基于低维流形先验的低剂量CT重建方法. (Chinese)

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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