Difference between revisions of "Projects:RegistrationLibrary:RegLib C02"
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*reference: T1 SPGR , 1x1x1 mm voxel size, sagittal, RAS orientation | *reference: T1 SPGR , 1x1x1 mm voxel size, sagittal, RAS orientation | ||
*moving: T2 FLAIR 1.2x1.2x1.2 mm voxel size, sagittal, RAS orientation | *moving: T2 FLAIR 1.2x1.2x1.2 mm voxel size, sagittal, RAS orientation | ||
− | *Content preview: [[Media:Lighbox_SPGR.jpg|SPGR Lighbox]] , [[Media:Lighbox_FLAIR.jpg|FLAIR Lighbox]] Does your data look like this? | + | *Content preview: [[Media:Lighbox_SPGR.jpg|SPGR Lighbox]] , [[Media:Lighbox_FLAIR.jpg|FLAIR Lighbox]] Does your data look like this? [[Media:Lighbox_SPGR.jpg|[[Image:Lighbox_SPGR.jpg|50px]] [[Image:Lighbox_SPGR.jpg|50px|link=[[Media:Lighbox_SPGR.jpg]]|SPGR Lighbox preview]] |
*download dataset to load into slicer | *download dataset to load into slicer | ||
Revision as of 17:44, 20 October 2009
Home < Projects:RegistrationLibrary:RegLib C02Contents
Slicer Registration Use Case Exampe: Intra-subject Brain MR FLAIR to MR T1
T1 SPGR | T2 FLAIR | LABEL-MAP | |
1mm isotropic 256 x 256 x 146 |
1.2mm isotropic 256 x 256 x 116 |
1.2mm isotropic 256 x 256 x 116 |
Objective / Background
This scenario occurs in many forms whenever we wish to align all the series from a single MRI exam/session into a common space. Alignment is necessary because the subject likely has moved in between series.
Keywords
MRI, brain, head, intra-subject, FLAIR, T1, defacing, masking, labelmap, segmentation
Media:Example.ogg===Input Data===
- reference: T1 SPGR , 1x1x1 mm voxel size, sagittal, RAS orientation
- moving: T2 FLAIR 1.2x1.2x1.2 mm voxel size, sagittal, RAS orientation
- Content preview: SPGR Lighbox , FLAIR Lighbox Does your data look like this? [[Media:Lighbox_SPGR.jpg|
- download dataset to load into slicer
Registration Challenges
- we expect the amount of misalignment to be small
- we know the underlying structure/anatomy did not change, hence whatever residual misalignment remains is of technical origin.
- the different series may have different FOV. The additional image data may distract the algorithm and require masking
- the different series may have very different resolution and anisotropic voxel sizes
- hi-resolution datasets may have defacing applied to one or both sets, and the defacing-masks may not be available
- the different series may have different contrast.
- individual series may contain motion or other artifacts
Procedure
- step-by step text instruction
- recommended parameter settings
- guided video tutorial
- power point tutorial
Registration Results
- registration parameter presets file (load into slicer and run the registration)
- result transform file (load into slicer and apply to the target volume)
- result screenshots (compare with your results)
- result evaluations (metrics)