Projects:RegistrationLibrary:RegLib C08

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Slicer Registration Use Case Exampe #8: Intra-subject whole-body PET-CT

					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 a passivley moving image.
whole body CT baseline	whole body PET baseline		whole body CT follow-up	whole body PET follow-up
CT: 512 x 512 x 267 0.97 x 0.97 x 3.27 mm	PET: 128 x 128 x 267 4.7 x 4.7 x 3.3 mm		CT: 512 x 512 x 195 0.98 x 0.98 x 5.0 mm	PET: 168 x 168 x 195 4.1 x 4.1 x 5 mm

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 , PET: 4.7 x 4.7 x 3.3 mm
• moving: CT: 0.98 x 0.98 x 5
• moving: PET: 4.1 x 4.1 x 5 mm

Registration Results

• 

  Original unregistered CT images.

• 

  Automated Affine alignment (RegisterImages) removes global differences. Differences due to posture and breathing remain.

• 

  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.

• 

  After BSpline registration of cropped volumes. The registering of thoracic and abdominal regions only produces good results. We still apply this transform to the uncropped image.

Download

• download full tutorial set (Data+intermediate results+presets+tutorial, zip file ?? MB)
• download example data set (Data only, zip file 135 MB)
• download step-by-step tutorial (PowerPoint 2 MB)
• download registration parameter presets only (zip file with MRML scene files ?? kB)

Link to User Guide: How to Load/Save Registration Parameter Presets

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

Discussion: Key Strategies

• the two images have identical contrast, hence we can consider "sharper" cost functions, such as NormCorr or MeanSqrd
• the contrast differences between the PET and CT far exceed the longitudinal differences in either modality. Hence, if necessary we can take advantage by registering each series separately and then build a concatenated transform.
• the two images are far apart initially, we will need some form of initialization. Manual alignment is a fast and effective approach for this.
• because accuracy is more important than speed here, we increase the sampling rate from the default 2% to 15%. Note however the large image size, which makes comparable sampling % still large compared to other datasets.