Tissue Dependent Registration
Key Investigators
- Petter Risholm (PhD Student, BWH, University of Oslo)
- William Wells (Professor, BWH)
Objective
Most registration methods treat tissue parameters as uniform over the image domain. For instance, when brain tissue is modeled as an elastic material, identical stiffness parameters are used for all tissue types (CSF, GM, WM). We let different tissue types take on varying stiffness parameters, so we can thereby optimize the deformation as well as the stiffness parameters at the same time. Our approach models the tissue with a linear elastic Finite Element (FE) model and solve for the linear elastic tissue parameters as well as the deformation through a Markov Chain Monte Carlo process.
However, to accurately model the tissue, we require a tissue dependent tetrahedral mesh. Assuming we have a segmentation of the brain tissue into CSF, GM and WM, we need a mesh where the tetrahedra conform to the different tissue types (the edges of a tetrahedra do not cross from one tissue type to the other but are either fully within a tissue type or on the boundary between to tissue types).
Approach, Plan
Find/Develop mesher that generates elements conforming to a labelmap.
Utilize command line tools to generate visualizations in Slicer of the registration results.
