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Anatomy of an antsRegistration call

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dorianps edited this page May 5, 2017 · 75 revisions

by Dorian Pustina and Philip Cook
A walkthrough of an example ANTs registration, with options explained in detail:

antsRegistration --dimensionality 3 --float 0 \  
        --output [$thisfolder/pennTemplate_to_${sub}_,$thisfolder/pennTemplate_to_${sub}_Warped.nii.gz] \  
        --interpolation Linear \  
        --winsorize-image-intensities [0.005,0.995] \  
        --use-histogram-matching 0 \  
        --initial-moving-transform [$t1brain,$template,1] \  
        --transform Rigid[0.1] \  
        --metric MI[$t1brain,$template,1,32,Regular,0.25] \  
        --convergence [1000x500x250x100,1e-6,10] \  
        --shrink-factors 8x4x2x1 \  
        --smoothing-sigmas 3x2x1x0vox \  
        --transform Affine[0.1] \  
        --metric MI[$t1brain,$template,1,32,Regular,0.25] \  
        --convergence [1000x500x250x100,1e-6,10] \  
        --shrink-factors 8x4x2x1 \  
        --smoothing-sigmas 3x2x1x0vox \    
        --transform SyN[0.1,3,0] \  
        --metric CC[$t1brain,$template,1,4] \  
        --convergence [100x70x50x20,1e-6,10] \  
        --shrink-factors 8x4x2x1 \  
        --smoothing-sigmas 3x2x1x0vox \  
        -x $brainlesionmask

INTRODUCTION:

Registration (sometimes called "Normalization") brings one image to match another image, such that the same voxels refers roughly to the same structure in both brains. Often the fixed (or target) image is a template, but you can register any two images with each other, there is nothing special in a template from the computation perspective.

Registration algorithms:

To achieve optimal registration, a set of algorithms are used in ANTs: rigid, then affine, then SyN. A "rigid" registration does not deform or scale the brain, it only rotates and moves it (the whole image is considered a rigid object). An "affine" registration allows not only rotation and motion, but also shearing and scaling. This further allows to match brains in size. The "rigid" and "affine" registrations are also called linear registrations, because each point of the image depends on the motion in space of the other points. Linear registrations help only superficially, you can imagine that gyri and sulci still remain misaligned. To achieve proper registration of individual gyri and sulci we need non-linear registrations. There are several non-linear algorithms in ANTs, but we typically use SyN, one of the top performing algorithms (see Klein 2009).

Iterations:

Beside going through registration algorithms, ANTs improves the registration within each algorithm gradually at different resolutions, called levels. The idea is to start with "blurry" images (low resolution, highly smoothed), register those to each other, then go to the next step, with a sharper higher resolution version, and so on. This is the reason why you see 1000x500x250x100 in registration calls, it means there will be 4 levels. The numbers show how many iterations for each level.
The above antsRegistration call runs a rigid, an affine, and a SyN registration. It is the exact call made by the script antsRegistrationSyN.sh. The following is a line by line comment of what each line of the call does. Note that words starting with $ are variables we have defined before. I.e.:
thisfolder=/user/local/mydata/
sub=Subject1
template=Template.nii.gz
t1brain=Subject1.nii.gz

Inline code explained

first two arguments tells the images are 3D, no floating point will be use (double instead)

	antsRegistration --dimensionality 3 --float 0 \

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save transformation matrices with prefix $thisfolder/pennTemplate_to_${sub}_
save registered image as $thisfolder/pennTemplate_to_${sub}_Warped.nii.gz

	--output [$thisfolder/pennTemplate_to_${sub}_,$thisfolder/pennTemplate_to_${sub}_Warped.nii.gz] \

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The interpolation type used when saving the warped image.
Applies just to the output image (from moving), nothing else

	--interpolation Linear \

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deal with outlier voxels. Clips values <5/1000 and >995/1000.
Range can be restricted for bad images, but be careful because it may destroy contrast in image.
This helps because images may have a few voxels with high value that impact badly the registration

	--winsorize-image-intensities [0.005,0.995] \

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Histogram matching is a pre-processing step, transforming the input intensities such that the histogram matches as much as possible the histogram of the target image. It's designed to make registration work better but it is independent of the alignment of the two images.
Set to 0 if registering across modalities (T1 on T2) and 1 for within modalities

	--use-histogram-matching 0 \

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registration works in real coordinates given by the scanner. So images can start quite far from each other (e.g., one in New York, one in London). An initial move is required to bring the images roughly in the same space (close to each other). Options are:
0-match by mid-point (i.e., center voxel of each image
1-match by center of mass
2-match by point of origin (i.e. coordinates 0,0,0)
One can also point to a .mat file obtained with antsAI, but there is no need. The AI solution is implemented in antsCortThicknes.sh and runs an affine with several random changes to check if one "unsual" solution is better. Useful if there are strong orientation issues.
the command tells [fixed,moving,option]

	--initial-moving-transform [$t1brain,$template,1] \

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####################################
START THE FIRST TRANSFORMATION: RIGID
0.1 is the gradientStep, that is, how big the linear shifts will be. Because we run many iterations, we can allow ourselves to run small steps toward the best solution, rather than doing big jumps. If gradientStep is too big registration will finish quickly, but results may not be as optimal. If gradientStep is too small it will take longer to converge (i.e. more iterations). Optimal values are 0.1-0.25.

