Projects:RegistrationLibrary:RegLib C01

Home < Projects:RegistrationLibrary:RegLib C01

Slicer Registration Use Case Exampe #2: Inter-subject Brain MRI: axial T1 Tumor Growth AssessmentI

		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	T1 SPGR
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 two images have identical contrast, hence we consider "sharper" cost functions, such as NormCorr or MeanSqrd
general practice is to register the follow-up to the baseline. However here the follow-up has slightly higher resolution. Unless there are overriding reasons, always use the highest resolution image as your fixed/reference.
because we seek to assess/quantify regional size change, we must use a rigid (6DOF) scheme, i.e. we must exclude scaling.
if the pathology change is soo large that it might affect the registration, we should mask it out. The simplest way to do this is to build a box ROI from the ROItool and feed it as input to the registration. Remember that masking does not mean that masked areas aren't matched, they just do not contribute to the cost function driving the registration, but move along passively. Next more involved level would be to outline the tumor. If a segmentation is available, you can use that.
these two images are not too far apart initially, so we reduce the default of expected translational misalignment
because accuracy is more important than speed here, we increase the sampling rate from the default 2% to 15%.
we also expect minimal differences in scale & distortion: so we can either set the expected values to 0 or run a rigid registrat
we test the result in areas with good anatomical detail and contrast, far away from the pathology.