南方医科大学学报

南方医科大学学报 ›› 2021, Vol. 41 ›› Issue (8): 1226-1233.doi: 10.12122/j.issn.1673-4254.2021.08.15

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低剂量脑灌注CT图像恢复方法：基于先验图像约束扩散张量

牛善洲，刘 宏，刘沛沄，张梦真，邱 洋，黎 钰，谢国强，刘国良，卢绍辉   

  1. 赣南师范大学数学与计算机科学学院，江西 赣州 341000；赣南医学院医学信息工程学院，第一附属医院，江西 赣州 341000
  • 出版日期:2021-08-20 发布日期:2021-09-07

Low-dose cerebral perfusion CT image restoration using prior image constrained diffusion tensor

NIU Shanzhou, LIU Hong, LIU Peiyun, ZHANG Mengzhen, QIU Yang, LI Yu, XIE Guoqiang, LIU Guoliang, LU Shaohui   

  1. School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China; School of Medical Information Engineering, First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
  • Online:2021-08-20 Published:2021-09-07

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摘要： 目的 为减少脑灌注CT检查的辐射剂量，本文提出了一种基于先验图像约束扩散张量的低剂量脑灌注CT图像恢复方法。方法 脑灌注CT图像之间存在丰富的结构冗余信息，高质量的先验图像可以作为结构互补信息引入到低剂量脑灌注CT图像恢复过程中，抑制低剂量脑灌注CT图像中的噪声和伪影。首先，分别计算出先验图像和低剂量脑灌注CT图像的扩散张量，然后构造先验图像约束的扩散张量，并利用新构建的扩散张量对低剂量脑灌注CT图像进行滤波。结果 在数值体膜实验中，本文方法得到的CBF参数图像的SSIM值与FBP算法相比提高了63%。在临床实验中，本文方法得到的CBF参数图像的SSIM值 与FBP算法相比提高了45%。结论 数值体膜和临床数据实验结果表明本文方法在抑制低剂量脑灌注CT图像噪声和伪影的同时可以保持图像的结构细节特征，并且可以获取准确的脑血流动力学参数图像。

关键词: 低剂量脑灌注CT；扩散张量；先验图像；血流动力学参数

Abstract: Objective We propose an efficient method to reduce the noise in low-dose cerebral perfusion CT images using prior image constrained diffusion tensor to reduce the radiation dose in brain CT examination. Methods By utilizing the redundant information in cerebral perfusion CT images, we embedded the complementary structure information in prior images into low-dose cerebral perfusion CT image restoration process to suppress the image noise and artifacts. We first calculated the diffusion tensor for the low-dose cerebral perfusion CT image and prior image separately and then constructed a prior image constrained diffusion tensor (PICDT) to incorporate the structure information from the prior image into low-dose image restoration process. Results In experiments with the Shepp-Logan phantom, the SSIM value of CBF map obtained by the proposed algorithm was increased by 63% as compared with that of the FBP algorithm. In analysis of the clinical dataset, the SSIM value of CBF map obtained by the proposed algorithm was increased by 45% as compared with that of FBP algorithm. Conclusion The proposed method can effectively reduce noises and artifacts of low-dose cerebral perfusion CT images while maintaining the structural details to obtain accurate cerebral hemodynamic maps.

Key words: low-dose cerebral perfusion CT; diffusion tensor; priori image; hemodynamic parameter maps