http://na-mic.org/wiki/index.php/projects:registrationdocumentation 1 multimodal registration clinic...
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1Multimodal Registration Clinic
“All Things Registered”
I. Theory & Tool Overview
II. Live Demo of Registration in 3D Slicer
III. Open Discussion: what’s on your wishlist?
Dominik S. Meier, Ph.D.
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2Multimodal Registration Clinic
“All Things Registered”
I. Background: Registration Theory
II. Image Registration Tools in 3DSlicer (v.3.5)
III. User-support , Training & Documentation
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3
Part I : Registration Theory
Background
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4Registration Concept
• Image registration seeks to bring two or more images into anatomical alignment.
• In mathematical terms: Image registration transforms multiple images into one coordinate system. This is necessary to compare or integrate/fuse the data obtained from different measurements.
• Purpose– change detection (small regional change, subtraction imaging)
– atlas building (normalize for individual anatomical idiosyncrasies)
– distortion or motion correction (different protocols or sensors)
– protocol matching (e.g. sagittal into axial)
– group analysis (anatomical reference space)
– Navigation, patient to reference, surgical planning
– atlas-based morphometry (estimates from atlases)
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5Quality Assessment
speed
robustnessprecision
Cross-sectional:group analysis
•Large numbers of images processed in a fully automated fashion
•Limited review and editing
•Processing speed less relevant than reliable performance across all images in the study
speed
robustness
precision
speed
robustness
precision
IGT:
•Supervised, so robustness can be supplemented with user interaction
•Speed and precision are critical
•Precision/error estimates also critical
Longitudinal:Change Assessment
•Smaller study size compared to cross-sectional
•Some review and editing
•Precision determines detectable change, is the key criterion
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6Registration ConceptImage Registration has 4 main components
– spatial transform: a model equation that describes how the two images should be aligned.
– similarity metric: a criterion that defines how well the images are aligned, i.e. what constitutes a “good match” (cost function).
– optimizer: an iterative exploring of the realm of possible solutions, looking to find the best one (search algorithm).
– interpolator: an algorithm to apply the transform and build the newly aligned images (resampling).
It is important to know what these 4 are and what they do to achieve the best possible result. We will briefly discuss each in turn.
metricoptimizer
interpolator
transform
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7Transform
f(x,y,z)
•analytical model•deformation/vector field•invertable if linear and isomorphic
e.g. translation + scale :x’ = x + 10y’ = y - 3z’ = 1.05 z + 5
z
x
y
z
x
y
metric
optimizer
interpolator
transform
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8
translation
rotation
scaling
shearing affine transform 12 DOF
3 x
3 x
3 x
3 x
similarity transform
9 DOF
rigid transform 6 DOF
Linear Transform: DOF
shift transform 3 DOF
+
+
+
metric
optimizer
interpolator
transform
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9Non-linear Transform: DOF
rigid
affine
81375≈ 50 Million
distances and angles change proportionallyMotion guided by single equation.
many image control points move independently, i.e. no single equation
type/name DOF shape description caveat
intact
globally distorted
careful with volumetry
non-rigid locally distorted
•3-pt Bspline grid:•5-pt Bspline grid:•full image:
rigid body motion, distances and angles preserved. Motion guided by single equation.
distances and angles change dis-proportionally
will not match global scale distortions
DOF matching required: careful not to use excessive DOF and thus normalize/remove the differennces you want to measure.
12
6
metric
optimizer
interpolator
transform
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10Coordinate Systems: the hidden Xform
Each image has a basic (linear) transform that connects the digital image grid to the physical world. This is usually part of the image header.
