digital orthopantomograms and the anatomic specimens.
These findings would suggest that clinicians should assume up to a 3 mm margin of
error when making any pre-surgical measures. Risk can be elevated if a practitioner
decides to neglect this margin of error and studies still have not fully determined the
extent of this limitation. Reliable margins of error for linear measurements need to be
determined, yet with the variability in study methods, assumptions of definitive accuracy
from any one study may not be applicable to all clinical situations.
7.7 Image Quality with Cone Beam Computed Tomography
The amount of variability found in the present study between the radiographic measures
and the anatomic measures suggests that image quality may have been a factor
preventing accurate interpretation. The ability to appropriately interpret a radiograph not
only depends on the inherent characteristics of the anatomic landmarks of interest but
also on many additional factors that affect image quality (Sayinsu et al., 2007; Yu et al.,
2008). Poor image quality prevents the clinician from clearly identifying the area of
interest and is a major source of inappropriate interpretation of a film. Without the ability
to clearly identify anatomic landmarks pre-surgical measurements cannot be made.
Past research has demonstrated significant variability in image quality between CBCT
and medical CT as expressed through the ability to depict various anatomical structures
in the maxillofacial region. Specifically, when compared to CBCT, medical CT was
deemed to have improved image quality for the detection of cortical bone (Loubele et
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al., 2007). Image quality is determined by the signal-to-noise ratio of the image, and
this is affected by kVp, mA, scatter and artifacts. It is important to expand on each of
the above factors to demonstrate the effects that each can have on clinical image
quality.
7.71 Image Contrast and FOV
Radiographic image quality is defined as the amount of information within the image
that allows the radiologist to make a diagnostic decision with a particular level of
certainty (Martin et al., 1999). Image quality is affected by two factors; contrast and
image definition. Contrast is the difference in optical density in a radiograph and is
influenced by spatial variation of the x-ray photon intensities that are transmitted
through the patient. Image contrast is affected by quantum noise, object absorption and
scatter radiation. Image definition depends on the size of the anatomical detail,
contrast, spatial resolution and noise (Medelnik et al., 2011). Ideally diagnostic images
also need to posses fidelity and diagnostic image clarity (Kundel, 1986). Fidelity is the
degree to which a radiographic technique accurately reproduces the image of its input
signal. It can be expressed in terms of signal to noise ratio, spatial resolution and the
absence of distortion. Image clarity is generally expressed in terms of diagnostic
accuracy (Kundel, 1986).
It is generally assumed that by increasing image quality one can increase the diagnostic
accuracy. It is known that image quality is degraded when the imaging dose
decreases. The diagnostic quality of an image can be increased when greater kVp and
mA settings are chosen (Kwong et al., 2008). This does however occur at the expense
of increased exposure to the patient (Jadu et al., 2011). Generally speaking, low dose
scanning can be achieved by either fixing the number of projections while decreasing
mAs, or fixing the mA level while decreasing the number of projections. The visibility of
an object relies on contrast and dimension and hence the clinically acceptable lowest
imaging dose level is task dependent. A recent investigation suggested that 72.8 mAs
is a safe dose for visualizing low-contrast objects and that imaging doses lower than 40
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mAs will lead to dramatic image degradation and should be used cautiously (Yan et al.,
2012). Clinically we must optimize not only image quality but also radiation dose to the
patient (Tanimoto, 2009). Current radiation safety philosophies are based on the
assertion that every radiation dose of any magnitude can produce some level of
detrimental effects, which may be manifested as an increased risk of genetic mutations
and cancer. Needlessly increasing exposure dose to decrease noise and improve
image quality is a contradiction to the ALARA (As low as reasonably achievable)
principle (Dykstra, 2011). As compared to 2D radiographic techniques, CBCT imaging
provides additional information but with the disadvantage of an increased radiation dose
to the patient. The necessity of such advanced imaging techniques for everyday use,
considering the concern of patient exposure is unknown. Technical advances have
reduced patient radiation exposure but the constant desire to obtain higher-quality
images creates a complex interplay among these variables. The principle of data
collection in CBCT does allow for a partial volume of the region of interest, which can
reduce overall patient exposure (Scarfe et al., 2006). In the selection of a pre-surgical
imaging modality, clinicians must consider the ability of the radiographic technique to
have adequate diagnostic quality to clearly depict the landmarks of interest. Selection
of a diagnostic technique with a higher patient exposure may be necessary to achieve
the desired surgical results.
