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. this indicates images that passively move into the reference space, i.e. they have the transform applied but do not contribute to the calculation of the transform.
Target Brain			Probabilistic Atlas image (different subject)
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	1.0 x 1.0 x 3.3 mm axial oblique 256 x 256 x 36 x 9 RAS

Objective / Background

This is a typical example of DTI processing. Goal is to align the DTI image with a structural scan that provides accuracte anatomical reference. The DTI contains acquisition-related distortion and insufficient contrast to discern anatomical detail.

Keywords

MRI, brain, head, intra-subject, DTI, DWI

Input Data

• reference/fixed : T2w axial, 0.4mm resolution in plane, 3mm slices
• moving: Baseline image of acquired DTI volume, corresponds to T2w MRI , 0.9375 x 0.9375 x 1.4 mm voxel size, oblique
• tag: Tensor data of DTI volume, oblique, same orientation as Baseline image. The result Xform will be applied to this volume. The original DWI has 26 directions, the extracted DTI volume has 9 scalars, i.e. 256 x 256 x 36 x 9

Registration Results

Download

• download entire package (Data,Presets,Tutorial, Solution, zip file 33.7 MB) (to be added: tutorial + presets)
• Presets
• Tutorial only
• Image Data only

Discussion: Registration Challenges

• The DTI contains acquisition-related distortions (commonly EPI acquisitions) that can make automated registration difficult.
• the two images often 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.
• there may be widespread and extensive pathology (e.g stroke, tumor) that might affect the registration if its contrast is different in the baseline and structural reference scan

Discussion: Key Strategies

• the two images have identical contrast, hence we could consider "sharper" cost functions, such as NormCorr or MeanSqrd. But because of the strong distortions and lower resolution of the moving image, Mutual Information is recommended as the most robust metric.
• often anatomical labels are available from the reference scan. It would be less work to align the anatomical reference with the DTI, since that would circumvent having to resample the complex tensor data into a new orientation. However the strong distortions are better addressed by registering the other direction, i.e. move the DTI into the anatomical reference space.
• because we seek to assess/quantify regional size change, we must use a rigid (6DOF) scheme, i.e. we must exclude scaling.
• masking is likely necessary to obtain good results.
• in this example the initial alignment of the two scans is very poor. The strongly oblique orientation of the DTI makes an initial manual alignment step necessary.
• these two images are not too far apart initially, so we reduce the default of expected translational misalignment
• 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 scane: 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.