title: Cerebral perfusion computed tomography deconvolution via structure tensor total variation regularization.
abstract: OBJECTIVE
  Cerebral perfusion computed tomography (PCT) imaging as an accurate and fast acute ischemic stroke examination has been widely used in clinic. Meanwhile, a major drawback of PCT imaging is the high radiation dose due to its dynamic scan protocol. The purpose of this work is to develop a robust perfusion deconvolution approach via structure tensor total variation (STV) regularization (PD-STV) for estimating an accurate residue function in PCT imaging with the low-milliampere-seconds (low-mAs) data acquisition.
  
  
  METHODS
  Besides modeling the spatio-temporal structure information of PCT data, the STV regularization of the present PD-STV approach can utilize the higher order derivatives of the residue function to enhance denoising performance. To minimize the objective function, the authors propose an effective iterative algorithm with a shrinkage/thresholding scheme. A simulation study on a digital brain perfusion phantom and a clinical study on an old infarction patient were conducted to validate and evaluate the performance of the present PD-STV approach.
  
  
  RESULTS
  In the digital phantom study, visual inspection and quantitative metrics (i.e., the normalized mean square error, the peak signal-to-noise ratio, and the universal quality index) assessments demonstrated that the PD-STV approach outperformed other existing approaches in terms of the performance of noise-induced artifacts reduction and accurate perfusion hemodynamic maps (PHM) estimation. In the patient data study, the present PD-STV approach could yield accurate PHM estimation with several noticeable gains over other existing approaches in terms of visual inspection and correlation analysis.
  
  
  CONCLUSIONS
  This study demonstrated the feasibility and efficacy of the present PD-STV approach in utilizing STV regularization to improve the accuracy of residue function estimation of cerebral PCT imaging in the case of low-mAs.
authors.affiliation: Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Zeng
authors.affiliation: Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China.
authors.forename: Xinyu
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and 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: Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and 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: Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China.
authors.forename: Hua
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China.
authors.forename: Lijun
authors.initials: L
authors.lastname: Lu
authors.affiliation: Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China.
authors.forename: Wenbing
authors.initials: W
authors.lastname: Lyu
authors.affiliation: Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China.
authors.forename: Qianjin
authors.initials: Q
authors.lastname: Feng
authors.affiliation: Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and 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: Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Ma
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000000981:diagnostic imaging* / Q000503:physiopathology; D020520:Brain Infarction / Q000000981:diagnostic imaging / Q000503:physiopathology; D002560:Cerebrovascular Circulation; D003198:Computer Simulation; D005240:Feasibility Studies; D006439:Hemodynamics; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008953:Models, Anatomic; D055420:Perfusion Imaging / Q000379:methods*; D019047:Phantoms, Imaging; D014057:Tomography, X-Ray Computed / Q000379:methods*