Difference between revisions of "Projects:RegistrationLibrary:RegLib C01"

Revision as of 18:12, 22 November 2009

Home < Projects:RegistrationLibrary:RegLib C01

		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.
T1 SPGR	T2 FLAIR
0.9375 x 0.9375 x 1.4 mm 256 x 256 x 112 RAS	0.9375 x 0.9375 x 1.2 mm 256 x 256 x 130 RAS

This is a classic case of change assessment. We want to know if the tumor changed since last exam.

MRI, brain, head, intra-subject, T1, tumor growth, meningioma, change assessment

reference/fixed : T1 SPGR , 0.9375 x 0.9375 x 1.4 mm voxel size, axial, RAS orientation.
moving: T1 SPGR , 0.9375 x 0.9375 x 1.2 mm voxel size, sagittal, RAS orientation.
Content preview: Have a quick look before downloading: Does your data look like this? Media:RegUC2_lightbox.png
download dataset to load into slicer (~17 MB zip archive)

accuracy is the critical criterion here. We need the registration error (residual misalignment) to be smaller than the change we want to measure.
we have slightly different voxel sizes
if the pathology change is substantial it might affect the registration.
the different series may have different FOV.
the different series may have different resolution / voxel sizes.

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.

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 ===Registration Challenges===
-*precision is important
+*accuracy is the critical criterion here. We need the registration error (residual misalignment) to be smaller than the change we want to measure.
 *we have slightly different voxel sizes
-*the intra-subject sequences are not aligned either
+*if the pathology change is substantial it might affect the registration.
 *the different series may have different FOV.
 *the different series may have different resolution / voxel sizes.