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SlicerCBM - Computational Biophysics for Medicine in 3D Slicer

Key Investigators

Ben Zwick (The University of Western Australia and Talk2View, Australia)
Andy Huynh (Talk2View, Australia)

Project Description

SlicerCBM (Computational Biophysics for Medicine in 3D Slicer) is an extension for 3D Slicer that provides tools for creating and solving computational models of biophysical systems and processes with a focus on clinical and biomedical applications. Features include segmentation, mesh generation, assignment of material properties (mechanical and electrical) and boundary conditions, and solvers for biomechanical modeling, electrical field modeling (EEG forward problem), and biomechanics-based non-rigid image registration.

Objective

Submit to Extension Index
Update documentation
Fix bugs and improve code
Add automated tests

Approach and Plan

1. Submit to Extension Index

Complete submission requirements (#8)
Fix installation issues

2. Update documentation

Rewrite tutorials on slicercbm.org (#55)

3. Fix bugs and improve code

Fix critical module bugs
Improve Slicer integration (MRML nodes, VTK formats)
Enable stricter linting (#77)

4. Add automated tests

Write unit tests for core modules

Progress and Next Steps

1. Submit to Extension Index

#8: TODO list for submitting extension
#41: Fix gmsh Python package import
#45: Check installation of all Python packages
#73: Fix installation issues
#75: Investigate Windows support

2. Update documentation

#34: Put modules in categories
#39: Use Segmentations module instead of Model Maker
#55: Improve documentation and website

3. Fix bugs and improve code

#48: Fix MVoxMeshGenerator module’s handling of arguments
#61: Keep data in MRML nodes
#62: Fix Fusion/CreateSkullAndScalpSegments module
#64: Fix MTLEDSimulator
#65: Fix FuzzyClassification module
#67: Fix BrainMaterialProperties module
#68: Use consistent units (mm) for length
#70: Fix SkullGenerator module
#72: Use VTK mesh formats
#76: Fix long lines
#77: Enable stricter ruff linting

4. Add automated tests

Write unit tests for core modules
Set up CI/CD pipeline for automated testing

Illustrations

Flowchart of the patient-specific solution of the iEEG forward problem in deforming brain. Brain shift caused by implantation of electrodes is computed using the biomechanical model. The computed displacement field is used to transform the DTI to the postoperative configuration. This warped DTI is then used as the basis for creating the iEEG forward model. fig_flowchart-eeg

Original (actual preoperative) and deformed (predicted postoperative) MR images compared with original CT image and electrode positions. Postoperative CT image and electrode positions (white spheres in CT and red points in the slice planes) are overlaid on the (a,b,c) MRI acquired preoperatively and (d,e,f) MRI registered to postoperative configuration of the brain obtained using biomechanics-based image warping. fig_mri_ct_elec_unwarped_and_warped

Tissue label maps based on (a,b,c) original preoperative and (d,e,f) deformed by insertion of electrodes postoperative image data. Tissue classes are colored as follows: scalp (pink); skull (yellow); GM (gray); WM (white); and CSF (blue). The location of the electrode grid array can be identified by the line of black voxels in the vicinity of the right temporal and parietal lobes. fig_labelmaps

Mean conductivity (1/3 tr(C)) for models constructed using (a,b,c) original preoperative and (d,e,f) deformed by insertion of electrodes postoperative image data. The ECoG electrode grid substrate is denoted by the purple outline. fig_cond_MC

Streamlines of the electric field generated by a current dipole source located in the temporal lobe of an epilepsy patient. Finite element solution using a regular hexahedral grid implemented in MFEM. brain-electric-field

Background and References

Code repository and documentation:

Sample data:

Zwick BF, Safdar S, Bourantas GC, Joldes GR, Hyde DE, Warfield SK, Wittek A, Miller K. Data for patient-specific solution of the electrocorticography forward problem in deforming brain [Data set]. Zenodo; 2022. https://doi.org/10.5281/zenodo.7687631

Publications:

Zwick BF, Safdar S, Bourantas GC, Joldes GR, Hyde DE, Warfield SK, Wittek A, Miller K. Image data and computational grids for computing brain shift and solving the electrocorticography forward problem. Data in Brief. 2023;48:109122. https://doi.org/10.1016/j.dib.2023.109122
Safdar S, Zwick BF, Yu Y, Bourantas GC, Joldes GR, Warfield SK, Hyde DE, Frisken S, Kapur T, Kikinis R, Golby A, Nabavi A, Wittek A, Miller K. SlicerCBM: automatic framework for biomechanical analysis of the brain. Int J CARS. 2023. https://doi.org/10.1007/s11548-023-02881-7
Zwick BF, Bourantas GC, Safdar S, Joldes GR, Hyde DE, Warfield SK, Wittek A, Miller K. Patient-specific solution of the electrocorticography forward problem in deforming brain. NeuroImage. 2022;263:119649. https://doi.org/10.1016/j.neuroimage.2022.119649
Yu Y, Safdar S, Bourantas GC, Zwick BF, Joldes GR, Kapur T, Frisken S, Kikinis R, Nabavi A, Golby A, Wittek A, Miller K. Automatic framework for patient-specific modelling of tumour resection-induced brain shift. Comput Biol Med. 2022;143:105271. https://doi.org/10.1016/j.compbiomed.2022.105271
Safdar S, Zwick BF, Bourantas G, Joldes GR, Warfield SK, Hyde DE, Wittek A, Miller K. Automatic Framework for Patient-Specific Biomechanical Computations of Organ Deformation: An Epilepsy (EEG) Case Study. In: Nielsen PMF, Nash MP, Li X, Miller K, Wittek A, editors. Computational Biomechanics for Medicine. Cham: Springer International Publishing; 2022. p. 75–89. https://doi.org/10.1007/978-3-031-09327-2_5

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