Tang Shaojie, Liu Jin, Li Guo, Qiao Zhiwei, Chen Yang, Mou Xuanqin
School of Automation, Xi'an University of Posts and Telecommunications, Xi'an, China.
College of Computer and Information, Anhui Polytechnic University, Wuhu, China.
J Xray Sci Technol. 2025 Sep;33(5):959-977. doi: 10.1177/08953996251337889. Epub 2025 Jul 15.
Suppressing noise can effectively promote image quality and save radiation dose in clinical imaging with x-ray computed tomography (CT). To date, numerous statistical noise reduction approaches have ever been proposed in image domain, projection domain or both domains. Especially, a multiscale decomposition strategy can be exploited to enhance the performance of noise suppression while preserving image sharpness. Recognizing the inherent advantage of noise suppression in the projection domain, we have previously proposed a projection domain multiscale penalized weighted least squares (PWLS) method for fan-beam CT imaging, wherein the sampling intervals are explicitly taken into account for the possible variation of sampling rates. In this work, we extend our previous method into cone-beam (CB) CT imaging, which is more relevant to practical imaging applications.
The projection domain multiscale PWLS method is derived for CBCT imaging by converting an isotropic diffusion partial differential equation (PDE) in the three-dimensional (3D) image domain into its counterpart in the CB projection domain. With adoption of the Markov random field (MRF) objective function, the CB projection domain multiscale PWLS method suppresses noise at each scale. The performance of the proposed method for statistical noise reduction in CBCT imaging is experimentally evaluated and verified using the projection data acquired by an actual micro-CT scanner.
The preliminary result shows that the proposed CB projection domain multiscale PWLS method outperforms the CB projection domain single-scale PWLS, the 3D image domain discriminative feature representation (DFR), and the 3D image domain multiscale nonlinear diffusion methods in noise reduction. Moreover, the proposed method can preserve image sharpness effectively while avoiding generation of novel artifacts.
Since the sampling intervals are explicitly taken into account in the projection domain multiscale decomposition, the proposed method would be beneficial to advanced applications where the CBCT imaging is employed and the sampling rates vary.
在X射线计算机断层扫描（CT）临床成像中，抑制噪声可有效提高图像质量并降低辐射剂量。迄今为止，在图像域、投影域或两个域中都提出了许多统计降噪方法。特别是，可以利用多尺度分解策略来提高噪声抑制性能，同时保持图像清晰度。认识到投影域中噪声抑制的固有优势，我们之前提出了一种用于扇束CT成像的投影域多尺度惩罚加权最小二乘法（PWLS），其中明确考虑了采样间隔以应对采样率的可能变化。在这项工作中，我们将之前的方法扩展到锥束（CB）CT成像，这与实际成像应用更相关。
通过将三维（3D）图像域中的各向同性扩散偏微分方程（PDE）转换为CB投影域中的对应方程，推导出用于CBCT成像的投影域多尺度PWLS方法。采用马尔可夫随机场（MRF）目标函数，CB投影域多尺度PWLS方法在每个尺度上抑制噪声。使用实际微型CT扫描仪获取的投影数据，对所提出的CBCT成像统计降噪方法的性能进行了实验评估和验证。
初步结果表明，所提出的CB投影域多尺度PWLS方法在降噪方面优于CB投影域单尺度PWLS、3D图像域判别特征表示（DFR）和3D图像域多尺度非线性扩散方法。此外，该方法可以有效保持图像清晰度，同时避免产生新的伪影。
由于在投影域多尺度分解中明确考虑了采样间隔，所提出的方法将有利于采用CBCT成像且采样率变化的先进应用。
