Accurate and fast deformable medical image registration for brain tumor resection using image-guided neurosurgery

We present a Parallel Adaptive Physics-Based Non-Rigid Registration framework for aligning preoperative to intra-operative brain Magnetic Resonance Images (MRI) of patients who have undergone a tumor resection. This framework extends our earlier work on the Physics-Based methods by using an adaptive, multi-material, parallel Finite Element (FE) biomechanical model to describe the physical deformations of the brain. Our registration technology incorporates fast image-to-mesh convertors for remeshing the brain model in real-time eliminating the poor quality elements; various linear solvers to accurately estimate the volumetric deformations; efficient block-matching techniques to compensate for the missing/unrealistic matches induced by the tumor resection. Our evaluation is based on six clinical volume MRI data sets including: (i) isotropic and anisotropic image spacings, and (ii) partial and complete tumor resections. We compare our framework with four methods: a Rigid and BSpline deformable registration implemented on 3D Slicer v4.4.0, a Physics-Based non-rigid registration available on ITK v4.7.0, and an Adaptive Physics-Based non-rigid registration. We show that the proposed technology provides the finest MRI alignments among all the methods. The Hausdorff Distance is on average up to 3.78 and 3.12 times more accurate compared to the rigid and the other non-rigid registration methods, respectively. Additionally, it brings the end-to-end execution within the real-time constraints imposed by the neurosurgical procedure. In a Linux Dell workstation with 12 Intel Xeon 3.47 GHz CPU cores and 96GB of RAM, it registers the anisotropic volume data in less than 93 seconds and the isotropic data in less than 21 seconds.