Projects:RegistrationLibrary:RegLib C20

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updated for v4.1
Slicer Registration Library Case #20: Intra-subject whole-body PET-CT

Input

fixed image/target		moving image

Versions

For the Slicer 3.6 version of this case see here

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

Download

• Data:
  • RegLib C20 example data set; incl. solutions (zip file 86 MB)
• ScreenCast movies
  • loading the dataset & setting up the display (quicktime movie .mov 6MB)
  • launching registration CT-CT (quicktime movie .mov 9MB)
  • resampling PET (quicktime movie .mov 9MB)

Procedures

• Phase 1: Load & Display

1. drag & drop (or load via File menu): CT_1, CT_2, PET_1, PET_2
   1. in the load Dialog, select Show Options and for each volume, select Center.
   2. click OK
2. go to Volumes module
   1. Active Volume: PET_1
   2. in the Display tab, click on the PET icon for window & level preset
   3. ditto for PET_2
3. place CT_1 in background and PET_1 in foreground an pan between. You should see them aligned.

• Phase 2: Co-register CT

1. open the General Registration (BRAINS) module
   1. Fixed Image Volume: CT_1
   2. Moving Image Volume: CT_2
   3. Output Settings:
      1. Slicer BSpline Transform": create new & rename: "Xf2_CT21"
      2. Slicer Linear Transform: none
      3. Output Image Volume: create new & rename: "CT_2_Xf2"
   4. Regstration Phases: check boxes for Rigid, "Rigid+Scale" , Affine and "BSpline"
   5. Main Parameters:
      1. Number Of Samples: 400,000
      2. B-Spline Grid Size: 11,11,7
   6. Leave all other settings at default
   7. click: Apply; runtime ~ 5 min (MacPro QuadCore 2.4GHz) for faster performance, reduce sample points to 200,000

• Phase 3: Resample PET

1. open the BRAINSResample module (Registration menu)
   1. Image To Warp: PET_2
   2. Reference Image: PET_1
   3. Output Image: create new & rename: "PET_2_Xf2"
   4. Warping Parameters
      1. Warp By Transform: Xf2_CT21 created in Phase 2 above
   5. leave rest at defaults
   6. Apply
2. go to Volumes module
   1. Active Volume: PET_2_Xf2
   2. in the Display tab, click on the PET icon for window & level preset

Registration Results

rigid
BSpline registration of full volumes. 11 x 11 x 7 grid
PET overlay ; BSpline registration of full volumes. 11 x 11 x 7 grid grid overlay showing BSpline deformation

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.