| Citation: | WU Lijun, SUN Fengrong, YANG Jiangfei, et al. CT image reconstruction from ultra-sparse projection data[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(12): 2366-2373. doi: 10.13700/j.bh.1001-5965.2019.0613(in Chinese)
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In order to provide the reconstructed images which are suitable for the clinical imaging diagnosis, this study focuses on reconstructing the tomographic images from sparse projection data with sufficient accuracy under the premise of significantly reducing the X-ray dose of Computerized Tomography (CT) examination. Aimed at 2D image reconstruction of fan-beam projection under circular scanning, this paper proposes the view driven model and designs a CT iterative image reconstruction algorithm by combining the iterative algorithm and the Compressed Sensing (CS) theory. Then the algorithm is extended to 3D image reconstruction of cone-beam projection under circular scanning. The simulation results show that the algorithm has high numerical accuracy, low computational complexity, less memory overhead, and strong engineering practicability under the condition of ultra-sparse projection data (no more than 20 projection angles for fan-beam/cone-beam scanning in the range of[0, 2π)).
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