Projects:RegistrationLibrary:RegLib C19

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Slicer Registration Library Exampe #19: Multi-contrast group analysis: intra- and inter-subject registration of multi-contrast MRI

this is the main reference image. All images are aligned into this space this is the 1st intra-subject moving target, to be aligned with the main reference directly this is the 2nd intra-subject moving target, to be aligned with the main reference directly lleft this is both the fixed target for the 2nd subject and the moving image for inter-subject registration this is a moving image to be aligned indirectly to the main ref his is a moving image to be aligned indirectly to the main ref LEGEND

lleft this indicates the reference image that is fixed and does not move. All other images are aligned into this space and resolution
lleft this indicates the moving image that determines the registration transform.

lleft reference T1, subject 1 lleft moving T2, subject 1 lleft moving MRA, subject 1 lleftlleft reference T1, subject 2 lleft moving T2, subject 2 lleft moving MRA, subject 2
1mm isotropic
176 x 256 x 176
PA
1mm isotropic
192 x 256 x 128
PA
0.51 x 0.51 x 0.8 mm
448 x 448 x 128
PA
1mm isotropic
176 x 256 x 176
PA
1mm isotropic
192 x 256 x 128
PA
0.51 x 0.51 x 0.8 mm
448 x 448 x 128
PA


Objective / Background

This is an example of inter-subject registration via surface matching. The structures of interest are a small subset of the entire image, hence registration is not driven by image intensities but rather two model surfaces derived from the labelmaps.

Keywords

multi-stage registration, MRI, brain, head, multi-contrast, inter-subject, group analysis, MRA, T2

Input Data

  • Button red fixed white.jpgreference/fixed : T1w coronal, 1mm isotropic. Called A1_gray
  • Button red fixed white.jpgreference/fixed : labelmap , aligned with above. Called A1_label
  • Button blue tag white.jpg moving: T1w coronal, 0.9 inplane, 1.5mm coronal slices. Called A0_gray
  • Button green moving white.jpg moving: labelmap , aligned with above. Called A0_label

Methods

  1. Visualize & browse A0 data: determine label range of thalamic nuclei labels in A0_label: 500-526
  2. Visualize & browse A1 data: determine label range of thalamus lables in A1_label: 10 and 49
  3. Build label mask of thalamus for A0: Editor module
    1. Create Labelmap From”: A0_labels

Registration Results

unregistered
registered

Download

Link to User Guide: How to Load/Save Registration Parameter Presets

Discussion: Registration Challenges

  • Because the structures of interest are a very small subset of the image without distinct grayscale contrast
  • the two atlases represent different anatomies and hence some residual misalignment is inevitable
  • the two labelmaps have different resolutions and different smoothness of structure outlines. Some need filtering to remove spurious surface details that would distract the registration algorithm

Discussion: Key Strategies

  • Because the structures of interest are a very small subset of the image without distinct grayscale contrast, we co-register surfaces rather than intensity volumes


Acknowledgments

  • dataset provided by Ron Kikinis, M.D. and Florin Talos, M.D.