计算机应用 ›› 2013, Vol. 33 ›› Issue (06): 1732-1736.DOI: 10.3724/SP.J.1087.2013.01732

• 多媒体技术 • 上一篇    下一篇

基于压缩感知的微分相衬CT迭代图像重建

秦峰1,孙丰荣1,宋尚玲2,张新萍1,李新彩1   

  1. 1. 山东大学 信息科学与工程学院,济南 250100
    2. 山东大学 第二医院设备部,济南 250033
  • 收稿日期:2012-12-17 修回日期:2013-02-28 出版日期:2013-06-01 发布日期:2013-06-05
  • 通讯作者: 秦峰
  • 作者简介:秦峰(1989-),男,安徽六安人,硕士研究生,主要研究方向:医学图像处理;孙丰荣(1969-),山东泰安人,教授,博士,主要研究方向:医学图像处理;宋尚玲(1978-),山东淄博人,博士研究生,主要研究方向:计算机视觉、生物特征识别;张新萍(1983-),女,山东德州人,硕士研究生,主要研究方向:医学图像处理。
  • 基金资助:

    国家自然科学基金资助项目(60872092);山东省自然科学基金资助项目(ZR2010FM012);教育部留学回国人员科研启动基金资助项目

Iterative image reconstruction for differential phase contrast CT based on compressive sensing

QIN Feng1,SUN Fengrong1,SONG Shangling2,ZHANG Xinping1,LI Xincai1   

  1. 1. School of Information Science and Engineering, Shandong University, Jinan Shandong 250100, China
    2. Equipment Department of the Second Hospital, Shandong University, Jinan Shandong 250033, China
  • Received:2012-12-17 Revised:2013-02-28 Online:2013-06-05 Published:2013-06-01
  • Contact: QIN Feng

摘要: X-射线相衬计算机断层成像(CT)通过X-射线穿过样品后相位信息的改变来得到高衬度的图像,特别适用于轻元素的成像,并且可以获得远高于传统吸收衬度CT的密度分辨率。基于光栅的微分相衬CT(DPC-CT)由于可以使用常规的X射线光源而有着巨大的临床应用前景,但DPC-CT成像的X-射线辐射剂量问题尤为突出,是其走向实际应用的瓶颈。针对上述不足,提出了一种微分相衬CT迭代图像重建算法(DD-L1),该方法将压缩感知(CS)理论和CT迭代图像重建技术相结合并引入距离驱动(DD)的正/反投影运算计算策略。仿真实验结果表明,DD-L1算法能够在投影数据不完备的情况下得到较高质量的重建图像。

关键词: 微分相衬CT, 图像重建, 压缩感知, 距离驱动

Abstract: The X-ray phase contrast Computed Tomography (CT) can produce high contrast images by the X-ray phase information alteration, which comes forth after the X-ray passes through the sample, and it is highly favorable to the imaging of light elements and can get much higher contrast resolution than the absorption contrast CT. Grating-based Differential Phase Contrast CT (DPC-CT) shows great clinical prospects due to the possibility of using a conventional X-ray source, but the X-ray radiation dose issue limits its clinical applications. Concerning such inadequacies, an image reconstruction method for DPC-CT named DD-L1 was proposed. This algorithm combined Compressive Sensing (CS) theory with CT iterative reconstruction technique and introduced distance driven forward and backward projection computation strategy. The experimental results show that DD-L1 algorithm can generate tomographic images of higher quality even when the projection data is incomplete.

Key words: differential contrast Computed Tomography (CT), image reconstruction, Compressive Sensing (CS), distance driven

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