Projects:RegistrationLibrary:RegLib C10

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Slicer Registration Library Exampe #10: Co-registration of probabilistic tissue atlas for subsequent EM segmentation

			LEGEND this indicates the reference image that is fixed and does not move. All other images are aligned into this space and resolution this indicates the moving image that determines the registration transform. [
Target Brain		Tissue Atlas
0.46 x 0.46 x 3.0 mm axial 512 x 512 x 46 RAS		1.0 x 1.0 x 3.3 mm axial oblique 256 x 256 x 36 RAS

Objective / Background

This is an example of sparse atlas co-registration. Not all atlases have an associated reference image that can be used for registration. Because the atlas represents a map of a particular tissue class probability, its contrast differs significantly from the target image.

Keywords

MRI, brain, head, inter-subject, probabilistic atlas, atlas-based segmentation

Input Data

• reference/fixed : T1w axial, 1mm resolution in plane, 3mm slices
• moving: Probabilistic Tissue atlas,

x 36 x 9

Methods

1. build brain mask for fixed image using Skull Stripping module. Settings: 100 iterations, 20 subdivisions. New Volume: RegLib_C10_MRI_AtlasSegmentation_fixed_mask
2. manually edit brain mask with Editor. required manual fix at frontal and occipital lobe
3. run Register Images , Settings:

• Fixed Image:
• Moving Image:
• Resample Image:
• Load Transform:
• Save Transform: RegLib_C10_MRI_AtlasSegmentation_Xform_Affine_wmsk
• Initialization: Centers of Mass,
• Registration: PipelineAffine
• Expected offset: 10
• Expected Rotation: 0.2
• Expected Scale: 0.1
• Expected Skew: 0.05
• Fixed Image Mask: RegLib_C10_MRI_AtlasSegmentation_fixed_mask
• Affine Max Iteration: 80
• Affine Sampling Ratio: 0.05

1. 1. (alternatively automated Affine Registration: Register Images Multires (Slicer 3.5) also produces good results
2. run '"Deformable B-spline Registration'" module. Settings:

• Grid Size: 5
• Histogram Bins: 50,
• Spatial Samples: 50000,
• initial transform: RegLib_C10_MRI_AtlasSegmentation_Xform_Affine_wmsk

Registration Results

Download

• download entire package (Data,Tutorial, Solution, zip file xx MB)
• Presets
• Tutorial only
• Image Data only

Discussion: Registration Challenges

• Because the atlas represents a map of a particular tissue class probability, its contrast differs significantly from the target image.
• The two images may have strong differences in voxel sizes and voxel anisotropy. If the orientation of the highest resolution is not the same in both images, finding a good match can be difficult.
• The two images represent different anatomies, a non-rigid registration is required

Discussion: Key Strategies

• Because of the strong differences in image contrast, Mutual Information is recommended as the most robust metric.
• masking (skull stripping) is highly recommended to obtain good results.
• because speed is not that critical, we increase the sampling rate from the default 2% to 15%.
• we also expect larger differences in scale & distortion than with regular structural scans: so we significantly (2x-3x) increase the expected values for scale and skew from the defaults.
• a good affine alignment is important before proceeding to non-rigid alignment to further correct for distortions.