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Parallel Computation of Nonrigid Image Registration

dc.contributor.advisorShekhar, Rajen_US
dc.contributor.authorLeung, Frances Kimpiken_US
dc.description.abstractAutomatic intensity-based nonrigid image registration brings significant impact in medical applications such as multimodality fusion of images, serial comparison for monitoring disease progression or regression, and minimally invasive image-guided interventions. However, due to memory and compute intensive nature of the operations, intensity-based image registration has remained too slow to be practical for clinical adoption, with its use limited primarily to as a pre-operative too. Efficient registration methods can lead to new possibilities for development of improved and interactive intraoperative tools and capabilities. In this thesis, we propose an efficient parallel implementation for intensity-based three-dimensional nonrigid image registration on a commodity graphics processing unit. Optimization techniques are developed to accelerate the compute-intensive mutual information computation. The study is performed on the hierarchical volume subdivision-based algorithm, which is inherently faster than other nonrigid registration algorithms and structurally well-suited for data-parallel computation platforms. The proposed implementation achieves more than 50-fold runtime improvement over a standard implementation on a CPU. The execution time of nonrigid image registration is reduced from hours to minutes while retaining the same level of registration accuracy.en_US
dc.titleParallel Computation of Nonrigid Image Registrationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentElectrical Engineeringen_US
dc.subject.pqcontrolledComputer Engineeringen_US
dc.subject.pqcontrolledMedical Imaging and Radiologyen_US
dc.subject.pquncontrolledGPU accelerationen_US
dc.subject.pquncontrolledMutual Informationen_US
dc.subject.pquncontrolledNonrigid Image Registrationen_US

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