RAS : right - anterior - superior
R L
L
S
I
A P
vox2rasz
x
yImageto ImageTransform
z
x
y
vox2ras
R L
L
S
I
A P
metric
optimizer
interpolator
transform
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11Registration Glossary
• DOF• fixed & moving image• linear vs. nonlinear• rigid vs. non-rigid• forward vs. backward/inverse mapping• multi-modality• registration parameters• affine, similarity, B-spline,warping• pivot point, image origin, coordinate system• pixel space vs. raster space• voxel size & anisotropy• intensity vs. feature-based registration
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12Metric•defines how well the two images align•qualitative: you; visual assessment•quantitative:
•sum of intensity similarity•sum of topographical features
– Intensity Difference
– Intensity Ratio
– Cross-correlation
– Mutual Information
metrictransform optimizer
interpolator
speed
robustness
precision
– same subject, same contrast
– same subject, different contrast
– same subject, different modalities
– different subject, same contrast
– different subject, different contrast
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13Similarity Metricmetric
optimizer
interpolator
transform
speed
robustnessprecision
same subject, same contrast
same subject, different contrast
same subject, different modalities
different subject, same contrast
different subject, different contrast
Difference Ratio Correlation Mutual-Info
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14Interpolation
•Applies the transform to the image and generates a new volume.
•back from physical space into the image grid:
•the newly calculated position of an image voxel will not fall exactly onto a grid-point. Therefore its value is determined by the intensities of neighboring pixels. This process is known as interpolation.
T
metrictransform optimizer
interpolator
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15Interpolation
•nearest neighbor: picks value of the voxel nearest the point coordinates
• + fast
• - coarse
• + MUST use for label-maps
•linear: picks a weighted mean of neighboring voxel intensities
• + stable, default
• - introduces blurring
•cubic, sinc: fits a non-linear model to estimate the intensity
• + sharper, less blurring
• - slower
• - may introduce spurious outliers near edges (e.g. negative intensities)
original nearest neighbor
linear cubic
Example of a T1-weighted brain MRI, rotated by 6 degrees. Showing magnified sagittal view of cerebellum and midbrain.
nearest neighbor: note the false contouring around the ponslinear: note the blurringcubic: less blurring than linear
metric
optimizer
transform
interpolator
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16Optimizer
•manual: you
•automated: iterates between metric and transform
•exhaustive search
•gradient-based search
•annealing/stochastic schemes
metrictransform optimizer
interpolator
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17Optimization
rotation x
rotation y
similarity
optimumlocal maxima (suboptimal solutions)
The algorithm moves/wiggles one of the images around trying to find the best match, according to the similarity metric. It does so in increments from its current position, evaluating if the new position is better than the old one. A “local maximum” is a position around which all nearby changes appear worse, but farther away there is a better solution available. Optimization algorithms often get “stuck” in such positions.Depending on the difference in contrast between the two images, the similarity metric employed, and the amount of initial misalignment, this is more or less likely to happen to an automated registration.
metricoptimizer
transform
interpolator
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Part I : further topics
• the main components: transform, similarity metric, optimization, interpolation
• coordinate systems: physical vs. image space, RAS vs. LPI etc.
• relevant image meta-data: coordinate system, axis orientation, image/CS origin, voxel size
• overview of scenarios and their different challenges: image pairings, DOF, multi-modal, intra/inter-subject etc.
