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SlicerCBM "Computational Biophysics for Medicine in 3D Slicer"

Key Investigators

Presenter location: In-person

Project Description

SlicerCBM 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 grid generation, assignment of material properties and boundary conditions, and solvers for biomechanical modeling and biomechanics-based non-rigid image registration.


  1. Package SlicerCBM modules as an installable 3D Slicer extension.

Approach and Plan

  1. Complete the requirements for a new 3D Slicer extension (https://github.com/SlicerCBM/SlicerCBM/issues/8)

  2. Add the SlicerCBM extension to the Slicer Extensions Catalog.

Progress and Next Steps

  1. Completed the SlicerFreeSurferCommands project, which provides modules that are used in the SlicerCBM workflow.

  2. Discussed opportunities for integrating SlicerCBM with NousNav for image-guided surgery simulations (both as a research tool and for potential clinical applications).

  3. Solved the EEG forward problem on the SPL brain atlas mesh created using the SlicerAtlasEditor from the Open Meshed Anatomy project.

  4. We will continue working on SlicerCBM next week at PerkLab (Queen’s University).


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: