Publication Details




Adaptive Mesh Refinement for Non-Rigid Registration of Brain MRI


Andriy Fedorov and Nikos Chrisochoides.


Published in Computational Bioimaging and Visualization in 8th World Congress on Computational Mechanics, 2008




The objective of tumor resection during neurosurgery is to remove as much as possible of the tumor tissue with the minimum damage to the neighboring brain structures. This goal is complicated by intra-operative shift of the brain tissue: pre-operative images become inaccurate. Open Magnetic Resonance (MR) scanners facilitate low-quality image acquisition during the surgery. Non-rigid registration (NRR) is the image-processing operation, which aligns salient features of the pre-operative images with intra-operative data, and thus enables surgeons to benefit from the high-resolution information, otherwise unavailable. NRR method presented in~[1] is a part of the image processing protocol under evaluation at Brigham and Women's Hospital (Boston, MA, USA). This method uses FEM biomechanical model of brain deformation for brain shift estimation, and requires tetrahedral model of brain. In our research we aim algorithmic and software development and improvement of mesh generation for this specific NRR approach.




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