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dc.contributor.authorLiu, Ruien
dc.contributor.authorLuo, Yanen
dc.contributor.authorYu, Hengyongen
dc.date.accessioned2019-05-15T14:57:49Z
dc.date.available2019-05-15T14:57:49Z
dc.date.issued2014en
dc.identifier.urihttp://hdl.handle.net/10919/89532
dc.description.abstractThe compressive sensing (CS) theory shows that real signals can be exactly recovered from very few samplings. Inspired by the CS theory, the interior problem in computed tomography is proved uniquely solvable by minimizing the region-of-interest's total variation if the imaging object is piecewise constant or polynomial. This is called CS-based interior tomography. However, the CS-based algorithms require high computational cost due to their iterative nature. In this paper, a graphics processing unit (GPU)-based parallel computing technique is applied to accelerate the CS-based interior reconstruction for practical application in both fan-beam and cone-beam geometries. Our results show that the CS-based interior tomography is able to reconstruct excellent volumetric images with GPU acceleration in a few minutes.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherIEEEen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectComputed tomographyen
dc.subjectcompressed sensingen
dc.subjectparallel computingen
dc.subjectgraphics processing uniten
dc.subjectinterior tomographyen
dc.titleGPU-Based Acceleration for Interior Tomographyen
dc.typeArticle - Refereeden
dc.contributor.departmentSchool of Biomedical Engineering and Sciencesen_US
dc.title.serialIEEE Accessen
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2014.2340372en
dc.identifier.volume2en
dc.type.dcmitypeTexten
dc.identifier.eissn2169-3536en


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Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International