Publication Details




Real-Time Non-Rigid Registration for IGNS: Mesh Generation


Nikos Chrisochoides


Published in Invited Joint Seminar with Waterloo Institute for Health Informatics Research and The Center f, March, 2007




Image Guided Neurosurgery (IGNS) is an important tool for neurosurgical resection which is a common therapeutic intervention in the treatment of cerebral gliomas. Survival rate and quality of life for a patient greatly depend on the precision of the resection, which can be significantly improved by utilizing pre-operative brain scans as an aid in decision making during the procedure. However, during the course of intervention the areas of interest may dislocate due to brain shift/deformation, and thus invalidate existing brain image data. Research underway at Brigham and Women's Hospital (Boston) and the College of William and Mary, attempts to use intra-operative MRI to track brain deformation and align (register) preoperative data accordingly. The challenges of the (non-)rigid registration methods and software for IGNS are: (1) accuracy, (2) validation, (3) speed (4) fault-tolerance, and (5) ease-of-use. In this talk we will present a FEM-based method for (non-)rigid registration in order to motivate our work on real-time mesh generation which we believe can improve the accuracy of the intra-operative registration where it matters most i.e., near by the tumor. Existing parallel mesh generation codes based on the parallelization of well known sequential mesh generation methods. Given that it takes very long time to develop the software infrastructure for sequential industrial strength mesh generation libraries, it is clear that traditional parallelization approaches deliver technology that is outdated. This problem becomes more serious if one considers that improvements of sequential codes in terms of quality, speed, and functionality are open ended. In this talk we present a COTS (commercial of-the-shelf) based approach to real-time (parallel) mesh generation for addressing this serious problem. We will discuss our experience from different parallel meshing methods that are using state-of-the-art sequential software. In addition we will present their extentions to meet our new requirements like conforming the mesh to the boundary between different tissues. This work was possible because of the experience and software we developed from the following NSF grants: Career Award CCR-0049086, ITR ACI-0085969, RI EIA-9972853, NGS EIA-0203974, and ITR-0312980.




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