The FOV used during image acquisition is one of the most important factors in image
quality. It is the FOV that determines the size of the voxel used, and therefore, the
image resolution. Most studies using medium to large FOV use a voxel size of 0.4 mm
in their scans, whereas others assessing limited FOV may use a 0.2 mm or smaller
voxel size (Hilgers et al., 2005; Kobayashi et al., 2004; Mischkowski et al., 2007). No
improvement in image resolution was found by Tanimoto and others, below a voxel size
of 80 microns and these workers surmised that this was the lower limit for improvement
of image quality (Tanimoto, 2009). A recent study has demonstrated that fine anatomic
structures like trabecular bone, the periodontal ligament and the lamina dura, were the
least visible anatomic landmarks when comparing CBCT machines with various FOV
settings. The machine using the smallest FOV was superior to all of the others in
demonstration of the aforementioned structures (Liang et al., 2010). The size of the
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FOV also correlates to image noise. The smaller the FOV or the smaller the voxel size,
the higher the noise and artifacts that can be found within the final image (Liang et al.,
2010). As such, a smaller FOV with a thin slice setting does not necessarily directly
translate to better image quality or higher spatial resolution.
7.72 Image Noise
Image quality and hence resolution, are also highly correlated with image noise. In all
imaging procedures that use x-ray or gamma photons, most of the image noise is
produced by the random manner in which the photons are distributed within the image.
This is designated as quantum noise. The amount of noise is determined by the
variation in photon concentration from point to point within a small image area. Noise
represents itself as inconsistent attenuation or grey values in the projection images, i.e.
large standard deviations, in areas where a constant attenuation should be present
(Schulze et al., 2011). Quantum noise can be reduced by increasing the concentration
of photons (i.e., the exposure) used to form an image (Gies et al., 1999). The signal-to-
noise ratio which is often used as a parameter for the image quality, reaches higher
values within the high-dose modes (Seeberger et al., 2012). However, they did,
demonstrate that the use of low-dose modes are possible without a significant reduction
in image quality.
Noise diminishes the ability to depict small landmarks and those of low contrast that are
close to the visibility threshold. Image noise has been shown to be higher in CBCT
compared to medical CT(Medelnik et al., 2011). When compared to medical CT, CBCT
has been known to produce images with reduced contrast resolution and higher ‘‘noise’’
(more graininess) due to the projection geometry, and limitations in detector sensitivity
(Scarfe et al., 2010). Despite these findings many still believe that the influence of
noise on CBCT images is clinically acceptable. (Liang et al., 2010; Mozzo et al., 1998).
When selecting CBCT for pre-surgical planning, a clinician may find a higher noise level
in the final images compared to other imaging modalities (Fast et al., 2012; Kamburoglu
et al., 2009a) The effects of noise on identification of landmarks and on linear
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measures will likely vary from case to case and will depend on the specific landmarks of
interest.
7.73 Artifacts
Image noise is only one type of artifact that can degrade the diagnostic quality of a
radiographic image. CT images are inherently more prone to artifacts than conventional
radiographs, as the image is reconstructed from up to a million independent detector
measurements (De Vos et al., 2009). An image artifact may be defined as a visualized
structure in the reconstructed data that is not present in the object under investigation.
Artifacts may be considered as a source, or type, of noise and can further compromise
image quality and the ability to accurately identify anatomic landmarks.
In CT terminology the term artifact refers to any systematic discrepancy between the CT
numbers in the reconstructed image and the true attenuation coefficients of the object
(Barrett and Keat, 2004). Although there are a vast number of artifacts found, the
following are considered to be the most common and the most disruptive to image
quality: extinction artifacts; beam hardening artifacts; partial volume effect; ring artifacts;
motion artifacts (misalignment artifacts) and scatter(Schulze et al., 2011).
