30 March 2016 Robust dynamic myocardial perfusion CT deconvolution using adaptive-weighted tensor total variation regularization
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Abstract
Dynamic myocardial perfusion computed tomography (MPCT) is a promising technique for diagnosis and risk stratification of coronary artery disease by assessing the myocardial perfusion hemodynamic maps (MPHM). Meanwhile, the repeated scanning of the same region results in a relatively large radiation dose to patients potentially. In this work, we present a robust MPCT deconvolution algorithm with adaptive-weighted tensor total variation regularization to estimate residue function accurately under the low-dose context, which is termed `MPD-AwTTV'. More specifically, the AwTTV regularization takes into account the anisotropic edge property of the MPCT images compared with the conventional total variation (TV) regularization, which can mitigate the drawbacks of TV regularization. Subsequently, an effective iterative algorithm was adopted to minimize the associative objective function. Experimental results on a modified XCAT phantom demonstrated that the present MPD-AwTTV algorithm outperforms and is superior to other existing deconvolution algorithms in terms of noise-induced artifacts suppression, edge details preservation and accurate MPHM estimation.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Changfei Gong, Changfei Gong, Dong Zeng, Dong Zeng, Zhaoying Bian, Zhaoying Bian, Jing Huang, Jing Huang, Xinyu Zhang, Xinyu Zhang, Hua Zhang, Hua Zhang, Lijun Lu, Lijun Lu, Qianjin Feng, Qianjin Feng, Zhengrong Liang, Zhengrong Liang, Jianhua Ma, Jianhua Ma, "Robust dynamic myocardial perfusion CT deconvolution using adaptive-weighted tensor total variation regularization", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97833D (30 March 2016); doi: 10.1117/12.2217351; https://doi.org/10.1117/12.2217351
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