Summer ITKv4 ClinicalGroupMeetingNotes: Difference between revisions

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== 3D Real-Time Physics-Based Non-Rigid Registration for Image Guided Neurosurgery (PBMNRRegistration) ==
The following is a rough pipeline of the method with proposed classes.
Inputs: a segmentation mask, a mesh
Outputs: deformation field, transformed image(s)
1) FeaturePointSelection3dFilter:  No dependencies.  Plan to start implementation with this filter.
2) BlockMatching3Dfilter: Similar to Penn FEM registration classes?  Perhaps only need to implement a new metric? Plan to use the GPU infrastructure, but also have a non GPU version.
3) PBMSolver: PETSc dependence
4) ImageWarp: Already in ITK
Gaps:
* Mesh generation. Tetmesh reader / converter?  Use Biomesh3D and bridge to ITK?
* Self-updating transform object
* PETSc & MPI within an ITK filter?
What support is needed?
* CMake integration w/ PETSc and MPI.  Build / distribution issues.
* Further discussion and collaboration with the FEM, registration, and GPU groups.
Data:
* ?
== Lesion Sizing Toolkit ==
== Lesion Sizing Toolkit ==
Inputs: dicom
Inputs: DICOM
Outputs: measures of volumes, segmentations
Outputs: Lesion volume measurements and segmentations.


* Already in ITK as an external module.  Contract is to reimplement with ITKv4 and distribute.
* Already in ITK as an external module.  Contract is to reimplement with ITKv4 and distribute.
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Data: 60 datasets. Chest CT scans 1mm resolution. 200mb each. MIDAS?  Store as DICOM?  Automatically download using CTest.
Data: 60 datasets. Chest CT scans 1mm resolution. 200mb each. MIDAS?  Store as DICOM?  Automatically download using CTest.
== 3D Real-Time Physics-Based Non-Rigid Registration for Image Guided Neurosurgery (PBMNRRegistration) ==
The following is a rough pipeline of the method with proposed classes.
(segmentation -> mesh gen -> registration -> petsc -> generate image (deformation field))
Inputs: a mask, a mesh
1) FeaturePointSelection3dFilter(mask, fixed image): start with this filter
2) BlockMatching3Dfilter(fixed, moving, metrics,feature points) -- GPU, similar to penn fem registration classes, maybe metric plug in
3) PBMSolver(displacement vector, mesh) --petsc dependence
4) ImageWarp--already in ITK
Outputs: deformation field, transformed image(s)
Gaps:
* Mesh generation, tetmesh reader / converter: Biomesh3D and bridge to ITK?
* ITK Mesh
* Self-updating transform object
* Integration of solver
* MPI with ITK?
What support is needed?
* CMake integration w/ petsc (uses MPI).  Build / distribution
* Interface w/ FEM guys, registration & GPU guys




Revision as of 16:46, 27 June 2011

3D Real-Time Physics-Based Non-Rigid Registration for Image Guided Neurosurgery (PBMNRRegistration)

The following is a rough pipeline of the method with proposed classes.

Inputs: a segmentation mask, a mesh Outputs: deformation field, transformed image(s)

1) FeaturePointSelection3dFilter: No dependencies. Plan to start implementation with this filter. 2) BlockMatching3Dfilter: Similar to Penn FEM registration classes? Perhaps only need to implement a new metric? Plan to use the GPU infrastructure, but also have a non GPU version. 3) PBMSolver: PETSc dependence 4) ImageWarp: Already in ITK


Gaps:

  • Mesh generation. Tetmesh reader / converter? Use Biomesh3D and bridge to ITK?
  • Self-updating transform object
  • PETSc & MPI within an ITK filter?

What support is needed?

  • CMake integration w/ PETSc and MPI. Build / distribution issues.
  • Further discussion and collaboration with the FEM, registration, and GPU groups.

Data:

  • ?

Lesion Sizing Toolkit

Inputs: DICOM Outputs: Lesion volume measurements and segmentations.

  • Already in ITK as an external module. Contract is to reimplement with ITKv4 and distribute.
  • Using spatial objects as inputs and outputs
  • Should some of these algorithms be migrated into ITK proper? (e.g. enhanced canny edge detection)

What support it needed?

  • Does ITK want a tighter integration of these classes, and in this same form? Does this cover more general concepts useful to other groups.

Gaps:

  • Representing measures as a concept in ITK

Data: 60 datasets. Chest CT scans 1mm resolution. 200mb each. MIDAS? Store as DICOM? Automatically download using CTest.


ITK Algorithms for Analyzing Time-Varying Shape with Application to Longitudinal Heart Modeling

Data:

  • 25 longitudinal cardiac DE-MRI (1.25mm in-plane, 2.5mm thick) with segmentations of the left atrium. 2-4 datapoints each (pre ablation, 3mo, 6mo, 1 year)
  • Need IRB to release image data

Gaps:

  • Multivariate stats.
  • Bridge to R for complex statistical analysis without going to file system.
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