Difference between revisions of "Projects:RegistrationLibrary:RegLib C08"
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=== Modules === | === Modules === | ||
− | *'''Slicer 3.6.1''' recommended modules: [http://www.slicer.org/slicerWiki/index.php/Modules: | + | *'''Slicer 3.6.1''' recommended modules: [http://www.slicer.org/slicerWiki/index.php/Modules:RegisterImages-Documentation-3.6 '''Expert Automated Registration'''] + [http://www.slicer.org/slicerWiki/index.php/Modules:DeformableB-SplineRegistration-Documentation-3.6 '''Fast Nonrigid BSpline'''], [http://www.slicer.org/slicerWiki/index.php/Modules:BRAINSFit '''BrainsFit'''], |
===Objective / Background === | ===Objective / Background === |
Revision as of 17:25, 5 October 2010
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Contents
v3.6.1
Slicer Registration Library Case #08: Intra-subject whole-body PET-CT
Input
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fixed image/target | fixed image/target | moving image | moving image |
Modules
- Slicer 3.6.1 recommended modules: Expert Automated Registration + Fast Nonrigid BSpline, BrainsFit,
Objective / Background
Change assessment.
Keywords
PET-CT, whole-body, change assessment
Input Data
- reference/fixed : baseline CT: 0.97 x 0.97 x 3.27 mm , 512 x 512 x 267; PET: 4.7 x 4.7 x 3.3 mm , 128 x 128 x 267
- moving: CT: 0.98 x 0.98 x 5, 512 x 512 x 195; PET: 4.1 x 4.1 x 5 mm, 168 x 168 x 195
Registration Results
BSpline registration of full volumes achieves better alignment in the abdominal and thoracic region of interest. But the solution is suboptimal because the algorithm is distracted by the strong differences in head position.Rather than trying to address this with more DOF, we calculate a BSpline transform based on croped images including only the main region of interest.
Download
- Data
- download registration BSpline testset only (zip file .nrrrd files 67 MB)
- Presets
- Documentation
Procedure
Discussion: Registration Challenges
- accuracy is the critical criterion here. We need the registration error (residual misalignment) to be smaller than the change we want to measure/detect. Agreement on what constitutes good alignment can therefore vary greatly.
- the two series have different voxel sizes
- because of the large FOV we have strong non-rigid deformations from differences in patient/limb positions etc.
- images are large volumes (>100 MB total)
- image content reaches border of image on two sides
- 2 images pairs have to be aligned, i.e. the calculated transform must be applied to the second (PET) image.
Discussion: Key Strategies
- to calculate the transform, we use the images with the most accurate geometric representation and the smallest expected change, i.e. we align the follow-up CT to the baseline CT and then apply the transforms to the PET image.
- because of the non-rigid differences due to posture and breathing we will need to apply a 2-step registration with an affine alignment followed by a BSpline.
- the strong differences in head position is likely to distract the registration and lead to suboptimal results. Hence we produce a cropped version of the two CT images to calculate the BSpline transform.
- the two images are far apart initially, we will need some form of initialization. We will try an automated alignment first. If this fails, we do a 2-step process with manual initial alignment, followed by automated affine.
- because accuracy is more important than speed here, we increase the iterations and sampling rates. Note however the large image size, which makes comparable sampling % still large compared to other datasets.
- the two images have identical contrast, hence we could consider "sharper" cost functions, such as NormCorr or MeanSqrd. However, since these are not (yet) available for the BSpline registration.