Intra-operative adaptive FEM-based registration accommodating tissue resection

Intra-operative imaging during neurosurgical procedures facilitates aggressive resections and potentially an increased surgical success rate compared to the traditional approach of relying purely on pre-operative data. However, acquisition of functional images like fMRI and DTI still have to be performed pre-operatively which necessitates registration to map them to the intra-operative image space. We present an elastic FEM-based registration algorithm which is tailored to register pre-operative to intra-operative images where a superficial tumor has been resected. To restrict matching of the cortical brain surface of the pre-operative image with the resected cavity in the intra-operative image, we define a weight function based on the "concavity" of the deformation field. These weights are applied to the load vector which effectively restricts the unwanted image forces around the resected area from matching the brain surface in the pre-operative image with the surface of the resected cavity. Another novelty of the proposed method is an adaptive multi-level FEM grid. After convergence of the algorithm on one level, the FEM grid is subdivided to add more degrees of freedom to the deformation around areas with a bad match. We present results from applying the algorithm on both 2D synthetic and medical image data and can show that the adaptivity of the grid both improves registration results and registration speed while the inclusion of the weighting function improves the results in the presence of resected tissue.