Projects:RegistrationLibrary:RegLib C20

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

v3.6.3 Slicer Registration Library Case #20: Intra-subject whole-body PET-CT

Input

fixed image/target		moving image

Modules

• Slicer 3.6.3 recommended modules: BrainsFit,

Objective / Background

Change assessment.

Keywords

PET-CT, whole-body, change assessment

Input Data

• reference/fixed : baseline CT: 0.98 x 0.98 x 5 mm , 512 x 512 x 149; PET: 4.1 x 4.1 x 5 mm , 168 x 168 x 149
• moving: CT: 0.98 x 0.98 x 5 mm , 512 x 512 x 149; PET: 4.1 x 4.1 x 5 mm , 168 x 168 x 149

Registration Results

rigid
affine alignment
BSpline registration of full volumes. 9 x 9 x 5 grid
PET overlay ; BSpline registration of full volumes. 9 x 9 x 5 grid
BSpline registration of full volumes. 11 x 11 x 7 grid
PET overlay ; BSpline registration of full volumes. 11 x 11 x 7 grid

Download

• Data
  • RegLib C20 example data set; incl Presets and solutions (zip file 116 MB)
  • Registration Presets (.mrml files 16kB)
  • Link to User Guide: How to Load/Save Registration Parameter Presets

Procedures

• Phase 1: rigid alignment

1. Go to the BRAINSfit module
   1. select Presets "Xf1_Rigid" or "Xf2_Affine" or set the parameters as given below:
   2. fixed image: "CT_1", moving image: "CT_2"
   3. Initialize with previous transform: select "Off"
   4. Initialize Transform Mode: check box for use MomentsAlign
   5. check Include Rigid registration Phase box.
   6. Output: under Slicer Linear Transform, select new and rename to "Xf1_Rigid" or similar
   7. Registration Parameters: set "Number of Samples" to 200,000 at least
   8. leave rest at defaults
   9. Click Apply
2. return to the Data module and drag the CT_2 inside/outside the different registration transforms to compare the alignment

• Phase 2: affine alignment

1. Go to the BRAINSfit module
   1. same as Phase 1 above, except:
   2. Initialize with previous transform: select "Xf1_Rigid" from phase 1 above
   3. Initialize Transform Mode: check box for Off
   4. Output: under Slicer Linear Transform, select new and rename to "Xf2_Affine" or similar

• Phase 3: BSpline alignment

1. 1. same as Phase 2 above, except:
   2. Initialize with previous transform: select "Xf2_Affine" from phase 2 above
   3. Output: under Slicer BSpline Transform, select new and rename to "Xf3_BSpline" or similar
   4. Output Image Volume: select new and rename to "CT_2_Xf3" or similar
   5. Registration Parameters: set "Number of Samples" to 250,000 at least
   6. Number of Grid Subdivisions: 9,9,5 or 11,11,7
   7. Click Apply
2. return to the Data module and drag the CT_2 inside/outside the different registration transforms to compare the alignment
3. to obtain a resampled volume: move CT_2 inside Xform of choice and then right-click on the volume and select Harden Transforms. Save MRI under new name.

• Phase 4: Resample PET

1. Go to the ResampleScalarVectorDWIVolume module
   1. Input Volume: PET_2 ; Reference Volume: PET_1 ; Output Volume: create new, rename to "PET_2_Xf3"
   2. Transform Node: select "Xf3_BSpline" created in phase 3 above
   3. Transform Order: check box for "output-to-input"
   4. Click Apply

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.
• 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)
• 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.