Artifacts arise as a result of the interaction between the subject and the machine; it is
therefore also useful to classify them by the nature of the error made in the scanning
process. Sources for CT and CBCT image artifacts include: (a) Physics-based artifacts,
which result from the physical processes involved in the acquisition of CT data. Artifacts
caused by beam hardening and x-ray scatter are examples of physics-based artifacts;
(b) Detector-based artifacts, which result from imperfections in detector function. The
ring artifact is the most prominent artifact in this category; (c) Patient-based artifacts,
which are caused by such factors such as patient movement or the presence of metallic
materials in or on the patient and (d) Artifacts which are produced by the image
reconstruction process (Schulze et al., 2011).
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Extinction artifacts are often termed ‘‘missing value artifacts’’. If the object under study
contains a highly absorbing material, e.g. prosthetic gold restorations, then the signal
recorded in the detector pixels behind that material may be close to zero or actually
zero. No absorption can be computed and severe artifacts are induced as these zero
entries are backprojected into the volume (Prell et al., 2009). Several studies have
produced improved images for patients with metallic devices, using pre-processing
algorithms (Bechara et al., 2012). By increasing the milliampere per second factor
levels, higher-quality images will result because the increased beam energy is not
absorbed entirely by the metallic structures (Vande Berg et al., 2006).
Beam hardening is a major source of artifact in CBCT and is caused by the lower
wavelength rays of the emitted x-ray source that suffer substantial absorption when
passing through the object under study. An x-ray beam is composed of individual
photons with a range of energies. As the beam passes through an object, it becomes
“harder,” which means its average energy increases, because the lower energy photons
are absorbed more rapidly than the higher energy photons. The more dense the latter
and the higher the atomic number of its composition, the larger the share of absorbed
wavelengths (Barrett and Keat, 2004). The result is a series of dark streaks on the final
image. Beam hardening can contribute to grey level non-uniformity in CT images, as it
is not included as a factor in the mathematics of image formation (Schulze et al., 2011).
The presence of metallic bodies within the maxillofacial complex causes beam
hardening and streak artifacts (El-Khoury et al., 2004). Ultimately this leads to a limited
diagnostic field by obscuring anatomical structures especially fine anatomic landmarks.
It reduces the contrast between adjacent objects and can impair the detection of the
areas of interest (Draenert et al., 2007).
In reconstructed images, dental metallic artifacts may be observed as hypodensities
surrounded by hyperdense areas and can be found near any type of metal, including
titanium implants and metallic restorations (Perrella et al., 2010). It has been noted that
beam hardening caused by dental metallic restorations does not influence
measurements performed at various boney sites. However, the presence of such
artifacts can make it more difficult to accurately locate the alveolar crest (Cremonini et
al., 2011). Accurate detection of the alveolar crest was essential to several
80
measurements performed in the current study and may be the reason for the variability
found compared to the anatomic measures. It is well known that beam hardening
artifacts in particular make CBCT imaging unsuitable for dental caries diagnosis (Scarfe
et al., 2010). As such, it may also be an unsuitable technology for identification of other
fine anatomic details.
A partial volume effect is a common artifact in CT imaging and can introduce
imprecision into the digital image. When tissues of widely different absorption occupy
the same voxel, the beam attenuation is proportional to the average value of the
attenuation (Glover and Pelc, 1980). An average is computed for such voxels and is
called volume averaging which can lead to the artifacts. If a voxel lies completely within
an object, it will reflect the true object density. However, if a voxel is at the junction of
two objects of different densities, than it will represent an average between the true
values for both. This voxel can be interpreted as being part of either area, potentially
misrepresenting the data set (Baumgaertel et al., 2009). Volume averaging may reduce
the visibility of a landmark within an image layer and could result in underestimation of
the edges of structures such as cortical bone surfaces (Kumar et al., 2007).
Consideration and reduction of this artifact is necessary when imaging any part of the
body where the anatomy is rapidly changing. Partial volume artifacts can best be
avoided by using a thin acquisition section width and selection of the smallest
acquisition voxel (Scarfe and Farman, 2008).
A ring artifact is a circular artifact that is produced when any detector is out of calibration
leading to a consistently erroneous reading at each angular position. These artifacts can
impair the diagnostic quality of an image, and this is particularly likely when the central
detectors are affected. The result is the creation of a dark smudge at the center of the
image(Barrett and Keat, 2004). These artifacts are quite visible and often when
detected a correction is made and the scan is repeated.