• how to evaluate a match: tools & concepts
• common mistakes to avoid: inappropriate DOF, overly flat similarity metric, CS inconsistencies, FOV discrepancies, wrong interpolation, insufficient search (sample points, multi-scale, DOF scale-space)
• Troubleshooting guide: insufficient match - what next? Parameter modification, DOF change, initial alignment assist, fiducial help, ROI masking (e.g. skull stripping)
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Part II : 3DSlicer Registration
Tools
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20Overview of Registration Toolsin 3D Slicer
Registration Main Modules
Registration Auxilary Modules
Driver:
•manual
•intensity
•surfaces
•fiducials
•segmentation
DOF:
6
7
9
12
27 ~ 103
Support for:•fiducials•ROI definition•mask building & editing•resampling•visualization/evaluation
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21Registration Modules:
Manual - Interactive
• ideal for initial alignment
• immediate feedback in 3D
• fail-safe if automated registration fails or is too slow
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22
speed & precision
mask
starting point
robustness
contrast & contentDOF
presets
Registration Modules:
Intensity Affine
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23Registration Modules NEW:
Multi-resolution Affine
•Method of choice for robustness•Supports masking
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presets
DOFimage contrast
constraints
speed & precision
Registration Modules:
Non-rigid BSpline
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1. build 2 fiducial lists with 3 or more points each2. click: Apply3. supports translation to similarity (3-9 DOF)
very fast (< 1 sec)
Example: inter-subject knee registration
Registration Modules:
Fiducial Registration
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• Input: 2 surface models• click: Apply• supports rigid to affine (6-12 DOF)• very fast (~ 1 sec)
Registration Modules:
Surface Registration
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• Aligns brain image along midsagittal plane and places anterior-posterior commissures on a horizontal line
• Input: 2 fiducial pairs defining
• anterior & posterior commissure
• midsagittal plane
Registration Modules:
AC-PC alignment
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• Non-rigid Cortical surface
alignment based on
WM/GM segmentation
• warps based on attributes
derived at gyrus crown,
sulcal root and ventricle
corners.
Registration Modules:
HAMMER Cortical Surface Matching
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• Non-rigid registration based on optical flow principle
considered very robust
Registration Modules:
Demons - Warping
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• define and edit in 3D• use for masking registration
Auxilary Tools:
ROI Module
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• define and edit in 2D or 3D• use for masking registration
where masking is not (yet) explicitly supported
• increase speed and robustness
Auxilary Tools:
Extract Subvolume
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32Auxilary Tools:
Visualization
Checkerboard Filter• to evaluate registration quality (particularly
for areas with high contrast/edges)• Subtraction Images to evaluate overall
alignment
Subtraction• Subtraction Images to evaluate regional
changes and alignment
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Editor• create and manipulate binary
label maps from grayscale images
• fix labelmaps returned by other modules (skull stripping, Otsu’s etc.)
Auxilary Tools:
Editor
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34Auxilary Tools:
Resampling
• Apply a transform to a scalar or vector (e.g.
DTI) volume
• select tailored interpolation scheme (nearest
neighbor, linear, sinc, b-spline)
• correctly reorients vector data
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• define and edit in 3D• organize in fiducial lists• use ordered lists of
fiducial pairs for registration
Auxilary Tools: Fiducials
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36
Part III : Registration
User Support Training
Documentation
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37Registration Case LibraryBrain
Other
A growing collection of example registration problems, complete with image data, tutorial, solution, discussion and a parameter preset file that can be loaded into 3DSlicer.
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38Registration Case Library
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39Registration Case Library:Tutorials
Guided/narrated Video Tutorials
Step-by-step Powerpoint/PDF Tutorials
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40Call For Datasets"if you have a registration problem that is not yet covered in our library, send us your case: we will post it along with our best registration solution/strategy. If you agree to the posting of the anonymized image data, you get a free registration, the user community gets a new example case. Everybody wins.”
What We Will Do•seek the best possible registration obtainable with the most recent version of 3DSlicer
•post the anonymized image as a new case in our Slicer Registration Case Library
•post the exact workflow used to obtain the shown solution registration will be posted alongside the data as a guided step-by-step tutorial
•the parameters for successful registration will also be posted as a loadable custom "Registration Preset" file that you can load directly into Slicer and apply on your data
•if you can provide us with fiducial pairs or other criteria that define a good registration, we will use them in optimization efforts.
•the registration objective & background, main challenges and strategy recommendations will be posted
•an acknowledgment of your lab as the data source is posted, if desired with a link to your institution and/or related research papers
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41Registration Case Library:Tutorials
Slicer Training Compendium:Tutorials for all skill levels
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42Acknowledgments
National Alliance for Medical ImageComputingNIH U54EB005149
Neuroimage Analysis Center NIH P41RR013218
Surgical Planning Laboratory, Brigham and Women’s Hospital