Low-Dose CT Reconstruction Using Low-Dimensional Manifold Prior

doi:10.3778/j.issn.1002-8331.2206-0348

Abstract

Abstract: In this paper, a low-dose CT reconstruction method using the low-dimensional manifold prior is presented. Incorporating with the low-dimensional manifold prior, a penalty weighted least-squares（PWLS-LDMM） approach is presented to reduce the noise in projection（sinogram） domain, and then the image is reconstructed by filtered back-projection（FBP） algorithm. The relative root mean square errors of Shepp-Logan image reconstructed by PWLS-LDMM method are reduced by 64.87%, 54.81% and 7.02%, respectively, as compared with that of FBP, PWLS-QM and PWLS-DL methods. The structural similarity values of Shepp-Logan image reconstructed by PWLS-LDMM method are increased by 16.78%, 1.88% and 1.91%, respectively, as compared with that of FBP, PWLS-QM and PWLS-DL methods. The relative root mean square errors of XCAT image reconstructed by PWLS-LDMM method are reduced by 37.46%, 22.17% and 11.48%, respectively, as compared with that of FBP, PWLS-QM and PWLS-DL methods. The structural similarity values of XCAT image reconstructed by PWLS-LDMM method are increased by 3.33%, 0.73% and 1.22%, respectively, as compared with that of FBP, PWLS-QM and PWLS-DL methods. The relative root mean square errors of clinical image reconstructed by PWLS-LDMM method are reduced by 45.96%, 25.61% and 15.87%, respectively, as compared with that of FBP, PWLS-QM and PWLS-DL methods. The structural similarity values of clinical image reconstructed by PWLS-LDMM method are increased by 19.12%, 7.46% and 8.63%, respectively, as compared with that of FBP, PWLS-QM and PWLS-DL methods. The simulation and clinical data empirical results demonstrate that the new method can effectively suppress the noise and artifacts in low-dose CT images, and maintain the image structure information and spatial resolution.

Key words: low-dose CT, image reconstruction, projection restoration, penalized weighted-least squares, low-dimensional manifold prior

摘要： 针对低剂量CT成像问题，提出了一个基于低维流形先验的低剂量CT重建方法。对投影数据进行统计建模，将低维流形正则化作为先验信息引入到投影数据恢复过程中，从而达到抑制噪声的目的，再使用传统的滤波反投影（filtered back-projection，FBP）算法进行CT图像重建。在Shepp-Logan体膜实验新方法重建结果与FBP、PWLS-QM（penalized weighted least-squares via quadratic?membrane）以及PWLS-DL（penalized weighted least-squares via dictionary learning）方法重建结果相比，相对均方根误差分别降低了64.87%、54.81%和7.02%；结构相似性指标分别提高了16.78%、1.88%和1.91%。在XCAT体膜实验中，新方法重建结果与FBP、PWLS-QM以及PWLS-DL方法重建结果相比，相对均方根误差分别降低了37.46%、22.17%和11.48%；结构相似性指标分别提高了3.33%、0.73%和1.22%。在临床数据实验中，新方法重建结果与FBP、PWLS-QM以及PWLS-DL方法重建结果相比，相对均方根误差分别降低了45.96%、25.61%和15.87%；结构相似性指标分别提高了19.12%、7.46%和8.63%。仿真和临床数据实验结果表明，新方法在有效抑制低剂量CT图像中噪声和伪影同时，可以很好地保持图像的结构信息和空间分辨率。

关键词: 低剂量CT, 图像重建, 投影数据恢复, 惩罚加权最小二乘, 低维流形先验

NIU Shanzhou, LIANG Lijing, LI Shuo, ZHANG Mengzhen, QIU Yang, LIU Hanming, LI Nan. Low-Dose CT Reconstruction Using Low-Dimensional Manifold Prior[J]. Computer Engineering and Applications, 2023, 59(18): 242-248.

牛善洲, 梁礼境, 李硕, 张梦真, 邱洋, 刘汉明, 李楠. 基于低维流形先验的低剂量CT重建方法[J]. 计算机工程与应用, 2023, 59(18): 242-248.

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