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Improving Low-Dose Brain Perfusion Computed Tomography Using 3D Dictionary Learning Based Processing

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Though with lower health risks compared with standard dose scanning, low-dose CT perfusion (LDCTp) images tend to be severely degraded by quantum noise and streak artifacts. Accordingly, in this paper, 3D dictionary learning (DL) based processing is proposed to improve the LDCTp image quality. Feature information on both spatial and temporal continuity is exploited via sparse representation to improve LDCTp quality. Experiments on clinical data validate the good performance of the proposed method.
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Keywords: CT PERFUSION (CTP); DICTIONARY LEARNING (DL); LOW-DOSE CT (LDCT); LOW-DOSE CT PERFUSION (LDCTP); SPARSE REPRESENTATION

Document Type: Research Article

Publication date: December 1, 2015

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  • Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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