Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction |
| |
| Authors: | Yan Zhang Yuanyuan Wang Chen Zhang |
| |
| Institution: | 1. Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur 440010, India;2. Department of Electronics and Communication Engineering, Vishnu Institute of Technology, Bhimavaram, Vishnupur 534202, Andhra Pradesh, India;1. Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China;2. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 210006, China;1. Department of Biomedical Engineering, Wayne State University, Detroit, MI, USA;2. Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI, USA;3. Barbara Ann Karmanos Cancer Institute, Detroit, MI, USA;4. Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA;5. Department of Imaging Sciences, University of Rochester, Rochester, NY, USA;1. University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, China;2. Biomedical Engineering Department, Peking University, Beijing 100191, China;1. Photoacoustic Imaging Lab, Duke University, Durham, NC, 27708, USA;2. Department of Biomedical Engineering, Tsinghua University, Beijing, 100084, China;3. Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Cambridge, MA, 02139, USA;4. Department of Biomedical Engineering and USC Roski Eye Institute, University of Southern California, Los Angeles, CA, 90089, USA;5. Computational Optics Lab, Duke University, Durham, NC, 27708, USA |
| |
| Abstract: | In photoacoustic imaging (PAI), reconstruction from sparse-view sampling data is a remaining challenge in the cases of fast or real-time imaging. In this paper, we present our study on a total variation based gradient descent (TV-GD) algorithm for sparse-view PAI reconstruction. This algorithm involves the total variation (TV) method in compressed sensing (CS) theory. The objective function of the algorithm is modified by adding the TV value of the reconstructed image. With this modification, the reconstructed image could be closer to the real optical energy distribution map. Additionally in the proposed algorithm, the photoacoustic data is processed and the image is updated individually at each detection point. In this way, the calculation with large matrix can be avoided and a more frequent image update can be obtained. Through the numerical simulations, the proposed algorithm is verified and compared with other reconstruction algorithms which have been widely used in PAI. The peak signal-to-noise ratio (PSNR) of the image reconstructed by this algorithm is higher than those by the other algorithms. Additionally, the convergence of the algorithm, the robustness to noise and the tunable parameter are further discussed. The TV-based algorithm is also implemented in the in vitro experiment. The better performance of the proposed method is revealed in the experiments results. From the results, it is seen that the TV-GD algorithm may be a practical and efficient algorithm for sparse-view PAI reconstruction. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
