4.6 Article

Cerebral perfusion computed tomography deconvolution via structure tensor total variation regularization

期刊

MEDICAL PHYSICS
卷 43, 期 5, 页码 2091-2107

出版社

WILEY
DOI: 10.1118/1.4944866

关键词

cerebral perfusion computed tomography; low-mAs; deconvolution; structure tensor total variation; regularization

资金

  1. National Natural Science Foundation of China [81371544, 61571214, 81501466, 81501541]
  2. National Science and Technology Major Project of the Ministry of Science and Technology of China [2014BAI17B02]
  3. Guangdong Natural Science Foundation [2015A030313271, 2014A030310243, 2015A030310018]
  4. Science and Technology Program of Guangzhou, China [201510010039]

向作者/读者索取更多资源

Purpose: 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. (C) 2016 American Association of Physicists in Medicine.

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