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
中国博士后科学基金; 中国国家自然科学基金;
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
  • [1] SIMULTANEOUS ALGEBRAIC RECONSTRUCTION TECHNIQUE (SART) - A SUPERIOR IMPLEMENTATION OF THE ART ALGORITHM
    ANDERSEN, AH
    KAK, AC
    [J]. ULTRASONIC IMAGING, 1984, 6 (01) : 81 - 94
  • [2] A review of image denoising algorithms, with a new one
    Buades, A
    Coll, B
    Morel, JM
    [J]. MULTISCALE MODELING & SIMULATION, 2005, 4 (02) : 490 - 530
  • [3] Block-matching sparsity regularization-based image reconstruction for low-dose computed tomography
    Cai, Ailong
    Li, Lei
    Zheng, Zhizhong
    Wang, Linyuan
    Yan, Bin
    [J]. MEDICAL PHYSICS, 2018, 45 (06) : 2439 - 2452
  • [4] Block matching sparsity regularization-based image reconstruction for incomplete projection data in computed tomography
    Cai, Ailong
    Li, Lei
    Zheng, Zhizhong
    Zhang, Hanming
    Wang, Linyuan
    Hu, Guoen
    Yan, Bin
    [J]. PHYSICS IN MEDICINE AND BIOLOGY, 2018, 63 (03)
  • [5] Compressed sensing
    Donoho, DL
    [J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 2006, 52 (04) : 1289 - 1306
  • [6] Engl H W., 1996, REGULARIZATION INVER, V375
  • [7] Ertas M., 2014, P 2014 22 SIGN PROC
  • [8] Iterative image reconstruction using non-local means with total variation from insufficient projection data
    Ertas, Metin
    Yildirim, Isa
    Kamasak, Mustafa
    Akan, Aydin
    [J]. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY, 2016, 24 (01) : 1 - 8
  • [9] An iterative tomosynthesis reconstruction using total variation combined with non-local means filtering
    Ertas, Metin
    Yildirim, Isa
    Kamasak, Mustafa
    Akan, Aydin
    [J]. BIOMEDICAL ENGINEERING ONLINE, 2014, 13
  • [10] State of the Art: Iterative CT Reconstruction Techniques
    Geyer, Lucas L.
    Schoepf, U. Joseph
    Meinel, Felix G.
    Nance, John W., Jr.
    Bastarrika, Gorka
    Leipsic, Jonathon A.
    Paul, Narinder S.
    Rengo, Marco
    Laghi, Andrea
    De Cecco, Carlo N.
    [J]. RADIOLOGY, 2015, 276 (02) : 338 - 356