title: Low-dose cerebral perfusion computed tomography image restoration via low-rank and total variation regularizations.
abstract: Cerebral perfusion x-ray computed tomography (PCT) is an important functional imaging modality for evaluating cerebrovascular diseases and has been widely used in clinics over the past decades. However, due to the protocol of PCT imaging with repeated dynamic sequential scans, the associative radiation dose unavoidably increases as compared with that used in conventional CT examinations. Minimizing the radiation exposure in PCT examination is a major task in the CT field. In this paper, considering the rich similarity redundancy information among enhanced sequential PCT images, we propose a low-dose PCT image restoration model by incorporating the low-rank and sparse matrix characteristic of sequential PCT images. Specifically, the sequential PCT images were first stacked into a matrix (i.e., low-rank matrix), and then a non-convex spectral norm/regularization and a spatio-temporal total variation norm/regularization were then built on the low-rank matrix to describe the low rank and sparsity of the sequential PCT images, respectively. Subsequently, an improved split Bregman method was adopted to minimize the associative objective function with a reasonable convergence rate. Both qualitative and quantitative studies were conducted using a digital phantom and clinical cerebral PCT datasets to evaluate the present method. Experimental results show that the presented method can achieve images with several noticeable advantages over the existing methods in terms of noise reduction and universal quality index. More importantly, the present method can produce more accurate kinetic enhanced details and diagnostic hemodynamic parameter maps.
authors.affiliation: School of Mathematics and Computer Sciences, Gannan Normal University, Ganzhou 341000, China ; School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
authors.forename: Shanzhou
authors.initials: S
authors.lastname: Niu
authors.affiliation: The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou 510405, China.
authors.forename: Shanli
authors.initials: S
authors.lastname: Zhang
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China ; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Huang
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China ; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China.
authors.forename: Zhaoying
authors.initials: Z
authors.lastname: Bian
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China ; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China.
authors.forename: Wufan
authors.initials: W
authors.lastname: Chen
authors.affiliation: School of Mathematics and Computer Sciences, Gannan Normal University, Ganzhou 341000, China.
authors.forename: Gaohang
authors.initials: G
authors.lastname: Yu
authors.affiliation: Department of Radiology, State University of New York, Stony Brook, NY 11794, USA.
authors.forename: Zhengrong
authors.initials: Z
authors.lastname: Liang
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China ; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Ma
keywords: Cerebral perfusion CT; Low-dose; Low-rank; Regularization; Total variation