BACKGROUND: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, anadvanced reconstruction algorithm is also needed. OBJECTIVE:To develop a novel algorithm used for sparse-view CT reconstruction associated with the prior image. METHODS: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. RESULTS: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. CONCLUSIONS: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.

中文翻译：

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基于正常剂量图像先验信息的低剂量CT重建方法

背景：计算机断层扫描 (CT) 的辐射风险一直是患者面临的一个问题，尤其是那些需要重复 CT 扫描的临床情况。对于重复CT扫描的患者，往往采用稀疏扫描等低剂量方案，因此也需要先进的重建算法。目的：开发一种新算法，用于与先验图像相关的稀疏视图 CT 重建。方法:基于正常剂量图像先验信息(PI-NDI)的低剂量CT重建方法,包括待重建对象衰减系数的变换模型和前向投影过程中的先验信息应用进行重建。来自稀疏视图投影数据的 CT 图像。采用数字扩展心脏躯干 (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。测量重建图像的均方根误差 (RMSE)、峰值信噪比 (PSNR) 和平均绝对误差百分比 (MPAE)，以对所提出的 PI-NDI 方法进行定量评估。结果：通过所提出的PI-NDI方法重建的具有稀疏视角投影数据的图像通过目视检查比通过比较方法具有更高的质量。在定量评价方面，PI-NDI方法在稀疏投影数据重建的图像上测得的RMSE与MLEM-TV、PWLS-TV和PWLS-PICCS在全采样投影数据下的结果相当。当投影数据非常稀疏时，PI-NDI 方法重建的图像与比较算法相比，具有更高的 PSNR 值和更低的 MPAE 值。结论：本研究提出了一种新的基于正常剂量图像先验信息的低剂量 CT 重建方法 (PI-NDI)，用于稀疏视图 CT 图像重建。实验结果验证了新方法的性能优于其他最先进的方法。