Medical Image ProcessingR3

8/9/2019 Medical Image ProcessingR3

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Medical Image Processing

H&Y compilant for PD Diagnosis

and Prognosis using EPI and FAimages

Author:Roxana Oana TEODORESCU

Supervisor:Vladimir ± Ioan CRETU

Daniel RACOCEANU



Summary

Introductiono Motivation. Purposeo Work Overview

Challengeso Medical challengeso Technical challenges

Approach proposed in PDFibAtl@so PDFibAtl@s overviewo Preprocessingo Automatic Volume extraction

Registration

Fusiono Fiber tracking

Evaluation Test batches Results Conclusion & Future Work



Introduction

Diagnosis based on cognitive evaluation URPDS; H&Y [1]

Adding measurable coefficients for diagnosis Why

o

Diagnosis after 80-90 % loss of dopamine [1][2]

o Evaluation based only on cognitiveimmeasurable tests

Howo Image analysis and processing

Whereo At the image level where the disease acts



Approach in PDFibAtl@so PDFibAtl@s overviewo Preprocessingo Automatic Volume extraction

Registration Fusion

o Fiber tracking Evaluation Test batches Results Conclusion & Future Work



Introduction

Evaluate image quality [Rep1] Technical image characteristics [Rep1] Disease specific evaluation parameters

[Rep1]o FA ± fractional anisotropyo

ADC- apparent diffusion coefficiento Dopamine ± main neurostrasmitters

Brain anatomy elements [5] Substantia Nigra Putamen

Image specific evaluationo DTI specific elements

EPI FA




Registration Fusion




Approach in PDFibAtl@s



Preprocessing

DICOM data extractiono Identify the EPI and FA images using

the header fileo K-Means clustering on

Brain tissue Skull ± bone tissue Brain tissue catches the phantom

effecto

Eliminate the phantom effecto Eliminate the skullo Align slices for the 3D volume




Registration Fusion




Preprocessing

Automatic detection of geometrical elementso Determine the brain center of mass in the 3D

image ± voxel levelo Determine the midline that delimits the two

hemispheres Extract the brain outline Determine the highest inflexion point ±

start point for the midline axis Determine the center of gravity on the 2D

image ± second point of the axis Construct the axis based on the two points

o Compute the brain volume




Registration Fusion




Automatic volume extraction

Determine the slice of interesto Based on the region needed, get the relative of

position of the VOI with respect to the center Midbrain limitation

o Initialization point : center of masso

Limit the growth in 2D to the specific hemisphereo Limit the growth in 3D to the relative volume of the

specific anatomical element (8-10 mm : 2 slices)o Construct the mask of the volume

Putamen limitationso Initialization point: based on intensity segmentation

on 3 classes: corpus callosum, globus palladi andputamen

o Pass through the CC and GP to reach the Putamencluster area

o Initialize the shape to a triangle formato Make the growth on 2Do Extend to 3D until reaching the AC/PC slide ± the

one that contains the center of mass of the brain




Registration Fusion




Automatic detection of the midbrain area for a

patient case and a control case

ControlPatient

Midbrain on controlMidbrain on patient



Registration

Definitiono Changing one image (moving image) in order to

fit aonther one ( model image) Rigid registration [10]

o Changing only the position of the pixelso Intensity values stay the sameo

Proportionality on the shape stays the same Geometry-based registration [10]o Checkpoints are determined geometrically

Our approacho Determine automatically the checkpoints on the

EPI B0 image and FA

Center of mass of the brain ± for translation Midline axis orientation ± flip horizontal or

vertical Angle of the Midline axis and the image axis ±

for rotationo Rigid body transformation by matrix application

on the 2D images of masked volume of theputamen image




Registration Fusion




Registration Our approach

o Determine automatically the checkpoints on the EPI B0 imageand FA Midline axis orientation ± flip horizontal or vertical

Horizontal flip :sign(yinflexion, FA - yCM,FA )!=sign( yinflexion, EPI ± yCM, EPI )

Vertical flip:sign(yinflexion, FA - yCM,FA )!=sign( yinflexion, EPI ± yCM, EPI )

Center of mass of the brain ± for translation (dx,dy,dz)= abs (CMFA(x,y,z) ±CMEPI(x,y,z))

Angle of the Midline axis and the image axis for rotation( x,y)

o Rigid body transformation by matrix application on the 2Dimages of masked volume of the putamen image

Where is the difference of the angle between the midlinedetermined on the FA and EPI and the image coordinateaxes




Registr ation Fusion




Fusion elements

Definition

o On the image processing area: morphing two images together o General approach: putting together information form different source

Source of informationo FA image

Anisotropy Dopamine flow on anatomical structures

o EPI Tensor ditections of the fibers Diffusion information ± angularity of the fibers

Information put together o From the FA definition and segmentation of the Putamen areao From the EPI

Midbrain volume Tensor information for fiber growth

Fusion methodo Destination of information: EPI imageo Extraction of Putamen on FAo Make mask on the segmented VOIo Register FA and EPIo Apply mask on the EPI