Patient motion can have significant effects on image quality and was the reason for the
development of the current scanners that can acquire images in a shorter period of
time. The acquisition time of current CBCT machines roughly ranges between 6 and 20
seconds. There is still however, sufficient time for the patient to have some minor
81
movement (Ritchie et al., 1992). The smaller the voxel size (i.e. the higher the spatial
resolution), the smaller the movement necessary to demonstrate artifacts on the film
(Schulze et al., 2011). It has been reported that images will suffer a distortion of the
volume data sets when translation movements of the head are greater than 5 mm
rotations are more than 2 degrees (Wagner et al., 2003). Although this is a concern in
the clinical setting, this type of artifact can be ruled out as a contributor to poor image
quality in this study since no live patients were imaged.
Scatter is caused by those photons that are diffracted from their original path after
interacting with matter. The amount of scatter increases with object thickness and field
size and is proportional to the tissue density and atomic number. Scatter reduces
subject contrast by adding background signals that are not representative of the
anatomy thereby reducing image quality. This artifact known to affect the density values
of all tissues, but in particular it can reduce soft tissue contrast (Tofts and Gore, 1980).
Considering the geometry of large area detectors, it is known that the larger the
detector, the higher the probability that scattered photons will be detected. Thus, the
image-degrading effect of scattered radiation will affect CBCT machines more than
classical highly-collimated fan-beam CT (Kalender and Kyriakou, 2007). In conventional
medical grade CT, collimation at the x-ray source restricts the coverage of the beam,
only allowing scatter from a thin axial volume of tissue to reach the detector elements
during section acquisition. In contrast, CBCT expands the coverage of the beam,
allowing x-ray scatter generated from the entire volume of coverage to reach the
detector elements as the image is acquired (Miracle and Mukherji, 2009; Zhang et al.,
2007). Artifact formation by scatter is very similar to beam hardening owing to the fact
that both will reduce the measured attenuation coefficients (Meganck et al., 2009; Zhu
et al., 2009). Scatter radiation has been shown to reduce the accuracy in reconstructed
values, which in turn will degrade accuracy when preforming any associated measures
(Siewerdsen and Jaffray, 2001).
The imaging of any metallic body will show strong beam hardening and scattering effect
artifacts which can lead to unsuitable images for diagnostic purposes (Draenert et al.,
2007). Scatter reduces image quality by degrading the CT number linearity, and
reducing the contrast-to-noise ratio (Ren et al., 2012). Scatter can present as (a) spatial
82
low frequency grey value deformations, known as cupping; (b) streaks, bars, or
shadows, particularly in the vicinity and between highly absorbing regions; and (c)
decreased soft tissue contrasts.
The cupping and shadowing artifacts look similar to those from beam hardening but are
often more severe (Ruhrnschopf and Klingenbeck, 2011). Cupping occurs due to a
decrease in grey levels in the center of an object owing to the increase in transmitted
intensity to the detector from the presence of beam hardening during image acquisition
(Hunter and McDavid, 2012). Upon reconstruction, the attenuation coefficients and the
CT numbers will appear to have been decreased. This results in CT images showing
less dense materials in the center of the object and hence artifacts (Barrett and Keat,
2004).
Finally, image degradation can result from image lag affecting the final diagnostic
quality. Image lag is mainly due to the trapping of charges in the sensitive area of the
imager. It delays the signal reading and causes a portion of the signal from the current
image to appear superimposed on the images taken in later time frames(Mail et al.,
2008). The superimposed images can affect the clarity of fine anatomic structures.
Any one of the aforementioned artifacts alone or in combination can degrade diagnostic
image quality. Attempts should be made to reduce the occurrence of all artifacts
through adjustment of pre-processing parameters. By eliminating these effects, the final
image will be more highly representative of the true anatomic structures.
There seems to be a knowledge transfer gap between the technical understanding of
these artifacts or image limitations and those clinicians reading the films. This lack of
knowledge may introduce diagnostic errors that could be avoided by a better
understanding of the causative factors and the error effects (Barrett and Keat, 2004).
Design features incorporated into modern CT scanners minimize some types of
artifacts, and some can be partially corrected by the scanner software during post-
processing. However, in many instances, careful patient positioning and optimum
selection of scanning parameters are the most important factors in avoiding CT artifacts.
