Projects:RegistrationLibrary:RegLib C12

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v3.6.3 Slicer Registration Library Case #12: Liver Tumor Cryoablation

Input

fixed image/target		intermediate ref. image		moving image

Modules

• Slicer 3.6.3 recommended modules: BrainsFit

Objective / Background

We seek to align a pre-operative MRI with the intra-operative CT to enhance tumor visibility for surgical guidance.

Keywords

MRI, CT, IGT, intra-operative, liver, cryoablation, change detection, non-rigid registration

Input Data

• reference/fixed : pr-op CT, 0.95 x 0.95 x 5 mm voxel size
• moving: intra-op MRI, 0.78 x 0.78 x 2.5 mm axial,

Notes / Overall Strategy

• because the torso is already well registered and does not move across the sequence, a global affine/rigid registration is of no use. Instead we seek to assess the internal displacements caused across the breathing cycle. We therefore choose a low-DOF BSpline model.
• Because the implementation of the BRAINSfit module used here expects a 3D volume, we choose a slightly larger grid and make it isotropic, although the 3rd dimension of the grid is not used. We expect the optimizer in the registration to not move outside the slice plane and simply return 0 deformations there.
• An alternative strategy would be to register each image to its immediate previous timepoint, and then integrating the displacements. This has the advantage that nonrigid registration tends to be better for smaller differences, but it bears the risk of accumulating error and hence a lower robustness.
• A related option to improve precision would be to initialize each registration with the result of the previous one. Unfortunately the BRAINSfit registration modules do not (yet) allow to initialize with anything other than an affine.
• The additional information/constraint, that the displacements defined by the series of registrations should describe a smooth motion that is contiguous and differentiable, has not been exploited in this approach. This could be done a posteriori by applying a temporal filter across the transforms or fitting an appropriate model curve to each node displacement.
• Overall strategy:

1. select one frame within the breathing cycle as reference frame
2. compute non-rigid BSpline registration of all images to the reference frame
3. extract the displacements of indiv. grid nodes and plot over time

Discussion: Registration Challenges

• large differences in FOV
• strong differences in image contrast between MRI & CT
• contrast enhancement and pathology and treatment changes cause additional differences in image content
• we have strongly anisotropic voxel sizes with much less through-plane resolution

Procedures

• Phase I: Pilot to determine optimal registration parameters

1. load reference image and one moving image from the series
2. open Registration : BrainsFit module
   1. Registration Phases:
   2. set "reference" fixed and "moving_??" as moving image
      1. select/check Include BSpline registration phase
   3. Output Settings:
      1. select a new transform "Slicer BSpline Transform", rename to "Xf1_moving_??"
      2. select a new volume "Output Image Volume, rename to "moving_??_Xf1"
   4. Registration Parameters: increase Number Of Samples to 200,000
   5. Registration Parameters: set Number Of Grid Subdivisions to 5,5,5
   6. Leave all other settings at default
   7. click: Apply; (runtime < 10 sec. on MacPro)
   8. adjust grid size until registration is acceptable
   9. you can see the commandline text of the registration performed by opening the Window/Error Log window and clicking on BRAINSfit commandline input

• Phase II: Batch Run

1. open a terminal window
   1. via a TextEditor or prototyping/scripting software (e.g. Matlab), copy and modify the prototype line below, by changing only the moving input image:

 /Applications/Slicer36/Slicer3 --launch  /Applications/Slicer36/lib/Slicer3/Plugins/BRAINSFit --useBSpline --splineGridSize 5,5,5 --outputVolumePixelType short /
 --numberOfSamples 200000 --costMetric MMI --fixedVolume Reference/refLung_001.dcm --movingVolume Moving/Moving_001/Moving_001.dcm /
 --bsplineTransform Xforms/Moving_001_XfBSpl5.tfm --outputVolume MovingResampled/Moving_001_r.nrrd >> Logs/Moving_001_RegLog.txt 2>&1

1. 1. replace "/Applications/Slicer36" with your path of 3DSlicer
   2. create result directories MovingResampled, Logs, Xforms
   3. note that because input image is DICOM, and images are 2D only, each image of the time series must be in its own directory, otherwise Slicer will read them as a volume.
   4. paste all commands into a terminal window, or copy into a shell script and execute.

• Phase III: Evaluate Transform files

1. 1. upon completion, read the transform files with an editor and extract the displacements of interest
   2. The ITK transform files describe displacements at the grid nodes, many of which are outside the region of interest. Because BRAINSfit pads the grid with 1 voxel, the grid returned is actually 8x8x8. We use only the plane (*,*,4) for analysis and discard the y-direction displacements, which as expected are all zero.
   3. for details on the ITK transform format see the FAQ here
   4. To obtain displacements at arbitrary coordinates, interpolate the transform into a deformation field, e.g. using this module (details FAQ here):

/Applications/Slicer3.6.3/lib/Slicer3/Plugins/BSplineToDeformationField

Registration Results

unregistered moving series	moving input after registration (only frames 1-27 shown)	displacements (PDF)

Download

• Data
• download input image data (Input Data, NRRD images, zip file 42 MB)
• download registration parameter presets file (.mrml file 20 kB)
• download guided tutorial (PowerPoint, xx MB)
• download full tutorial set (Input Data, presets, results, tutorial, zip file xx MB)

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

• Results
  • RegLib_C46_RegistrationTransforms.zip : the 200 BSpline registration transforms (ITK transform (text) files, zip file 274 kB)
  • RegLib_C46_MovingResampled.zip : the 200 registered image stack (NRRD image files, zip file 16MB)

Acknowledgments

Thanks to Dr.Stuart Silverman and Dr. Nobuhiko Hata for sharing this case.