Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
Department of Radiation Oncology, Kosin University College of Medicine, Busan, Korea.
Sci Rep. 2021 Feb 11;11(1):3681. doi: 10.1038/s41598-021-83266-1.
This study develops an improved Feldkamp-Davis-Kress (FDK) reconstruction algorithm using non-local total variation (NLTV) denoising and a cubic B-spline interpolation-based backprojector to enhance the image quality of low-dose cone-beam computed tomography (CBCT). The NLTV objective function is minimized on all log-transformed projections using steepest gradient descent optimization with an adaptive control of the step size to augment the difference between a real structure and noise. The proposed algorithm was evaluated using a phantom data set acquired from a low-dose protocol with lower milliampere-seconds (mAs).The combination of NLTV minimization and cubic B-spline interpolation rendered the enhanced reconstruction images with significantly reduced noise compared to conventional FDK and local total variation with anisotropic penalty. The artifacts were remarkably suppressed in the reconstructed images. Quantitative analysis of reconstruction images using low-dose projections acquired from low mAs showed a contrast-to-noise ratio with spatial resolution comparable to images reconstructed using projections acquired from high mAs. The proposed approach produced the lowest RMSE and the highest correlation. These results indicate that the proposed algorithm enables application of the conventional FDK algorithm for low mAs image reconstruction in low-dose CBCT imaging, thereby eliminating the need for more computationally demanding algorithms. The substantial reductions in radiation exposure associated with the low mAs projection acquisition may facilitate wider practical applications of daily online CBCT imaging.
本研究开发了一种改进的 Feldkamp-Davis-Kress (FDK) 重建算法,使用非局部全变分 (NLTV) 去噪和基于三次 B 样条插值的反向投影器来提高低剂量锥形束 CT (CBCT) 的图像质量。NLTV 目标函数在所有对数变换的投影上最小化,使用最陡梯度下降优化,并自适应控制步长,以增加真实结构和噪声之间的差异。使用从低剂量协议获得的具有较低毫安秒 (mAs) 的体模数据集评估了所提出的算法。NLTV 最小化和三次 B 样条插值的组合使得增强后的重建图像与传统的 FDK 和具有各向异性惩罚的局部全变分相比,噪声显著降低。重建图像中的伪影得到了明显抑制。使用来自低 mAs 的低剂量投影进行重建图像的定量分析表明,具有空间分辨率的对比噪声比与使用来自高 mAs 的投影重建的图像相当。所提出的方法产生的 RMSE 最低,相关性最高。这些结果表明,所提出的算法可以将传统的 FDK 算法应用于低剂量 CBCT 成像中的低 mAs 图像重建,从而无需使用更具计算挑战性的算法。与低 mAs 投影采集相关的辐射暴露的大量减少可能会促进日常在线 CBCT 成像的更广泛实际应用。