Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
J Xray Sci Technol. 2020;28(6):1091-1111. doi: 10.3233/XST-200716.
Radiation risk from computed tomography (CT) is always an issue for patients, especially those in clinical conditions in which repeated CT scanning is required. For patients undergoing repeated CT scanning, a low-dose protocol, such as sparse scanning, is often used, and consequently, an advanced reconstruction algorithm is also needed.
To develop a novel algorithm used for sparse-view CT reconstruction associated with the prior image.
A low-dose CT reconstruction method based on prior information of normal-dose image (PI-NDI) involving a transformed model for attenuation coefficients of the object to be reconstructed and prior information application in the forward-projection process was used to reconstruct CT images from sparse-view projection data. A digital extended cardiac-torso (XCAT) ventral phantom and a diagnostic head phantom were employed to evaluate the performance of the proposed PI-NDI method. The root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR) and mean percent absolute error (MPAE) of the reconstructed images were measured for quantitative evaluation of the proposed PI-NDI method.
The reconstructed images with sparse-view projection data via the proposed PI-NDI method have higher quality by visual inspection than that via the compared methods. In terms of quantitative evaluations, the RMSE measured on the images reconstructed by the PI-NDI method with sparse projection data is comparable to that by MLEM-TV, PWLS-TV and PWLS-PICCS with fully sampled projection data. When the projection data are very sparse, images reconstructed by the PI-NDI method have higher PSNR values and lower MPAE values than those from the compared algorithms.
This study presents a new low-dose CT reconstruction method based on prior information of normal-dose image (PI-NDI) for sparse-view CT image reconstruction. The experimental results validate that the new method has superior performance over other state-of-art methods.
来自计算机断层扫描（CT）的辐射风险一直是患者关注的问题，尤其是那些需要重复 CT 扫描的临床情况。对于需要重复 CT 扫描的患者，通常使用低剂量方案，如稀疏扫描，因此也需要先进的重建算法。
开发一种新的稀疏视图 CT 重建算法，该算法与先验图像相关联。
使用基于正常剂量图像先验信息（PI-NDI）的低剂量 CT 重建方法，该方法涉及用于要重建的物体衰减系数的变换模型以及先验信息在正向投影过程中的应用，以从稀疏视图投影数据重建 CT 图像。使用数字扩展心脏-胸部（XCAT）腹侧体模和诊断头部体模来评估所提出的 PI-NDI 方法的性能。通过均方根误差（RMSE）、峰值信噪比（PSNR）和平均绝对误差百分比（MPAE）来测量重建图像的性能，以对所提出的 PI-NDI 方法进行定量评估。
通过所提出的 PI-NDI 方法从稀疏视图投影数据重建的图像通过视觉检查比通过比较方法重建的图像质量更高。在定量评估方面，通过稀疏投影数据重建的 PI-NDI 方法的 RMSE 与通过完全采样投影数据重建的 MLEM-TV、PWLS-TV 和 PWLS-PICCS 相当。当投影数据非常稀疏时，通过 PI-NDI 方法重建的图像具有比比较算法更高的 PSNR 值和更低的 MPAE 值。
本研究提出了一种新的基于正常剂量图像先验信息（PI-NDI）的低剂量 CT 重建方法，用于稀疏视图 CT 图像重建。实验结果验证了该新方法优于其他最先进的方法。
