Difference between revisions of "Projects:RegistrationLibrary:RegLib C12"
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=== Input === | === Input === | ||
{| style="color:#bbbbbb; " cellpadding="10" cellspacing="0" border="0" | {| style="color:#bbbbbb; " cellpadding="10" cellspacing="0" border="0" | ||
− | |[[Image: | + | |[[Image:RegLib_C12_Thumb1.png|150px|lleft|this is the intra-op CT reference image. All images are aligned into this space]] |
|[[Image:RegArrow_NonRigid.png|100px|lleft]] | |[[Image:RegArrow_NonRigid.png|100px|lleft]] | ||
− | |[[Image: | + | |[[Image:RegLib_C12_Thumb2.png|150px|lleft|this is an intermediate pre-op CT, used as reference to match the MRI]] |
|[[Image:RegArrow_NonRigid.png|100px|lleft]] | |[[Image:RegArrow_NonRigid.png|100px|lleft]] | ||
− | |[[Image: | + | |[[Image:RegLib_C12_Thumb3.png|150px|lleft|this is the pre-op MRI we seek to align with the intra-op CT]] |
|- | |- | ||
|fixed image/target | |fixed image/target | ||
Line 24: | Line 24: | ||
===Objective / Background === | ===Objective / Background === | ||
− | We seek to align a pre-operative MRI with the intra-operative CT | + | We seek to align a pre-operative MRI with the intra-operative CT for surgical guidance. |
=== Keywords === | === Keywords === | ||
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=== Notes / Overall Strategy === | === Notes / Overall Strategy === | ||
− | * | + | *the intra-op CT is acquired with a clipped FOV (to minimize acquisition time & exposure). This causes difficulty for intensity-based automated registration. We therefore use an intermediate pre-op CT with full FOV to bridge to the MRI |
− | + | *masking is required to focus the registration algorithm on the structure of interest | |
− | |||
− | * | ||
− | |||
*Overall strategy: | *Overall strategy: | ||
− | :# | + | :#obtain (manual) a coarse segmentation of the liver in both MRI and CT. Dilate by a few pixels to include the organ boundary |
− | :# | + | :#perform a manual initial alignment of MR to CT. Use this alignment as starting point for the automated registration |
− | + | :#run an affine registration with above masks and intial alignment | |
+ | #run a non-rigid BSpline registration with above affine alignment as starting point | ||
+ | |||
=== Discussion: Registration Challenges === | === Discussion: Registration Challenges === | ||
*large differences in FOV | *large differences in FOV | ||
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*contrast enhancement and pathology and treatment changes cause additional differences in image content | *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 | *we have strongly anisotropic voxel sizes with much less through-plane resolution | ||
− | |||
=== Procedures === | === Procedures === | ||
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=== Registration Results=== | === Registration Results=== | ||
{| style="color:#bbbbbb; " cellpadding="10" cellspacing="0" border="0" | {| style="color:#bbbbbb; " cellpadding="10" cellspacing="0" border="0" | ||
− | |[[Image: | + | |[[Image:RegLib_C12_unregistered.gif|300px|lleft|unregistered MRI & CT]] |
− | |[[Image: | + | |[[Image:RegLib_C12_masks.png|300px|lleft|registration masks]] |
− | |[[Image: | + | |[[Image:RegLib_C12_manualInit.gif|300px|lleft|manual initial alignment of MRI & CT]] |
+ | |[[Image:RegLib_C12_affine.gif|300px|lleft|affine registered MRI & CT]] | ||
+ | |[[Image:RegLib_C12_Nonrigid.gif|300px|lleft|nonrigid registered MRI & CT]] | ||
|- | |- | ||
− | |unregistered | + | |unregistered MRI & CT]] |
− | | | + | |registration masks]] |
− | | | + | |manual initial alignment of MRI & CT]] |
+ | |affine registered MRI & CT]] | ||
+ | |nonrigid registered MRI & CT]] | ||
|} | |} | ||
Revision as of 18:50, 18 August 2011
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Contents
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 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
- the intra-op CT is acquired with a clipped FOV (to minimize acquisition time & exposure). This causes difficulty for intensity-based automated registration. We therefore use an intermediate pre-op CT with full FOV to bridge to the MRI
- masking is required to focus the registration algorithm on the structure of interest
- Overall strategy:
- obtain (manual) a coarse segmentation of the liver in both MRI and CT. Dilate by a few pixels to include the organ boundary
- perform a manual initial alignment of MR to CT. Use this alignment as starting point for the automated registration
- run an affine registration with above masks and intial alignment
- run a non-rigid BSpline registration with above affine alignment as starting point
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
- load reference image and one moving image from the series
- open Registration : BrainsFit module
- Registration Phases:
- set "reference" fixed and "moving_??" as moving image
- select/check Include BSpline registration phase
- Output Settings:
- select a new transform "Slicer BSpline Transform", rename to "Xf1_moving_??"
- select a new volume "Output Image Volume, rename to "moving_??_Xf1"
- Registration Parameters: increase Number Of Samples to 200,000
- Registration Parameters: set Number Of Grid Subdivisions to 5,5,5
- Leave all other settings at default
- click: Apply; (runtime < 10 sec. on MacPro)
- adjust grid size until registration is acceptable
- 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
- open a terminal window
- 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
- replace "/Applications/Slicer36" with your path of 3DSlicer
- create result directories MovingResampled, Logs, Xforms
- 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.
- paste all commands into a terminal window, or copy into a shell script and execute.
- Phase III: Evaluate Transform files
- upon completion, read the transform files with an editor and extract the displacements of interest
- 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.
- for details on the ITK transform format see the FAQ here
- 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
registration masks | manual initial alignment of MRI & CT | affine registered MRI & CT | nonrigid registered MRI & CT | |
unregistered MRI & CT]] | registration masks]] | manual initial alignment of MRI & CT]] | affine registered MRI & CT]] | nonrigid registered MRI & CT]] |
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
Acknowledgments
Thanks to Dr.Stuart Silverman and Dr. Nobuhiko Hata for sharing this case.