Abstract — We present a new algorithm to register 3D preoperative Magnetic Resonance (MR) images to intra-operative MR images of the brain which have undergone brain shift. This algorithm relies on a robust estimation of the deformation from a sparse noisy set of measured displacements. We propose a new framework to compute the displacement field in an iterative process, allowing the solution to gradually move from an approximation formulation (minimizing the sum of a regularization term and a data error term) to an interpolation formulation (least square minimization of the data error term). An outlier rejection step is introduced in this gradual registration process using a weighted least trimmed squares approach, aiming at improving the robustness of the algorithm. We use a patient-specific model discretized with the finite element method (FEM) in order to ensure a realistic mechanical behavior of the brain tissue. To meet the clinical time constraint, we parallelized the slowest step of the algorithm so that we can perform a full 3D image registration in 35 seconds (including the image update time) on a heterogeneous cluster of 15 PCs. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift of up to 14 mm. The results show a good ability to recover large displacements, and a limited decrease of accuracy near the tumor resection cavity. Keywords: Non-rigid registration, intra-operative magnetic resonance imaging, finite element model, brain shift I.

In this thesis, we present a computerized surgical assistant whose core functionality is embodied in a software package we call the 3D Slicer. We describe its system architecture and its novel integration with an interventional Magnetic Resonance (MR) scanner. We discuss the 3D Slicer’s wide range of applications including guiding biopsies and craniotomies in the operating room, offering diagnostic visualization and surgical planning in the clinic, and facilitating research into brain shift and volumetric studies in the lab. The 3D Slicer uniquely integrates several facets of image-guided medicine into a single environment. It provides capabilities for automatic registration, semi-automatic segmentation, surface model generation, 3D visualization, and quantitative analysis of various medical scans. We formed the first system to augment intra-operative imaging performed with an open MR scanner with this full array of pre- operative data. The same analysis