Transcript Slide 1
Image Registration
Techniques, Benchmarking, Strategy
Surgical Planning Laboratory
Center for Neurological Imaging
July 2010
Lidwien Veugen
Supervision by Dominik S. Meier, PhD
July 2010
Contents
- Introduction
Image Registration, 3D Slicer
- Theory
Transformations, Similarity Metrics
- Benchmarking
Time/Memory vs Iterations/Samples
- Registration Strategies
- Registration Cases
Brains, PET-CT, EMPIRE10
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Introduction
Image Registration:
- Process of matching multiple image by optimal transformation
3D Slicer:
- Free Open Source Software program
- Huge amount of Registration Modules/Methods
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3D Slicer
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Theory
Transformations
Mapping points from original spatial coordinates
to new spatial coordinates: (u,v,w) = T{(x,y,z)}
Rigid Transform
Affine Transform
Rotation + Translation
Rotation + Translation + Scaling + Shear
(u,v,w) = R*(x,y,z) + t
(u,v,w) = A*(x,y,z) + t
6 DOF
12 DOF
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Theory
Transformations
BSpline
Spline: function defined piecewice by polynomials
Cubic grid of moving control points describes deformation
3 DOF per control point
BrainsDemonWarp
Thirion + Maxwell: Image registration based on optical flow
Boundaries are semi-permeable membranes with effectors/demons
High DOF
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Theory
BRAINSFit
- Rigid, Affine, BSpline
- Mutual Information
- 6/12/higher DOF
Plastimatch
- Pipeline: Rigid/Affine, BSpline(s)
- MutualInfo + MeanSqE
- 6/12/higher DOF
Expert Automated Registration
- Pipelines: Rigid, Affine, BSpline
- MutualInfo + MeanSqE + NormCorr
- 6/12/higher DOF
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Transformation
Theory
Similarity Metrics
Tells to what degree two images are aligned
Based on: intensity, landmarks
Mutual Information
- Measure of the statistical dependence between two random variables:
Information about image A that is shared by B and vice versa
- Maximized if the two images are spatially aligned
- Based on Shannon entropy H: measure of intensity prediction
- Fast measure
H(A) pA alog pA a
MIA,B H B HB | A
a
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Theory
Similarity Metrics
Mean Squared Difference
- Summation of the squared differences between two images
- Minimized if the two images are spatially aligned
- Intra-patient + Intra-modality
2
1 m n
MSD
Ax, y Bx, y
- Time consuming
mn x1 y1
Normalized Cross Correlation
- Based on cross correlation
- Maximized if the two images are spatially aligned
- Intra-patient + Intra-modality
NCC
- Time consuming
m
Ax, y A Bx, y B
x1 y1
m
n
x1 y1
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n
Ax, y A
2
m
n
Bx, y B
x1 y1
2
Theory
Optimization
Optimization algorithm:
Tries to find a global solution to an energy function
- Gradient descent
- Statistical optimization
- Line search algorithm
- One-plus-one evolutionary
- Multiresolution
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Registration Accuracy
Subtraction
Fixed - MovingRegistered
Checkerboard
Alternating squares from
fixed and moving image
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Benchmarking
Effect of the amount of iterations and samples on CPU
time and memory for different modules/methods
Rigid:
Affine:
BSpline:
4 methods
7 methods
2 methods
Default: Samples = 10000, Iterations = 200
Iterations: 11 values, ranging from 25 to 20 000
Samples: 20 values, ranging from 25 to 10 000 000
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Benchmarking
Fast results with:
SPL Dell Linux Cluster of 50 computers
Creates log-file of every job Matlab
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Benchmarking
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Results
Benchmarking
Results
Time vs Iterations
Time vs Samples
- Not much effect
- Increase: Brainsfit, Exp.Autom.
- Decrease: Multiresolution
- Constant: BSpline modules
- Increase: All modules, except:
- Decrease: Exp.Autom. NormCr
- 10 800 seconds (0.003% 13%)
- Rigid < Affine < BSpline
Memory vs Iterations
Memory vs Samples
Not much effect
- Increase: All modules, except:
- Constant: Brainsfit, Multires
- Lowest: 2MB; Highest: 155MB
- Increase: All modules, except:
- Decrease: Exp.Autom. NormCr
- Lowest: Rigid (10-100MB)
- Highest: BSpline (400-1300MB)
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Registration Cases
Slicer Registration Case Library
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Registration Strategies
Choice of Transformation
Modality, Subject, Inter/Intra, Part of body
Choice of Similarity Metric
Inter/Intra, Time/Accuracy
Focus
Time/Accuracy/Memory Sim.metric/iterations/samples
Fixed Image
Resolution/Contrast
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Registration Cases
EMPIRE10
Evaluation of Methods for Pulmonary Image Registration 2010
= Challenge of International Conference on Medical Image Computing
and Computer Assisted Intervention (MICCAI)
20 Pairs of chest CT scans: variety scanners, voxel size, breathing phase
Evaluation: Lung boundaries, Fissures, Landmarks, Singularities
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Registration Cases
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Registration Cases
EMPIRE10 - Registration Pipeline:
1.
2.
3.
4.
5.
Fast Affine Registration
Fast nonrigid Bspline Registration (grid = 7)
Fast nonrigid Bspline Registration (grid = 12)
Fast nonrigid Bspline Registration (grid = 17)
BrainsDemonWarp
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Registration Cases
EMPIRE10 - Quality Registration
Subtraction + MATLAB help in evaluation registration:
Median pixelvalue of absolute subtracted image: the lower the better
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Registration Cases
fMRI alignment to structural scan (T1)
- Fixed: T1 scan (anatomical reference)
- Moving: fMRI scan
- Problem: Low tissue contrast, acquisition related distortions
Registration based on
ventricles only
fMRI
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T1
Registration Cases
Aging Mobility Study 2 year follow-up
- 2 Exams at different times: nonrigid (BSpline)
Exam 1
- Incorrect axis-info
FLAIR
MPRAGE
- Fixed: MPRAGE
T1
- Moving: T2, FLAIR
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Exam 2
MPRAGE
FLAIR
T1
Registration Cases
Inter-subject Normal brain MIDASexample
- Fixed: T1
- Moving: T2, MRA
- Interpatient: non-rigid (BSpline)
Patient 1
T1
T2
MRA
Patient 2
T1
T2
MRA
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Registration Cases
PET-CT Fusion 2
- Intersubject: nonrigid
BSpline, BrainsDemonWarp
- Fixed: CT-scan patient 1
- Moving: CT-scan patient 2
- Problem: Different posture
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Patient 1
Patient 2
CT
PET
CT
PET
Registration Cases
Brain Intersubject PNL-XNAT
- Intersubject: nonrigid (BSpline)
- Problems with (too much) BSpline
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Patient 1
Patient 2
MRI
MRI
Registration Cases
Brain Intersubject OrientationFlx
- Intersubject: nonrigid (BSpline)
- Fixed: T1
- Moving: T2
- Problems with nested transformations
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Patient 1
Patient 2
T1
MRI
T1
T2
T2
Registration Cases
Brain Intersubject Dartmouth
Montreal Neurological Institue:
Colin27 for group analysis in MRI studies
- Fixed: Colin27
- Moving: Patient
- Orientation!
Colin27
Patient
MRI
MRI
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Acknowledgements
Finally, I would like to thank everybody from CNI for the
possibility to do an internship here!
Thanks to my supervisor Dominik S. Meier, PhD
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Questions?
?
July 2010