    --transform Rigid[0.1] \

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mutual information measures how similar the two images look. It uses the histograms of the two images to check the similarity. The histogram will have 32 bins, and values are sampled regularly at 25%, i.e. a voxel is considered every four.
The value of 1 is a weight used if you do multimodal registration (i.e. using, T1, T2, FLAIR). Here is an example
# --metric MI[$t1brain,$template,0.7,32,Regular,0.25] # weight 0.7 on t1
# --metric MI[$t2brain,$T2template,0.3,32,Regular,0.25] # weight 0.3 on t2
# the call format is [fixed, moving, weight, bins, sampling]

    --metric MI[$t1brain,$template,1,32,Regular,0.25] \

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we will run 4 levels (or multi-resolution steps) with a maximum number of iterations of 1000,500,250,100. The threshold (1e-6) tells the algorithm to stop if the improvement in mutual information has not changed more than 1e-6 in the last 10 iterations (convergenceWindowSize=10). To translate it in plain english:
"if the change in MI value for the last 10 iterations is below the 1e-6, stop the iterations and go to next level"
Typically not all iterations are run and the loop finishes because no further improvement is possible. If you want to run all iterations, set the threshold to a large negative number. This will allow iterations to keep going even if mutual information becomes worse.

    --convergence [1000x500x250x100,1e-6,10] \

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the 4 hierarchical steps will have resolutions divided by 8,4,2,1
For example, for an image with 256x256x256 voxels, the levels will work on images of size 32mm, 64mm, 128mm, and 256mm. IMPORTANT! The resolutions use the fixed image as reference. If you register 5mm images on 1mm, --shrink-factors 3x2x1 will register images at 3mm, then 2mm, then 1mm. But if you register 1mm images to 5mm, --shrink-factors 3x2x1 will register images at 15mm, then 10mm, then 5mm. Keep this in mind and try to register low res to high res, not vice versa.

    --shrink-factors 8x4x2x1 \

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Here are the smoothing values for each step: sigma 3,2,1,0.
To convert the sigma in amount of mm you can use roughly a factor of 2.36. The above correspond roughly to 7mm, 5mm, 2mm and 0mm (no smoothing).
Note, smoothing is applied before shrinking the image to lower resolution.

    --smoothing-sigmas 3x2x1x0vox \

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END OF RIGID TRANSFORMATION
###########################################

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###########################################
START THE SECOND TRANSFORMATION: AFFINE
the speed of deformation (gradientStep) at each iteration is again 0.1

    --transform Affine[0.1] \

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all following options are explained above

    --metric MI[$t1brain,$template,1,32,Regular,0.25] \
    --convergence [1000x500x250x100,1e-6,10] \
    --shrink-factors 8x4x2x1 \
    --smoothing-sigmas 3x2x1x0vox \

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END AFFINE TRANSFORMATION
############################################

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############################################
START THE THIRD TRANSFORMATION: SyN
The parameters inside SyN[] are: gradientStep,updateFieldVarianceInVoxelSpace,totalFieldVarianceInVoxelSpace

gradientStep - the SyN metric computes in which direction each point needs to move. This movement can be large (high gradientStep) or small (low gradientStep). Optimal values 0.1-0.25. Because the shift of each point is computed separately, high values here may also increase high frequency deformations (i.e., each point going its own way), but see the other parameters below that mitigate this problem.

After each iteration, a gradient field is computed, which indicates how each point (or voxel) will shift in space. This small deformation (or "updated" gradient field) is combined with previous updates to form a "total" gradient deformation. Normally each point can follow it's own path. This may create non-realistic deformations.
updateFieldVarianceInVoxelSpace - By adding a penalty here, we smooth the deformation computed on the "updated" gradient field, before this is added to previous deformations to form the "total" gradient field. Thus, for each point the deformation of neighboring points is taken into account as well, which avoids too much independent moving of points at each iteration (i.e., a point cannot move 2 voxels away in one direction if all it's neighbors are moving 0.1 voxels away in the other direction). totalFieldVarianceInVoxelSpace - By adding a penalty here, we smooth the deformation computed on the "total" gradient field. The smoothing here, therefore, is applied on all the deformations computed from the beginning (i.e., at this and all previous SyN iterations).
In principle, smoothing of the update field can be viewed as fluid-like registration whereas smoothing of the total field can be viewed as elastic registration.

    --transform SyN[0.1,3,0] \

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here we use cross-correlation (CC) instead of mutual information (MI).
Instead of checking the histograms of images, now we check individual voxels. A radius of 4 indicates that for each voxel we take 4 layers around it (728 voxels) and compute the correlation with the respective 728 on the other image. So, now we care that the surrounding neighborhood of voxels are as similar as possible.
the call is [fixed,moving,weight,radius]

    --metric CC[$t1brain,$template,1,4] \

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the following options are explained above

    --convergence [100x70x50x20,1e-6,10] \
    --shrink-factors 8x4x2x1 \
    --smoothing-sigmas 3x2x1x0vox \

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END OF SyN TRANSFORMATION
#############################################

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mask defined in template (aka target) space. It will restrict all computations only to voxels with value 1 (i.e. brain), and ignore whatever is outside the mask.
why in template space? because you are supposed to move the image in that space and check how well it fits with your target.
this is a problem for patients with lesions: the lesion is drawn on the subject's image.
don't worry, just flip the order, make your subject fixed and move the template on it.
This is the reason why all above calls show template as moving

    -x $brainlesionmask

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** Tips for registration:**

  1. Run a bias correction before antsRegistration (i.e. N4). It helps getting better registration.
  2. Remove the skull before antsRegistration. If you have two brain-only images, you can be sure that surrounding tissues (i.e. the skull) will not take a toll on the registration accuracy.
  3. Never register a lesioned brain with a healthy brain without a proper mask. The algorithm will just pull the remaining parts of the lesioned brain to fill "the gap". Despite initial statements that you can register lesioned brains without the need to mask out the lesion, there is evidence showing that results without lesion masking are sub-optimal. If you really don't have the lesion mask, even a coarse and imprecise drawing of lesions helps (see Andersen 2010).
  4. Don't forget to read the parts of the manual related to registration.
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