title: Robust dynamic myocardial perfusion CT deconvolution for accurate residue function estimation via adaptive-weighted tensor total variation regularization: a preclinical study.
abstract: Dynamic myocardial perfusion computed tomography (MPCT) is a promising technique for quick diagnosis and risk stratification of coronary artery disease. However, one major drawback of dynamic MPCT imaging is the heavy radiation dose to patients due to its dynamic image acquisition protocol. In this work, to address this issue, we present a robust dynamic MPCT deconvolution algorithm via adaptive-weighted tensor total variation (AwTTV) regularization for accurate residue function estimation with low-mA s data acquisitions. For simplicity, the presented method is termed 'MPD-AwTTV'. More specifically, the gains of the AwTTV regularization over the original tensor total variation regularization are from the anisotropic edge property of the sequential MPCT images. To minimize the associative objective function we propose an efficient iterative optimization strategy with fast convergence rate in the framework of an iterative shrinkage/thresholding algorithm. We validate and evaluate the presented algorithm using both digital XCAT phantom and preclinical porcine data. The preliminary experimental results have demonstrated that the presented MPD-AwTTV deconvolution algorithm can achieve remarkable gains in noise-induced artifact suppression, edge detail preservation, and accurate flow-scaled residue function and MPHM estimation as compared with the other existing deconvolution algorithms in digital phantom studies, and similar gains can be obtained in the porcine data experiment.
authors: Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, People's Republic of China. Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People's Republic of China.
authors: Dong
authors: D
authors: Zeng
authors: Changfei
authors: C
authors: Gong
authors: Zhaoying
authors: Z
authors: Bian
authors: Jing
authors: J
authors: Huang
authors: Xinyu
authors: X
authors: Zhang
authors: Hua
authors: H
authors: Zhang
authors: Lijun
authors: L
authors: Lu
authors: Shanzhou
authors: S
authors: Niu
authors: Zhang
authors: Z
authors: Zhang
authors: Zhengrong
authors: Z
authors: Liang
authors: Qianjin
authors: Q
authors: Feng
authors: Wufan
authors: W
authors: Chen
authors: Jianhua
authors: J
authors: Ma
mesh_terms: D000465:Algorithms; D000818:Animals; D002560:Cerebrovascular Circulation; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D055414:Myocardial Perfusion Imaging / Q000379:methods*; D019047:Phantoms, Imaging; D013552:Swine; D014057:Tomography, X-Ray Computed / Q000379:methods*