New advancements are continuously being created to reduce image artifacts. Many of
83
them are post-processing algorithms operating on the 3D volume data set (Barrett and
Keat, 2004). Although this may result in considerable reduction of some apparent
artifacts, from a physical point of view post-processing is like putting the cart before the
horse since the error has already been integrated into the volume. In these cases the
final image does not fully reflect the true anatomic structures in study. Consequently,
more modern approaches should be attempting to avoid reconstruction errors through
alterations in image acquisition (Schulze et al., 2011). It has been suggested that until
such time that image quality can be improved with these newer imaging modalities,
traditional films should remain the gold standard in pre-operative diagnostic planning.
7.74 Summary
Any of the aforementioned variables can have a significant effect on image quality and
the clarity of anatomic structures. Despite adjustments to the image acquisition
parameters some inherent noise and artifacts will be present that clinically can affect the
ease by which a clinician can interpret a film. Any of the factors discussed could have
been a reason for the variability found in this investigation in the measures made on the
radiographic images as compared to those of the anatomic specimens. A potential
reduction in image clarity may have made it more difficult to identify the fine anatomic
landmarks with consistency, which would have led to variability in the final associated
measures. Clinicians must appreciate and consider the effects of these variables on
images prior to their use in any pre-surgical planning.
7.8 Access to Radiographic Modalities
The ease of accessibility and handling of dedicated CBCT scanners raises some
important concerns as it has caused a major shift in the user group of highly
sophisticated 3D CT imaging. Most purchasers of CBCT scanners are specialist
dentists and maxillofacial surgeons and not radiologists (De Vos et al., 2009). The
errors and confusion found in the clinical literature on CBCT imaging can be partially
84
attributed to the limited technical knowledge about medical imaging devices of this new
user group (Honey et al., 2007). Currently, in regions where CBCT technology is in
regular use, images are read by clinicians at various levels of training and not
necessarily by radiologists. Most clinicians rarely revise their basic concepts of image
reading and the resultant errors occurring during clinical practice may be caused by
modifications in personal perception acquired overtime. One might speculate that
landmark identification becomes improved with pattern recognition, which is more
applicable to experienced observers. One study reported that a standardized period of
training reduced structure related causes of variability among observers (Delamare et
al., 2010). One could surmise that multiple repeat readings of the same images might
improve accuracy of landmark identification, irrespective of additional training. If
accuracy is however not improved, then the errors in identification will simply be
repeated. Consistency in locating anatomic structures would decrease the variability of
any repeated measures involving such landmarks.
The results of the present study demonstrated that those observers with less
experience reading orthopantomograms and CBCT images did not perform any
differently than the more experienced clinicians. Paired t-test comparisons
demonstrated no statistically significance difference between groups. Similarly, Berco
and others, found the accuracy of linear measurements was uninfluenced by operator
experience (Berco et al., 2009). Contrary to this, one study has reported that the major
influence on reliability of a landmark is inter-observer variation and that this can affect
accuracy outcomes overall (Chen et al., 2004). CBCT technology may provide images
with more diagnostic information, but users have to be aware of their responsibility to
interpret the data thoroughly and appropriately. A complete understanding of the
potential inherent limitations of these new radiographic modalities is essential. It must
be noted that measures performed by radiologists who read such images more regularly
may more closely match those of the anatomic standards when compared to other
clinicians.
85
7.9 Clinical Applications In Pre-Surgical Planning
The choice of the ideal radiographic technique for pre-operative surgical planning can
be quite confusing for the clinician. The introduction of 3D imaging choices combined
with computer planning software is becoming the mainstay of pre-surgical planning,
particularly in the field of implant placement. Resorption of bone, position of the IAN
canal and the location of the maxillary antrum have been reported as being more clearly
depicted on CBCT than on conventional films (Nakagawa et al., 2002). The impact that
CBCT technology has had on maxillofacial imaging, cannot be underestimated. This
does not however imply that CBCT be the first or only choice in imaging modalities in
clinical practice. 3D images of skeletal and dental structures without superimpositions
do provide much more information than a conventional 2D radiograph. The clinician
must decide whether this extra information will facilitate the patient's diagnosis and
treatment to avoid unnecessary radiation exposure for no therapeutic advantage.
