Metal artifact reduction (MAR) is important for clinical diagnosis with CT images. The existing state-of-the-art deep learning methods usually suppress metal artifacts in sinogram or image domains or both. However, their performance is limited by the inherent characteristics of the two domains, i.e., the errors introduced by local manipulations in the sinogram domain would propagate throughout the whole image during backprojection and lead to serious secondary artifacts, while it is difficult to distinguish artifacts from actual image features in the image domain. To alleviate these limitations, this study analyzes the desirable properties of wavelet transform in-depth and proposes to perform MAR in the wavelet domain. First, wavelet transform yields components that possess spatial correspondence with the image, thereby preventing the spread of local errors to avoid secondary artifacts. Second, using wavelet transform could facilitate identification of artifacts from image since metal artifacts are mainly high-frequency signals. Taking these advantages of the wavelet transform, this paper decomposes an image into multiple wavelet components and introduces multi-perspective regularizations into the proposed MAR model. To improve the transparency and validity of the model, all the modules in the proposed MAR model are designed to reflect their mathematical meanings. In addition, an adaptive wavelet module is also utilized to enhance the flexibility of the model. To optimize the model, an iterative algorithm is developed. The evaluation on both synthetic and real clinical datasets consistently confirms the superior performance of the proposed method over the competing methods.

中文翻译：

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使用多视角正则化进行自适应和迭代学习，以减少金属伪影


金属伪影减少 （MAR） 对于使用 CT 图像进行临床诊断很重要。现有的最先进的深度学习方法通常会抑制正弦图和/或图像域中的金属伪影。然而，它们的性能受到两个域固有特性的限制，即在反向投影过程中，正弦域局部操作引入的误差会传播到整个图像中，导致严重的二次伪影，而在图像域中很难区分伪影和实际图像特征。为了减轻这些限制，本研究深入分析了小波变换的理想性质，并建议在小波域中进行 MAR。首先，小波变换产生与图像具有空间对应关系的组件，从而防止局部误差的传播，以避免二次伪影。其次，使用小波变换可以促进从图像中识别伪影，因为金属伪影主要是高频信号。利用小波变换的这些优势，本文将图像分解为多个小波分量，并在所提出的 MAR 模型中引入了多视角正则化。为了提高模型的透明度和有效性，所提出的 MAR 模型中的所有模块都旨在反映其数学含义。此外，还利用了自适应小波模块来增强模型的灵活性。为了优化模型，开发了一种迭代算法。对合成和真实临床数据集的评估一致证实了所提出的方法优于竞争方法的性能。