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Pseudo-‐CT generation using MRI images from undersampled, single-‐acquisition UTE-‐mDixon
Kuan-Hao (Dylan) Su, Jung-Wen (Gloria) Kuo, Lingzhi Hu, Christian Stehning, Michael Helle, Gisele C. Pereira, David W. Jordan , Pengjigng Qian, Cheryl L. Thompson, Karin A. Herrmann, Raymond F. Muzic, Jr., Melanie Traughber, Bryan J. Traughber
Presenter: Kuan-Hao (Dylan) Su, PhD
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Introduction
Photon Attenuation and Absorption info.
CT
MR
pseudo-CT
T1, T2, PD, UTE…UTE-mDixon
Fuzzy c-means clustering(FCM)
pseudo-CT
PET/MR
MR Linac
MR UTE-‐mDixon acquisition
MR UTE reconstruction
Point spread function evaluation
Are point spread functions optimized?
FID spatial scaling optimization
Fuzzy c-‐means
Tissue assignment
Resolution matching
yes
noMR image
optim
izatio
nclu
stering
R2* and Dixon reconstruction
Fig. 1
pseudo-‐CT
Flow chartpseudo-‐CT generation
MR
CT
5
MR Sign
al
TE
Soft
Bone
Air
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Materials and Methods -‐-‐-‐ multi-‐echo UTE-‐mDixon sequence
FID (TE = 0.1 ms) echo1 (TE = 1.5 ms) echo2 (TE = 2.8 ms)
MR UTE-‐mDixon acquisition
MR UTE reconstruction
Point spread function evaluation
Are point spread functions optimized?
FID spatial scaling optimization
Fuzzy c-‐means
Tissue assignment
Resolution matching
yes
noMR image
optim
izatio
nclu
stering
R2* and Dixon reconstruction
Fig. 1
pseudo-‐CT
Flow chartpseudo-‐CT generation
MR
CT
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Materials and Methods -‐-‐-‐ UTE trajectory delay correctionNMR rod
without correction with correction (+1.18 μs)
Eddy-‐current induced gradient delay
MR UTE-‐mDixon acquisition
MR UTE reconstruction
Point spread function evaluation
Are point spread functions optimized?
FID spatial scaling optimization
Fuzzy c-‐means
Tissue assignment
Resolution matching
yes
noMR image
optim
izatio
nclu
stering
R2* and Dixon reconstruction
Fig. 1
pseudo-‐CT
Flow chartpseudo-‐CT generation
MR
CT
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UTE-mDixon sequence
FID (TE = 0.1 ms) echo1 (TE = 1.5 ms) echo2 (TE = 2.8 ms)
Dixon-‐fat Dixon-‐waterR2*
Dixon separation:R2* (1/T2*) estimation:I(p) = I0 exp[ -‐ R2* x TE(p) ]
(190 seconds)
10Su et al. , Medical Physics, 42(8), 2015
MR UTE-‐mDixon acquisition
MR UTE reconstruction
Point spread function evaluation
Are point spread functions optimized?
FID spatial scaling optimization
Fuzzy c-‐means
Tissue assignment
Resolution matching
yes
noMR image
optim
izatio
nclu
stering
R2* and Dixon reconstruction
Fig. 1
pseudo-‐CT
Flow chartpseudo-‐CT generation
MR
CT
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Clustering
Membership function ( five clusters)
class1
FCM
Dixon-‐fat Dixon-‐waterR2*
MR features
R2*
Dixon-‐fat
Dixon-‐water
Mapclass1class2
class4
class3
class5
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Pseudo-‐CT composition
× CTair + × CTfat + × CTfluid
+ × CTbrain + × CTbone
= pseudo-‐CT
(-‐1000 HU) (-‐98 HU) (-‐13 HU)
(40 HU) (-‐1524 HU)
Schneider et al., Phys Med Biol, vol. 45, 2000
Membership function ( five clusters)
MR UTE-‐mDixon acquisition
MR UTE reconstruction
Point spread function evaluation
Are point spread functions optimized?
FID spatial scaling optimization
Fuzzy c-‐means
Tissue assignment
Resolution matching
yes
noMR image
optim
izatio
nclu
stering
R2* and Dixon reconstruction
Fig. 1
pseudo-‐CT
Flow chartpseudo-‐CT generation
MR
CT
axial coronal sagittal
Steel beads (size 1.00 mm)
GEMINI CT 600 mm recon. FOVslice thickness: 5 mmStandard, filter B Pitch = 0.813
ACR CT phantom (GAMMEX 464)
Resolution matching -‐-‐-‐ measure CT resolution
Resolution matching
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MR
Resolution mismatched
1.5 x 1.5 x 1.5 mm3 1.7 x 1.7 x 6.3 mm3
Spatial resolution (FWHM):
Matched
Low-‐dose CTfor Attenuation correction
MR UTE-‐mDixon acquisition CT acquisition
MR UTE reconstruction
Point spread function evaluation
Are point spread functions optimized?
