9 March 2018 CPCT-LRTDTV: cerebral perfusion CT image restoration via a low rank tensor decomposition with total variation regularization
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Ischemic stroke is the leading cause of serious and long-term disability worldwide. The cerebral perfusion computed tomography (CPCT) is an important imaging modality for diagnosis in case of an ischemic stroke event by providing cerebral hemodynamic information. However, due to the dynamic sequential scans in CPCT, the associative radiation dose unavoidably increases compared with conventional CT. In this work, we present a robust CPCT image restoration algorithm with a spatial total variation (TV) regularization and a low rank tensor decomposition (LRTD) to estimate high-quality CPCT images and corresponding hemodynamic map in the case of low-dose, which is termed “CPCT-LRTDTV”. Specifically, in the LRTDTV regularization, the spatial TV is introduced to describe local smoothness of the CPCT images, and the LRTD is adopted to fully characterize spatial and time dimensional correlation existing in the CPCT images. Subsequently, an alternating optimization algorithm was adopted to minimize the associative objective function. To evaluate the presented CPCT-LRTDTV algorithm, both qualitative and quantitative experiments are conducted on digital perfusion brain phantom and clinical patient. Experimental results demonstrate that the present CPCT-LRTDTV algorithm is superior to other existing algorithms with better noise-induced artifacts reduction, resolution preservation and accurate hemodynamic map estimation.
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Jiangjun Peng, Jiangjun Peng, Dong Zeng, Dong Zeng, Jianhua Ma, Jianhua Ma, Yao Wang, Yao Wang, Deyu Meng, Deyu Meng, } "CPCT-LRTDTV: cerebral perfusion CT image restoration via a low rank tensor decomposition with total variation regularization", Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 1057337 (9 March 2018); doi: 10.1117/12.2293964; https://doi.org/10.1117/12.2293964

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