[用于低剂量脑灌注CT图像恢复的非局部低秩和稀疏矩阵分解]
[Nonlocal low-rank and sparse matrix decomposition for low-dose cerebral perfusion CT image restoration].
作者信息
Niu S, Liu H, Liu P, Zhang M, Li S, Liang L, Li N, Liu G
机构信息
School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China.
Ganzhou Key Laboratory of Computational Imaging, Gannan Normal University, Ganzhou 341000, China.
出版信息
Nan Fang Yi Ke Da Xue Xue Bao. 2022 Sep 20;42(9):1309-1316. doi: 10.12122/j.issn.1673-4254.2022.09.06.
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.
目的
提出一种用于低剂量脑灌注CT图像恢复的非局部低秩稀疏矩阵分解（NLSMD）方法。
方法
首先将低剂量脑灌注CT图像分割成一个矩阵，构建低秩稀疏矩阵分解模型以获得高质量的低剂量脑灌注CT图像。从恢复后的高质量CT图像中计算脑血流动力学参数。
结果
在体模研究中，滤波反投影（FBP）算法获得的序列图像的平均结构相似性（SSIM）值为0.9438，使用所提出的算法后增加到0.9765；FBP算法获得的脑血流量（CBF）和脑血容量（CBV）图的SSIM值分别为0.7005和0.6856，使用所提出的算法后分别增加到0.7871和0.7972。
结论
所提出的方法可以有效抑制低剂量脑灌注CT图像中的噪声，以获得准确的脑血流动力学参数。
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