Lee Ho, Sung Jiwon, Choi Yeonho, Kim Jun Won, Lee Ik Jae
Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Front Oncol. 2021 Oct 21;11:751057. doi: 10.3389/fonc.2021.751057. eCollection 2021.
Conventional non-local total variation (NLTV) approaches use the weight of a non-local means (NLM) filter, which degrades performance in low-dose cone-beam computed tomography (CBCT) images generated with a low milliampere-seconds (mAs) parameter value because a local patch used to determine the pixel weights comprises noisy-damaged pixels that reduce the similarity between corresponding patches. In this paper, we propose a novel type of NLTV based on a combination of mutual information (MI): MI-NLTV. It is based on a statistical measure for a similarity calculation between the corresponding bins of non-local patches vs. a reference patch. The weight is determined in terms of a statistical measure comprising the MI value between corresponding non-local patches and the reference-patch entropy. The MI-NLTV denoising process is applied to CBCT images generated by the analytical reconstruction algorithm using a ray-driven backprojector (RDB). The MI-NLTV objective function is minimized based on the steepest gradient descent optimization to augment the difference between a real structure and noise, cleaning noisy pixels without significant loss of the fine structure and details that remain in the reconstructed images. The proposed method was evaluated using patient data and actual phantom measurement data acquired with lower mAs. The results show that integrating the RDB further enhances the MI-NLTV denoising-based analytical reconstruction algorithm to achieve a higher CBCT image quality when compared with those generated by NLTV denoising-based approach, with an average of 15.97% higher contrast-to-noise ratio, 2.67% lower root mean square error, 0.12% lower spatial non-uniformity, 1.14% higher correlation, and an average of 18.11% higher detectability index. These quantitative results indicate that the incorporation of MI makes the NLTV more stable and robust than the conventional NLM filter for low-dose CBCT imaging. In addition, achieving clinically acceptable CBCT image quality despite low-mAs projection acquisition can reduce the burden on common online CBCT imaging, improving patient safety throughout the course of radiotherapy.
传统的非局部全变分（NLTV）方法使用非局部均值（NLM）滤波器的权重，这在使用低毫安秒（mAs）参数值生成的低剂量锥束计算机断层扫描（CBCT）图像中会降低性能，因为用于确定像素权重的局部块包含噪声损坏的像素，这会降低相应块之间的相似度。在本文中，我们提出了一种基于互信息（MI）组合的新型NLTV：MI-NLTV。它基于一种统计度量，用于计算非局部块与参考块的相应区间之间的相似度。权重是根据一种统计度量确定的，该度量包括相应非局部块与参考块熵之间的MI值。MI-NLTV去噪过程应用于使用射线驱动反投影器（RDB）的解析重建算法生成的CBCT图像。基于最陡梯度下降优化，将MI-NLTV目标函数最小化，以增强真实结构与噪声之间的差异，在不显著损失重建图像中保留的精细结构和细节的情况下清除噪声像素。使用患者数据和以较低mAs获取的实际体模测量数据对所提出的方法进行了评估。结果表明，与基于NLTV去噪的方法生成的图像相比，集成RDB进一步增强了基于MI-NLTV去噪的解析重建算法，以实现更高的CBCT图像质量，平均对比度噪声比高15.97%，均方根误差低2.67%，空间不均匀性低0.12%，相关性高1.14%，可检测性指数平均高18.11%。这些定量结果表明，对于低剂量CBCT成像，MI的加入使NLTV比传统的NLM滤波器更稳定、更稳健。此外，尽管低mAs投影采集，但实现临床可接受的CBCT图像质量可以减轻普通在线CBCT成像的负担，提高整个放射治疗过程中的患者安全性。