Provisional guidelines for the use of CBCT were created by SEDENTEXCT project in
2009. These evidence-based guidelines include referral criteria, quality assurance
guidelines and optimization strategies (Simeonov, 2011).
Imaging modalities, although being an integral component to the surgical planning
phase, as evidenced, all have inherent limitations that can affect image quality and
hence the accuracy of landmark identification and associated linear measures. Despite
the fact that 3D imaging provides more detailed information compared to traditional
films, the accuracy within these images is in not necessarily superior to traditional 2D
imaging techniques. The variability in 3D measures may be due to inaccuracies with the
technology itself or more likely errors in interpretation of the images due to their lack of
clarity, contrast and overall quality. Irrespective of the reasoning, it has been
demonstrated that CBCT technology has limitations that can lead to variability in any
final measures. The American Academy of Oral and Maxillofacial Radiology defends
the position that the success of any surgical treatment is, in part, dependent on
adequate diagnostic information about the bony structures of the oral region, including
accurate linear measurements (Carter et al., 2008). With this potential for inaccuracy
86
and the increase in radiation dose to the patient, it would suggest that the use of
orthopantomograms remain an adequate preoperative adjunct for the surgical planning
of ramus graft harvest or any other surgical procedure.
How these study results and others can be extrapolated to clinical scenarios is
unknown. The variations in structures, imaging parameters and subjects of interest that
arise in clinical scenarios make it difficult to apply results found in in-vitro to everyday
clinical use. Errors found under ideal laboratory conditions should be applied with
caution to in-vivo applications, particularly since all clinical situations have yet to be
investigated.
There is no question that a combination of traditional films and CBCT together provide
the best anatomic detail, but orthopantomograms alone can be invaluable in identifying
the need for further imaging studies (Pawelzik et al., 2002). An optimized examination
algorithm is necessary for each individual case. In more complex scenarios and those
demonstrating concerning findings on the orthopantomogram, the adjunctive use of
CBCT imaging would likely be of merit.
87
Chapter 8
Conclusions
The purpose of this study was to determine the utility of CBCT technology as an
alternative to digital orthopantomograms with respect to pre-surgical planning accuracy.
The conclusions as they relate to the original hypotheses are as follows;
Hypothesis #1
H0 : The thickness of buccal bone does not vary with location in the posterior mandible.
Conclusion: The greatest thickness of bone from the buccal cortex to the lateral aspect
of the IAN canal can be found in the first and second molar area. Ramus graft harvest
design should therefore extend into these areas to maximize harvest quantity. The
maximum thickness of the ramus graft should not exceed 4 mm to avoid injury to the
IAN.
Hypothesis #2
H0: The position of the IAN nerve in a superior-inferior direction does not correlate to a
specific thickness of buccal bone.
Conclusion: The superior-inferior position of the IAN does not correlate with a particular
thickness of buccal bone in the posterior mandible. In the majority of the mandibles the
superior most aspect of the IAN canal was found 10 to 15 mm inferior to the alveolar
crest in the posterior mandible.
88
Hypothesis #3
H0: Measurements made on digital orthopantomograms accurately reflect those made
on anatomic specimens.
Conclusion: The linear measurements made on digital orthopantomograms
demonstrated 15.9 % variation as compared to the measures on the anatomic
specimens.
Hypothesis #4
H0: Measurements made on CBCT images accurately reflect those made on anatomic
specimens.
Conclusion: The linear measurements made on the CBCT images demonstrated 24.9%
variation as compared to the measures on the anatomic specimens.
The results have demonstrated that both digital orthopantomograms and CBCT images
have inherent variability from the anatomic specimens. The use of both of these images
during the pre-surgical planning phase could introduce errors leading to inaccurate
surgical plans.
89
Chapter 9
Future Direction
The use of CBCT imaging coupled with computer imaging software is becoming the
standard in pre-operative surgical planning for dental implant treatment. This
investigation determined that there is variability in measures made with this imaging
modality when compared to those taken from the corresponding anatomic specimens.
