北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (12): 2366-2373.doi: 10.13700/j.bh.1001-5965.2019.0613

• 论文 • 上一篇    下一篇

极稀疏投影数据的CT图像重建

武丽君1, 孙丰荣2, 杨江飞3, 于倩蕾1, 贺芳芳1   

  1. 1. 山东大学 信息科学与工程学院, 青岛 266237;
    2. 山东大学 微电子学院, 济南 250101;
    3. 山东大学附属山东省立医院 医学影像科, 济南 250014
  • 收稿日期:2019-12-05 发布日期:2020-12-28
  • 通讯作者: 孙丰荣 E-mail:sunfr_journal@163.com
  • 作者简介:武丽君,女,硕士研究生。主要研究方向:生物医学信号与信息处理;孙丰荣,男,博士,教授。主要研究方向:CT图像重建、基于人工智能的医学影像分析与诊断。
  • 基金资助:
    国家自然科学基金(81671703);山东省自然科学基金(ZR2019MF048)

CT image reconstruction from ultra-sparse projection data

WU Lijun1, SUN Fengrong2, YANG Jiangfei3, YU Qianlei1, HE Fangfang1   

  1. 1. School of Information Science and Engineering, Shandong University, Qingdao 266237, China;
    2. School of Microelectronics, Shandong University, Jinan 250101, China;
    3. Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong University, Jinan 250014, China
  • Received:2019-12-05 Published:2020-12-28

摘要: 从稀疏投影数据足够精确地重建断层图像,从而能够在显著降低计算机断层成像(CT)检查X-射线辐射剂量的前提下,提供充分适宜影像学临床诊断需求的重建图像。针对圆周扫描扇束投影的二维CT图像重建,提出了投影驱动的系统模型,并将CT迭代图像重建与压缩感知(CS)理论相结合,设计了一种CT迭代图像重建算法,且将算法扩展到圆周扫描锥束投影的三维CT图像重建。仿真实验结果表明:在极稀疏投影数据的条件下([0,2π)范围内扇束/锥束扫描不超过20个投影角度),算法数值精度高,计算复杂度低,内存开销少,有很强的工程实用性。

关键词: 计算机断层成像(CT), 图像重建, 稀疏投影, 投影驱动, 压缩感知(CS)

Abstract: 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)).

Key words: Computerized Tomography (CT), image reconstruction, sparse projection, view driven, Compressed Sensing (CS)

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