Our approacho Uses FA specificity for putamen extraction

Better than high resolution images (T1/T2) Anisotropy ± based intensities

o Uses EPIs tensor information with higher accuracy for the fiber growtho Maintains the original image information on both images, transfering only

anatomical / geometrical knowledge




Registration Fusion




Fiber Tracking ± classicalalgorithm

Definitiono Based on angulation information attached to each voxel, the

neural flow can be detected as a fiber o Each diffusion direction determines for a voxel an anisotropy

level that indicates the dopamine flow in that specific pointo The neural flow on the specific direction can pas through ± fiber

in that directiono The fibers represent the axons of the neurons ± WM ± and a

bundle is a regular collection of fibers going on the samedirection

Algorithm [3] [4]o Take each image and based on the matrix of tensors compute all

the possible fibers that grow twards the next slide

o Dopamine flow on anatomical structures Upgrade

Start from the determined volume of interest ± midbrain Grow the fibers only on AP direction Validate only the fibers that reach the putamen volumes

determined Validation of fibers

o Anisotropy values > 0.1o Angulation < 60




Registration Fusion




Evaluation points

Whole brain evaluationo Anisotropy levels

Algorithms evaluationo Preprocessing step

Midline detection

Skull eliminationo VOI detection

Manual detection Algorithm detected Difefrence between volumes

o Fiber evaluation Faster time Smaller data considered New parameters introduced

Validation chriteria Neurologist validation of the detected volumes Statistical Tests on the database




Registration Fusion




Testing procedures Testing steps

o Direction of diffusion

Evaluation of direction by analysis of the green channel (AP direction) of the FA image

Correlation between anisotropy level and intra-patient H&Y classificationo Statistical elements

Statistical relevance on the patients and on the controls Significant difference

between the patients and the controls on the patients having a different stage of the disease

Image labeling and changes

o Preprocessing step Midline detection ± evaluated by Dr. Chan Skull elimination

Mathematical Difference between initial image and the one withoutskull

o VOI evaluation Manual volume ( detected by specialist) ± automatic volume Difference between volumes

o Fiber evaluation

Time in different software systems on the same stack and image Fiber volme (FV) and density (FD) on one volume consideration and twovolumes where we take into consideration the number of fibers ( FNr ),the volumes of the bran and the extracted volumes of interest, the voxelvalues (width/height/depth), fiber length (Flength)




Registration Fusion




Nr H&Yvalue

Age Male/ all

Patients Control Patients Control

1 2.312 64.5 59.37 11/16 6/16

2 2.375 63.31 60.93 9/16 9/16

3 2.375 64.06 58.5 8/16 7/16

4 2.467 62.75 61.5 9/16 8/16

Testing batches

Variate one of the demographic elements,maintaining the others

Randomly take out 5 contol cases and 5patients in each batch testo T1 ± male/all differenceo T2 ± small H&Y differenceo T3 ± same H&Y as T2, big age differenceo T4 ± H&Y higher than the other tests




Registration Fusion




Green channel analysis [6]

Nr. LeftIndependent

Sample T-Test

[p %]

RightIndependent

Sample T-Test

[p %]

LeftCorrelateBivariate

[%]

RightCorrelateBivariate

[%]

LeftANOVA

[Sig]

RightANOVA

[Sig]

1 24.4 74.0 13 8 0.872 0.937

2 12.2 69.3 7 8 0.906 1

3 75.5 65.3 3 6 0.937 1

4 83.6 71.4 7 7 0.937 0.906

Automatically extract the midbrain area on the FA image

Clolor separation on R,G,B channels of the volumes extractedo Color code on FA images shows the diffusion directionality

Red Left-Right Green Anterior- Posterior Blue Down ± Up

o Strationigral tracts grow on AP direction Anisotropy at SN level can make a better BOI selection

Histogram of G channel elements Normalize & Eliminate the noise Correlate the normalised histogram without nise with the H&Y

scores ± variation of anisotropy according to the disease level




Registration Fusion




Fiber study [9]

TestNr.

One Way ANOVA MANOVA

FV FD FD

Left Right Left Right Left Right

1 0.00 0.00 0.00 0.00 0.105 0.515

2 0.00 0.00 0.00 0.00 0.638 0.067

3 0.00 0.00 0.00 0.00 0.138 0.404

4 0.00 0.00 0.00 0.00 0.329 0.404

Total 0.00 0.00 0.00 0.00 0.149 0.629

For the global testing on 80% of the database p=0.05

on the group homogenity in the H&Y assigned caseson the left side

ANOVA test on the database (N=35 subjects from 42)the correlation significance is 83%

Testing the correlation with the Pearson¶s coefficientonly T3 is significant on the left side ± the test is

sensitive to demographic changes




Registration Fusion




PDFibAtl@s graphical andnumerical results



Conclusion & Future work



Approach in PDFibAtl@so PDFibAtl@s overviewo Preprocessingo Automatic Volume

extraction Registration Fusion




References



Publications & Research stages



Acknowledgements



Thank you!

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