TY - GEN
T1 - Noise suppression for cerebral perfusion CT via intrinsic tensor sparsity regularization
T2 - 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop, NSS/MIC/RTSD 2016
AU - Zeng, Dong
AU - Xie, Qi
AU - Bian, Zhaoying
AU - Meng, Deyu
AU - Huang, Jing
AU - Xu, Zongben
AU - Liang, Zhengrong
AU - Chen, Wufan
AU - Ma, Jianhua
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/10/16
Y1 - 2017/10/16
N2 - Cerebral perfusion computed tomography (CPCT) provides functional information about capillary-level hemodynamic of brain parenchyma, which is an important tool for evaluating acute stroke, vasospasm and other neurovascular disorders Meanwhile, the side effect of accumulated radiation, relating to genetic or cancerous diseases due to repeated dynamic scans at the same site, has caused great public concerns. One interesting general approach to counter the effect of overdose of radiation is to develop an iterative image restoration algorithm. In this paper, we focus on presenting a tensor-based restoration approach via fully adopting the intrinsic tensor sparsity (ITS) characteristics of CPCT sequential images. For simplicity, the present approach is termed as "CP-ITS". More specifically, the gains of ITS measure are to adopt the nonlocal self-similarity across space and global correlation along frames, therefore ITS measure can encode both sparsity insights delivered by the most Tucker and CANDECOMP/PAREFAC (CP) low-rank decomposition for a general tensor. Then the ITS measure is introduced into the CPCT images restoration model through characterizing the tensors formed by nonlocal similar patches with in CPCT images. To minimize the associative objective function, we proposed an efficient alternating direction method of multipliers (ADMM) based algorithm. Extensive evaluations on the digital phantom data demonstrate the performance of the present CP-ITS approach for low-dose CPCT imaging.
AB - Cerebral perfusion computed tomography (CPCT) provides functional information about capillary-level hemodynamic of brain parenchyma, which is an important tool for evaluating acute stroke, vasospasm and other neurovascular disorders Meanwhile, the side effect of accumulated radiation, relating to genetic or cancerous diseases due to repeated dynamic scans at the same site, has caused great public concerns. One interesting general approach to counter the effect of overdose of radiation is to develop an iterative image restoration algorithm. In this paper, we focus on presenting a tensor-based restoration approach via fully adopting the intrinsic tensor sparsity (ITS) characteristics of CPCT sequential images. For simplicity, the present approach is termed as "CP-ITS". More specifically, the gains of ITS measure are to adopt the nonlocal self-similarity across space and global correlation along frames, therefore ITS measure can encode both sparsity insights delivered by the most Tucker and CANDECOMP/PAREFAC (CP) low-rank decomposition for a general tensor. Then the ITS measure is introduced into the CPCT images restoration model through characterizing the tensors formed by nonlocal similar patches with in CPCT images. To minimize the associative objective function, we proposed an efficient alternating direction method of multipliers (ADMM) based algorithm. Extensive evaluations on the digital phantom data demonstrate the performance of the present CP-ITS approach for low-dose CPCT imaging.
UR - https://www.scopus.com/pages/publications/85041749840
U2 - 10.1109/NSSMIC.2016.8069427
DO - 10.1109/NSSMIC.2016.8069427
M3 - Conference contribution
AN - SCOPUS:85041749840
T3 - 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop, NSS/MIC/RTSD 2016
BT - 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop, NSS/MIC/RTSD 2016
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 October 2016 through 6 November 2016
ER -