Experimental studies on few-view reconstruction for high-resolution micro-CT

被引:16
|
作者
Sen Sharma, Kriti [1 ]
Jin, Xin [2 ]
Holzner, Christian [3 ]
Narayanan, Shree [1 ]
Liu, Baodong [4 ]
Wang, Dong [5 ]
Agah, Masoud [1 ]
Wang, Libing [5 ]
Yu, Hengyong [4 ]
Wang, Ge [6 ]
机构
[1] Virginia Tech, Dept Elect & Comp Engn, Blacksburg, VA USA
[2] Tsinghua Univ, Dept Engn Phys, Beijing 100084, Peoples R China
[3] Xradia Inc, Pleasanton, CA USA
[4] Wake Forest Univ Hlth Sci, VT WFU Sch Biomed Engn & Sci, Biomed Imaging Div, Winston Salem, NC USA
[5] Virginia Tech, Dept Civil & Environm Engn, Blacksburg, VA USA
[6] Rensselaer Polytech Inst, Biomed Imaging Ctr, Troy, NY USA
关键词
Few-view reconstruction; micro-CT; compressed sensing; TV minimization; SART-TV; COMPUTED-TOMOGRAPHY; IMAGE-RECONSTRUCTION; FAN-BEAM; ALGORITHM;
D O I
10.3233/XST-130364
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
High-resolution micro-CT offers 3D non-destructive imaging but scan times are prohibitively large in many cases. Advancements in image reconstruction offer great reduction in number of views while maintaining reconstruction accuracy; yet filtered back projection remains the de facto standard. An extensive study of few-view reconstruction using compressed-sensing based iterative techniques is carried out. Also, a novel 3D micro-CT phantom is proposed, and used for analyzing reconstruction accuracy. Numerical tests, and studies on real micro-CT data show that if measurement noise in projections is not extremely high, the number of views may be reduced to 1/8th of the typically acquired view numbers. The study motivates the adoption of advanced reconstruction techniques to allow faster scanning, lower dosage, and reduced data size in high-resolution micro-CT.
引用
收藏
页码:25 / 42
页数:18
相关论文
共 50 条
  • [1] A preliminary study of few-view image reconstruction of sparse objects in cone-beam micro-CT
    Han, Xiao
    Bian, Junguo
    Eaker, Diane R.
    Sidky, Emil Y.
    Ritman, Erik L.
    Pan, Xiaochuan
    MEDICAL IMAGING 2010: PHYSICS OF MEDICAL IMAGING, 2010, 7622
  • [2] Few-view CT reconstruction with group-sparsity regularization
    Bao, Peng
    Zhou, Jiliu
    Zhang, Yi
    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING, 2018, 34 (09)
  • [3] Few-View CT Reconstruction Method Based on Deep Learning
    Zhao, Ji
    Chen, Zhiqiang
    Zhang, Li
    Jin, Xin
    2016 IEEE NUCLEAR SCIENCE SYMPOSIUM, MEDICAL IMAGING CONFERENCE AND ROOM-TEMPERATURE SEMICONDUCTOR DETECTOR WORKSHOP (NSS/MIC/RTSD), 2016,
  • [4] High-resolution helical cone-beam micro-CT with theoretically-exact reconstruction from experimental data
    Varslot, T.
    Kingston, A.
    Myers, G.
    Sheppard, A.
    MEDICAL PHYSICS, 2011, 38 (10) : 5459 - 5476
  • [5] Accelerated augmented Lagrangian method for few-view CT reconstruction
    Wu, Junfeng
    Mou, Xuanqin
    MEDICAL IMAGING 2012: PHYSICS OF MEDICAL IMAGING, 2012, 8313
  • [7] Image reconstruction from few-view CT data by gradient-domain dictionary learning
    Hu, Zhanli
    Liu, Qiegen
    Zhang, Na
    Zhang, Yunwan
    Peng, Xi
    Wu, Peter Z.
    Zheng, Hairong
    Liang, Dong
    JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY, 2016, 24 (04) : 627 - 638
  • [8] Few-view cone-beam CT reconstruction with deformed prior image
    Zhang, Hua
    Ouyang, Luo
    Huang, Jing
    Ma, Jianhua
    Chen, Wufan
    Wang, Jing
    MEDICAL PHYSICS, 2014, 41 (12)
  • [9] Convergence and stability analysis of the half thresholding based few-view CT reconstruction
    Huang, Hua
    Lu, Chengwu
    Zhang, Lingli
    Wang, Weiwei
    JOURNAL OF INVERSE AND ILL-POSED PROBLEMS, 2020, 28 (06): : 829 - 847
  • [10] High-resolution local imaging using a micro-CT
    Lee, Soo Yeol
    Cho, Min Hyoung
    Choi, Jeong Min
    2008 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1-4, 2008, : 137 - 140