Summer ITKv4 ClinicalGroupMeetingNotes

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)
Contributions: New filters, classes. Application?

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

What support is needed?

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

Gaps:

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

Data:

• ?

Lesion Sizing Toolkit

Inputs: DICOM Outputs: Lesion volume measurements and segmentations. Contributions: Functioning toolkit. Possibly new ITK filters classes. Data.

• Already in ITK as an external module. Contract is to port to use ITKv4 and distribute.
• Using spatial objects as inputs and outputs

What support is 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. e.g. Enhanced canny edge detection

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

Inputs: Segmentations Outputs: Point sets Contributions: New ITK module (particle system), filters, classes.

• Existing code base mostly ITK
• Port significant portions to ITKv4
• New ParticleSystem module. New ITK filter process objects.

What support is needed?

• Logistics of integration and distribution, including data.

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. Only implement what is needed for within ITK algorithms.