Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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    Parallelization of Non-Rigid Image Registration
    (2008) Philip, Mathew; Shekhar, Raj; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Non-rigid image registration finds use in a wide range of medical applications ranging from diagnostics to minimally invasive image-guided interventions. Automatic non-rigid image registration algorithms are computationally intensive in that they can take hours to register two images. Although hierarchical volume subdivision-based algorithms are inherently faster than other non-rigid registration algorithms, they can still take a long time to register two images. We show a parallel implementation of one such previously reported and well tested algorithm on a cluster of thirty two processors which reduces the registration time from hours to a few minutes. Mutual information (MI) is one of the most commonly used image similarity measures used in medical image registration and also in the mentioned algorithm. In addition to parallel implementation, we propose a new concept based on bit-slicing to accelerate computation of MI on the cluster and, more generally, on any parallel computing platform such as the Graphics processor units (GPUs). GPUs are becoming increasingly common for general purpose computing in the area of medical imaging as they can execute algorithms faster by leveraging the parallel processing power they offer. However, the standard implementation of MI does not map well to the GPU architecture, leading earlier investigators to compute only an inexact version of MI on the GPU to achieve speedup. The bit-slicing technique we have proposed enables us to demonstrate an exact implementation of MI on the GPU without adversely affecting the speedup.
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    High-Speed Reconstruction of Low-Dose CT Using Iterative Techniques for Image-Guided Interventions
    (2008-07-18) Bhat, Venkatesh Bantwal; Shekhar, Raj; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Minimally invasive image-guided interventions(IGIs) lead to improved treatment outcomes while significantly reducing patient trauma. Because of features such as fast scanning, high resolution, three-dimensional view and ease of operation, Computed-Tomography(CT) is increasingly the choice for IGIs. The risk of radiation exposure, however, limits its current and future use. We perform ultra low-dose scanning to overcome this limitation. To address the image quality problem at low doses, we reconstruct images using the iterative Paraboloidal Surrogate(PS) algorithm. Using actual scanner data, we demonstrate improvement in the quality of reconstructed images using the iterative algorithm at low doses as compared to the standard Filtered Back Projection(FBP) technique. We also accelerate the PS algorithm on a cluster of 32 processors and a GPU. We demonstrate approximately 20 times speedup for the cluster and two orders of improvement in speed for the GPU, while maintaining comparable image quality to the traditional uni-processor implementation.