Abstract

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Title:: Statistical iterative reconstruction for low-dose cerebral perfusion CT using nonlocal low-rank and sparse matrix decomposition

Author(s):: NIU Shanzhou1; 2; LI Shuo1; 2; LIANG Lijing1; 2; XIE Guoqiang1; 2; LIU Hanming1; 1. School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China 2. Ganzhou Key Laboratory of Computational Imaging, Gannan Normal University, Ganzhou 341000, China

Keywords:: Keywords: cerebral perfusion CT nonlocal low-rank and sparse matrix decomposition penalized weighted least-squares image reconstruction

Abstract:: Abstract: The cerebral perfusion CT (CPCT) imaging requires continuous repetition scans, and the corresponding radiation dose is significantly increased compared with that of conventional CT. A low-dose CPCT statistical iterative reconstruction method using penalized weighted least-squares based on nonlocal low-rank and sparse matrix decomposition (PWLS-NLSMD) is present to reduce the radiation dose of CPCT imaging. After partitioning the sequence images of CPCT, a NLSMD model is developed, and the corresponding objective function is solved for reconstructing CPCT images. PWLS-NLSMD increases the structural similarity and feature similarity of the cerebral blood volume map and those of the mean transit time map by 38.07%, 13.17%, 59.73% and 20.26% as compared with filtered back-projection algorithm, and 5.61%, 2.47%, 0.28% and 0.70% as compared with penalized weighted least-squares based on low-rank and sparse matrix decomposition. PWLS-NLSMD can preserve the edge and structure information while effectively suppressing the noise, and obtain more accurate cerebral hemodynamic parameters.