Limiting the potential risks associated with radiation exposure is critically important when obtaining a diagnostic image. However, lowering the level of radiation may cause excessive noise and artifacts in computed tomography (CT) scans. In this study, we implemented and tested the performance of patch-based and block-based REDCNN models and revealed that a 3D kernel is efficient in removing 3D noise and artifacts. Additionally, we applied a 3D bilateral filter and a 2D-based Landweber iteration method to remove any remaining noise and to prevent the edges from blurring, which are limitations of a deep learning-based noise reduction system. For the 2D-based Landweber iteration, we examined the requisite step size and the number of iterations. The representative CT noise and artifacts, which were Gaussian noise and view aliasing artifacts, respectively, were simulated on XCAT and reproduced in vivo to verify that the proposed method could be used in an analogous clinical setting. Lastly, the performance of the proposed algorithm was evaluated on in vivo data with real low-dose noise. Our proposed method effectively suppressed complex noise without losing diagnostic features in both the simulation study and experimental evaluation. Furthermore, for the simulation study, we adopted a numerical observer model to evaluate the structural fidelity of the image quality more appropriately than existing image quality assessment methods.

在获取诊断图像时，限制与辐射暴露相关的潜在风险至关重要。但是，降低辐射水平可能会导致计算机断层扫描 (CT) 扫描中出现过多的噪音和伪影。在这项研究中，我们实施并测试了基于补丁和基于块的 REDCNN 模型的性能，并表明 3D 内核在去除 3D 噪声和伪影方面是有效的。此外，我们应用了 3D 双边滤波器和基于 2D 的 Landweber 迭代方法来去除任何剩余的噪声并防止边缘模糊，这是基于深度学习的降噪系统的局限性。对于基于 2D 的 Landweber 迭代，我们检查了必要的步长和迭代次数。具有代表性的 CT 噪声和伪影，即高斯噪声和视图混叠伪影，分别在 XCAT 上模拟并在体内复制，以验证所提出的方法可用于类似的临床环境。最后，在具有真实低剂量噪声的体内数据上评估了所提出算法的性能。我们提出的方法在模拟研究和实验评估中都有效地抑制了复杂噪声，而不会丢失诊断特征。此外，在模拟研究中，我们采用了数值观察模型来评估图像质量的结构保真度，比现有的图像质量评估方法更合适。我们提出的方法在模拟研究和实验评估中都有效地抑制了复杂噪声，而不会丢失诊断特征。此外，在模拟研究中，我们采用了数值观察模型来评估图像质量的结构保真度，比现有的图像质量评估方法更合适。我们提出的方法在模拟研究和实验评估中都有效地抑制了复杂噪声，而不会丢失诊断特征。此外，在模拟研究中，我们采用了数值观察模型来评估图像质量的结构保真度，比现有的图像质量评估方法更合适。