High performance algorithms for medical image registration with applications in neuroradiology
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This dissertation concerns the design, analysis and High-Performance Computing (HPC) implementation of fast algorithms for large deformation diffeomorphic registration and its application in quantifying abnormal anatomical deformations in Magnetic Resonance Image (MRI) scans of brain tumor patients. Image registration finds point correspondences between two images by solving an optimization problem. It is a fundamental and computationally expensive operation that finds applications in computer vision and medical image analysis. Diffeomorphic registration is a non-convex and nonlinear inverse problem and, as a result, presents significant numerical and computational challenges. Designing and implementing efficient and accurate numerical schemes on modern computer architectures is the key to accelerating and sometimes even enabling the development of image analysis workflows. In this dissertation, we contribute to several aspects of diffeomorphic registration: (i) a novel preconditioner that improves performance and scalability, (ii) algorithms and their scalable implementation on heterogeneous compute architectures, and (ii) applications in neuroradiology. Our work on diffeomorphic image registration is based on CLAIRE – a formulation, algorithmic framework, and software developed at the University of Texas at Austin. As the first highlight of our contributions, we introduced a novel two-level Hessian preconditioner that results in an improvement of 2.5× in CLAIRE’s performance. As a second highlight, our optimized HPC implementation yields orders of magnitude speedup as CLAIRE now supports GPU architectures and distributed memory parallelism via GPU-aware message passing interface (MPI). CLAIRE can register clinical-grade brain MRI scans of size 256³ in under 5 seconds on a single NVIDIA V100 GPU. For research-grade high-resolution volumetric images, e.g., mouse brain CLARITY images of size 2816 × 3016 × 1162, CLAIRE takes under 30 minutes using 256 NVIDIA V100 GPUs on the Texas Advanced Computing Center’s (TACC) Longhorn supercomputer. To the best of our knowledge, CLAIRE is the most scalable image registration algorithm and software. CLAIRE has been open-sourced under the GNU v3 license and is available on Github at https://github.com/andreasmang/claire. Our target clinical application concerns the utilization of image registration to characterize the mass effect in MRI scans of patients with glioblastoma, a fatal brain cancer. Mass effect is the mechanical deformation in surrounding healthy tissue caused by the growing tumor. The location and degree of mass effect could aid in differential diagnosis and treatment planning. Towards this end, we introduce an algorithm that integrates CLAIRE, statistical analysis for abnormality detection, and machine learning to quantify and localize mass effect. Given a patient’s brain tumor scan, we generate a clinical summary with (i) an estimate of the degree of mass effect along with a severity label – mild, moderate, or severe with up to 62% accuracy, (ii) a heatmap of mass effect for the brain scan and, (iii) a list of specific anatomical regions, e.g. frontal lobe, which is statistically likely to possess significant mass effect.