Projects:RegistrationLibrary:RegLib C01

Back to ARRA main page
Back to Registration main page
Back to Registration Use-case Inventory

Slicer Registration Use Case Exampe #2: Inter-subject Brain MRI: T1,T2 + MRA, DWI

				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.
T1 SPGR		T2 FLAIR	LABEL-MAP
1mm isotropic 256 x 256 x 146 RAS		1.2mm isotropic 256 x 256 x 116 RAS	1.2mm isotropic 256 x 256 x 116 RAS

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

Input Data

• reference/fixed : T1 SPGR , 1x1x1 mm voxel size, sagittal, RAS orientation.
• moving: T2 FLAIR 1.2x1.2x1.2 mm voxel size, sagittal, RAS orientation.
• tag: segmentation labelmap obtained from FLAIR.
• Content preview: Have a quick look before downloading: Does your data look like this? SPGR Lighbox , FLAIR Lighbox
• download dataset to load into slicer (~17 MB zip archive)

Registration Challenges

• as with all inter-subject registration, there is no perfect solution
• we have very diffferent amounts of initial misalignment.
• the intra-subject sequences are not aligned either
• the different series may have different FOV.
• the different series may have different resolution / voxel sizes.

Key Strategies

• the SPGR is the anatomical reference. It is also higher resolution. Unless there are overriding reasons, always use the highest resolution image as your fixed/reference.
• the defacing of the SPGR image introduces sharp edges that can be detrimental. Best to mask that area. If you have the mask available, use it. But in this case since we already have a skull-stripping mask as part of the labelmap, that is even better. We will load the labelmap and use it as mask in finding the registration
• because the two images are still reasonably similar in contrast, we can choose an intensity ratio as cost function, which is less stable but if successful provides a more precise alignment than mutual information.

Procedures

• download registration parameter presets file (load into slicer and run the registration)
• download/view guided video tutorial
• download power point tutorial
• download step-by step text instructions (text only)

Registration Results

• result transform file (load into slicer and apply to the target volume)
• result screenshots (compare with your results)
• result evaluations (metrics)