FID spatial scaling optimization
Fuzzy c-‐means
Tissue assignment
Resolution matching
Low-‐dose CT
Rigid-‐body transformation
yes
noMR image
optim
izatio
nclu
stering
R2* and Dixon reconstruction
Fig. 1
pseudo-‐CT
Flow chartpseudo-‐CT generation
MR
CT
(n = 9)
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0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
air brain fat fluid bone
Feature Intensity
Tissue Type
R2* (ms)
Dixon-‐fat (a.u.)
Dixon-‐water (a.u.)
Results -‐-‐-‐ views in feature domain(N = 9)
Class 1 Class 2 Class 3
FCM Membership functions
FCM clustering and tissue assignment
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Class 4 Class 5
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
air brain fat fluid bone
Feature Intensity
Tissue Type
R2* (ms)
Dixon-‐fat (a.u.)
Dixon-‐water (a.u.)
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Pseudo-‐CT generationMembership function (five clusters)
× CTair+ × CTfat + × CTfluid
+ × CTbrain + × CTcbone
= pseudo-‐CT
(-‐1000 HU) (-‐98 HU) (-‐13 HU)
(40 HU) (-‐1524 HU)
Schneider et al., Phys Med Biol, vol. 45, 2000
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CTLow-‐dose
Pseu
do-‐CT
Pseu
do-‐CT
(resolutio
n matching)
1200
0
-‐1000
HU
axial coronal sagittal
Results -‐-‐-‐ CT vs. pseudo-‐CT
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Results --- computation cost
l pseudo-CT generation timeØ FCM clustering:
~ 60 seconds
Ø Tissue assignment and CT generation< 1 second
Computer:Windows 7 64-‐bit16 GB RAMIntel® i7 3.4 GHz
COMKAT: http://comkat.case.edu
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ConclusionsThe UTE-‐mDixon, FCM approach is an accurate, clinically practical method for pseudo-‐CT generation and can be used to improve the accuracy of MR-‐ACand MR-‐RTP.
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PET bias (%) Frontal Occipital
Uniform mask -‐10.1 % -‐15.2 %
FCM 0.0 % -‐9.4 %
SVM 1.4 % 9.9 %
ANN -‐0.7 % -‐5.6 %
MR
VOI analysis -‐-‐ Bias of PET (%) Normalized by brain Act.
PET
Frontal
Occipital
Frontal
Occipital
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Histogram analysis -‐-‐ Bias of PET (%)
010000200003000040000500006000070000
-‐50 -‐40 -‐30 -‐20 -‐10 0 10 20 30 40 50
Uniform vs CT
010000200003000040000500006000070000
-‐50 -‐40 -‐30 -‐20 -‐10 0 10 20 30 40 50
FCM vs CT
010000200003000040000500006000070000
-‐50 -‐40 -‐30 -‐20 -‐10 0 10 20 30 40 50
SVM vs CT
010000200003000040000500006000070000
-‐50 -‐40 -‐30 -‐20 -‐10 0 10 20 30 40 50
ANN vs CT
Bias (%)
Bias (%)
Bias (%)
Bias (%)
SD = 3.1 %
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VOI analysis -‐-‐ Bias of PET (%)
PET bias (%) mean SD range
Uniform mask -‐9% 3% -‐13.5% ~ -‐4.7%FCM -‐1% 3% -‐3.4% ~ +4.1%SVM 2% 3% -‐0.7% ~ +7.7%ANN 1% 1% -‐0.7% ~ +3.0%
Berker’s paper(JNM, 2012)range:-‐4.8% ~ +7.6%
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Results
fuzzy c–means(FCM)
C5
ANNw/o spatial
features
ANNwith spatial
features
Bias (HU) -‐22 ± 29 6 ± 57 28 ± 21
|error|(HU) 130 ± 16 138 ± 41 113 ± 18
R 0.78 ± 0.05 0.83 ± 0.06 0.87 ± 0.04
Figure of Merit
-‐-‐-‐mean ± SD (n = 9)
* The ANN results were generated in the leave-one-out fashion.
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