南方医科大学学报 ›› 2022, Vol. 42 ›› Issue (9): 1309-1316.doi: 10.12122/j.issn.1673-4254.2022.09.06

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基于非局部低秩稀疏矩阵分解的低剂量脑灌注CT图像恢复方法

牛善洲,刘 宏,刘沛沄,张梦真,李 硕,梁礼境,李 楠,刘国良   

  1. 赣南师范大学数学与计算机科学学院,赣州市计算成像重点实验室,经济管理学院,江西 赣州 341000;赣南医学院医学信息工程学院,江西 赣州 341000
  • 出版日期:2022-09-20 发布日期:2022-09-28

Nonlocal low-rank and sparse matrix decomposition for low-dose cerebral perfusion CT image restoration

NIU Shanzhou, LIU Hong, LIU Peiyun, ZHANG Mengzhen, LI Shuo, LIANG Lijing, LI Nan, LIU Guoliang   

  1. School of Mathematics and Computer Science, Ganzhou Key Laboratory of Computational Imaging, School of Economics and Management, Gannan Normal University, Ganzhou 341000, China; School of Medical Information Engineering, Gannan Medical University, Ganzhou 341000, China
  • Online:2022-09-20 Published:2022-09-28

摘要: 目的 为了减少脑灌注CT检查的辐射剂量,提高低剂量脑灌注CT图像质量,本文提出一种基于非局部低秩稀疏矩阵分解的低剂量脑灌注CT图像恢复方法。方法 对低剂量脑灌注CT图像进行分块形成一个矩阵,构建低秩稀疏矩阵分解模型进行求解后得到优质的低剂量脑灌注CT图像,最后利用恢复后的脑灌注CT序列图像计算出脑血流动力学参数图像。结果 在数值实验中,滤波反投影算法的图像的平均结构相似性为0.9438,本文方法恢复结果的平均结构相似性提高到0.9765;滤波反投影算法得到的脑血流量和脑血容量参数图像的结构相似性分别为0.7005和0.6856,本文方法得到的脑血流量和脑血容量参数图像的结构相似性提高到0.7871和0.7972。结论 本文方法在低剂量脑灌注CT图像噪声抑制和结构保持方面均有很好的表现,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;图像恢复;非局部低秩稀疏矩阵分解;脑血流动力学参数

Abstract: Objective To present a nonlocal low-rank and sparse matrix decomposition (NLSMD) method for low-dose cerebral perfusion CT image restoration. Methods Low-dose cerebral perfusion CT images were first partitioned into a matrix, and the low- rank and sparse matrix decomposition model was constructed to obtain high-quality low-dose cerebral perfusion CT images. The cerebral hemodynamic parameters were calculated from the restored high-quality CT images. Results In the phantom study, the average structured similarity (SSIM) value of the sequential images obtained by filtered back-projection (FBP) algorithm was 0.9438, which was increased to 0.9765 using the proposed algorithm; the SSIM values of cerebral blood flow (CBF) and cerebral blood volume (CBV) map obtained by FBP algorithm were 0.7005 and 0.6856, respectively, which were increased using the proposed algorithm to 0.7871 and 0.7972, respectively. Conclusion The proposed method can effectively suppress noises in low-dose cerebral perfusion CT images to obtain accurate cerebral hemodynamic parameters.

Key words: low-dose cerebral perfusion CT; image restoration; nonlocal low- rank and sparse matrix decomposition; cerebral hemodynamic parameters