Improving Low-Dose Brain Perfusion Computed Tomography Using 3D Dictionary Learning Based Processing
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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Document Type: Research Article
Publication date: December 1, 2015
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