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High-Speed Reconstruction of Low-Dose CT Using Iterative Techniques for Image-Guided Interventions

dc.contributor.advisorShekhar, Rajen_US
dc.contributor.authorBhat, Venkatesh Bantwalen_US
dc.date.accessioned2008-08-07T05:31:54Z
dc.date.available2008-08-07T05:31:54Z
dc.date.issued2008-07-18en_US
dc.identifier.urihttp://hdl.handle.net/1903/8347
dc.description.abstractMinimally 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.en_US
dc.format.extent4071617 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.titleHigh-Speed Reconstruction of Low-Dose CT Using Iterative Techniques for Image-Guided Interventionsen_US
dc.typeThesisen_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.pqcontrolledEngineering, Electronics and Electricalen_US
dc.subject.pqcontrolledHealth Sciences, Radiologyen_US
dc.subject.pquncontrolledGPUen_US
dc.subject.pquncontrolledCT Reconstructionen_US
dc.subject.pquncontrolledLow Doseen_US
dc.subject.pquncontrolledIterative Techniquesen_US
dc.subject.pquncontrolledClusteren_US
dc.subject.pquncontrolledParaboloidal Surrogate Algorithmen_US


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