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Total Lagrangian Explicit Dynamics Finite Element Method for Brain Registration


Our overall objective is to significantly improve the efficacy and efficiency of image-guided neurosurgery for brain tumors by creating a novel system to improve intraoperative visualization, navigation and monitoring utilizing realistic estimation and prediction of brain deformations, based on a fully non-linear biomechanical model. The system will create an augmented reality visualization of the intraoperative configuration of the patient's brain merged with high resolution preoperative imaging data, including diffusion tensor- and functional magnetic resonance imaging (DTI and fMRI), in order to better localize the tumor and critical healthy tissues. In our previous work, we have explored the possibility of using intraoperative whole brain magnetic resonance imaging to create a target with which to align the preoperative data. Although whole brain intraoperative MRI is a rich source of information, it is time consuming to acquire. MR I physics and intraoperative scanner hardware limitations make it infeasible to achieve rapid whole brain MR imaging during surgery. So, such comprehensive acquisitions are only available at limited and infrequent times, whereas the brain is changing throughout the surgery. An alternative to this infrequent imaging, is to carry out very rapid (frequent) non-volumetric imaging (multi-planar imaging) which provides much sparser information regarding the position of the brain. We propose here to develop and validate an algorithm that enables estimation of the whole brain deformation from such rapidly acquired and sparse imaging data, and gives the neurosurgeon an objective assessment of the nature of the specific patient's intraoperative brain deformation. The aims of this proposal are to 1) develop a very efficient finite element solver using Total Lagrangian formulation and explicit time integration scheme, suited to computing brain deformation in real time; 2) implement the new constitutive model of brain tissue, accounting for brain tissue higher stiffness in compression than in extension in finite element code; and 3) carry out an extensive validation and evaluation of the proposed model in the setting of intraoperative MRI alignment. This research will contribute to public health by developing a system for improved visualization, navigation and targeting for image guided therapy. The development of a realistic nonlinear model of brain deformation during neurosurgery will enable more accurate and precise tumor resection and improved preservation of healthy tissues.


Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.