A potential lack of diagnostic quality within the images was deemed as a possible
contributor to this variability. Future studies, which control for selected image
acquisition and/ or pre or post-processing parameters would be of merit in an attempt to
determine if improved diagnostic quality will increase linear accuracy. Examination of
the CBCT and digital orthopantomograms after correction for inherent artifacts could be
compared to measures from corresponding anatomic specimens and those of the
original films. Results may demonstrate less variability as a result of improved image
quality and thereby improving diagnostic abilities.
The variability of the measured results may also be related to the clinical experience of
the observers reading the films. An important study would be one designed to compare
the results of linear measures made by both surgically trained clinicians and trained
radiologists. A study design that assesses any potential correlation between years of
experience with diagnostic accuracy of measures would be beneficial.
Finally, it is important that clinically acceptable margins of error in pre-surgical planning
be agreed upon. Using the anatomic averages calculated in this study, ramus harvest
could be preformed on cadaveric mandibles to determine the risk of injury to the inferior
alveolar nerve. The study design could include the application of a variety of margins of
error to determine which one would most reduce surgical risk.
90
Chapter 10
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Appendices
Appendix I
Anatomic
Site
Distance
Measured
Anatomic
Measure
Orthopantomogram
Measure
(Mean ± SD)
Mean
Percent
Difference
CBCT
Measure
(Mean±SD)
Mean
Percent
Difference
Ascending
Ramus
Buccal
cortex to
IAN canal
3.2±1.5
4.1±1.7
21.9
Width 10.4±2.2 14.7±2.5 29.3
Alveolar
crest to IAN
canal
13.7±3.4
13±3
5.1
13.6±4.9
0.7
Height 31.5±4.4 28.1±4.3 10.8 28.4±4.87 10.8
Area of
Second
Molar
Buccal
cortex to
IAN canal
5.3±1.7
5.1±1.9
3.8
Width 13.1±2.5 17.3±2.3 24.2
Alveolar
crest to IAN
canal
13.2±5.3
12.7±3.7
3.7
9.9±3.6
25
Height 22.8±5.1 22.8±5.1 0 20.7±4.4 9.2
117
Area of
First Molar
Buccal
cortex to
IAN canal
5.6±1.6
5.1±1.9
8.9
Width 12.1±2.6 14±2.6 14.2
Alveolar
crest to IAN
canal
11.9±4.8
14.3±4.7
20.1
10.2±3.5
14.2
Height 20.6±6.5 23.1±6.3 10.8 19.5±4.9 5.3
Mean of each measurement at the locations of the ascending ramus,
second molar and first molar and the mean percent difference of the
orthopantomogram measures and CBCT measures from the anatomic
measures for the Oral Surgery observers.
118
Appendix II
Anatomic
Site
Distance
Measured
Anatomic
Measure
Orthopantomogram
Measure
(Mean ± SD)
Mean
Percent
Difference
CBCT
Measure
(Mean±SD)
Mean
Percent
Difference
Ascending
Ramus
Buccal
cortex to
IAN canal
3.2±1.5
4±1.4
20
Width 10.4±2.2 15±2.6 30.1
Alveolar
crest to IAN
canal
13.7±3.4
13.4±2.8
2.2%
13.8±3.9
0.7
Height 31.6±4.4 28.3±4.1 10.4% 28.1±4.0 11.1
Area of
Second
Molar
Buccal
cortex to
IAN canal
5.3±1.7
5.3±1.8
0
Width 13.1±2.5 14.8±2.3 11.4
Alveolar
crest to IAN
canal
13.2±5.3
11.7±3.8
11.3%
10.2±3.2
22.7
119
Height 22.8±5.1 22.7±5.3 0.4% 20.3±4.6 10.9
Area of
First Molar
Buccal
cortex to
IAN canal
5.6±1.6
5.3±1.6
5.4
Width 12.1±2.6 15.1±2.5 19.8
Alveolar
crest to IAN
canal
11.9±4.8
12.4±4.6
4.0%
10.3±3.8
13.4
Height 20.6±6.5 22.3±5.9 7.6% 20.5±5.5 0.4
Mean of each measurement at the locations of the ascending ramus,
second molar and first molar and the mean percent difference of the
orthopantomogram measures and CBCT measures from the anatomic
measures for the Dental Student observers.
120