Total variation combining nonlocal means filtration for image reconstruction in X-ray computed tomography

被引:3
作者
Cai, Ailong [1 ]
Wang, Yizhong [1 ]
Zhong, Xinyi [1 ]
Yu, Xiaohuan [1 ]
Zheng, Zhizhong [1 ]
Wang, Linyuan [1 ]
Li, Lei [1 ]
Yan, Bin [1 ]
机构
[1] Informat Engn Univ, 62 Sci Ave, Zhengzhou 450001, Henan, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Computed tomography; Image reconstruction; nonlocal means filtration; total variation; alternating direction method of multipliers; first order approximation; ALGORITHM; IMPLEMENTATION;
D O I
10.3233/XST-211095
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
BACKGROUND: Image reconstruction for realistic medical images under incomplete observation is still one of the core tasks for computed tomography (CT). However, the stair-case artifacts of Total variation (TV) based ones have restricted the usage of the reconstructed images. OBJECTIVE: This work aims to propose and test an accurate and efficient algorithm to improve reconstruction quality under the idea of synergy between local and nonlocal regularizations. METHODS: The total variation combining the nonlocal means filtration is proposed and the alternating direction method of multipliers is utilized to develop an efficient algorithm. The first order approximation of linear expansion at intermediate point is applied to overcome the computation of the huge CT system matrix. RESULTS: The proposed method improves root mean squared error by 25.6% compared to the recent block-matching sparsity regularization (BMSR) on simulation dataset of 19 views. The structure similarities of image of the new method is higher than 0.95, while that of BMSR is about 0.92. Moreover, on real rabbit dataset of 20 views, the peak signal-to-noise ratio (PSNR) of the new method is 36.84, while using other methods PSNR are lower than 35.81. CONCLUSIONS: The proposed method shows advantages on noise suppression and detail preservations over the competing algorithms used in CT image reconstruction.
引用
收藏
页码:613 / 630
页数:18
相关论文
共 24 条
[11]   Quantifying Admissible Undersampling for Sparsity-Exploiting Iterative Image Reconstruction in X-Ray CT [J].
Jorgensen, Jakob S. ;
Sidky, Emil Y. ;
Pan, Xiaochuan .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2013, 32 (02) :460-473
[12]   Non-local total-variation (NLTV) minimization combined with reweighted L1-norm for compressed sensing CT reconstruction [J].
Kim, Hojin ;
Chen, Josephine ;
Wang, Adam ;
Chuang, Cynthia ;
Held, Mareike ;
Pouliot, Jean .
PHYSICS IN MEDICINE AND BIOLOGY, 2016, 61 (18) :6878-6891
[13]   A Nonlocal Bayesian Image Denoising Algorithm [J].
Lebrun, M. ;
Buades, A. ;
Morel, J. M. .
SIAM JOURNAL ON IMAGING SCIENCES, 2013, 6 (03) :1665-1688
[14]   Image Recovery via Nonlocal Operators [J].
Lou, Yifei ;
Zhang, Xiaoqun ;
Osher, Stanley ;
Bertozzi, Andrea .
JOURNAL OF SCIENTIFIC COMPUTING, 2010, 42 (02) :185-197
[15]   A TV-minimization image-reconstruction algorithm without system matrix [J].
Qiao, Zhiwei ;
Lu, Yang .
JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY, 2021, 29 (05) :851-865
[16]   A simple and fast ASD-POCS algorithm for image reconstruction [J].
Qiao, Zhiwei .
JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY, 2021, 29 (03) :491-506
[17]   FAST CALCULATION OF THE EXACT RADIOLOGICAL PATH FOR A 3-DIMENSIONAL CT ARRAY [J].
SIDDON, RL .
MEDICAL PHYSICS, 1985, 12 (02) :252-255
[18]   Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization [J].
Sidky, Emil Y. ;
Pan, Xiaochuan .
PHYSICS IN MEDICINE AND BIOLOGY, 2008, 53 (17) :4777-4807
[19]   GPU implementation of non-local maximum likelihood estimation method for denoising magnetic resonance images [J].
Upadhya, Adithya H. K. ;
Talawar, Basavaraj ;
Rajan, Jeny .
JOURNAL OF REAL-TIME IMAGE PROCESSING, 2017, 13 (01) :181-192
[20]  
Wahlberg Bo., 2012, IFAC P VOLUMES, V45, P83, DOI [10.3182/20120711-3-be-2027.00310, DOI 10.3182/20120711-3-BE-2027.00310]