Difference between revisions of "Projects:RegistrationLibrary:RegLib C02"
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==Slicer Registration Use Case Exampe: Intra-subject Brain MR FLAIR to MR T1== | ==Slicer Registration Use Case Exampe: Intra-subject Brain MR FLAIR to MR T1== | ||
===Objective / Background === | ===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 | + | 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. |
===Input Data=== | ===Input Data=== | ||
+ | |||
+ | ===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 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 | ||
+ | |||
+ | ===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) |
Revision as of 15:24, 20 October 2009
Home < Projects:RegistrationLibrary:RegLib C02Contents
Slicer Registration Use Case Exampe: Intra-subject Brain MR FLAIR to MR T1
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.
Input Data
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 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
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)