Inspired by the great success of deep neural networks, learning-based methods have gained promising performances for metal artifact reduction (MAR) in computed tomography (CT) images. However, most of the existing approaches put less emphasis on modelling and embedding the intrinsic prior knowledge underlying this specific MAR task into their network designs. Against this issue, we propose an adaptive convolutional dictionary network (ACDNet), which leverages both model-based and learning-based methods. Specifically, we explore the prior structures of metal artifacts, e.g., non-local repetitive streaking patterns, and encode them as an explicit weighted convolutional dictionary model. Then, a simple-yet-effective algorithm is carefully designed to solve the model. By unfolding every iterative substep of the proposed algorithm into a network module, we explicitly embed the prior structure into a deep network, \emph{i.e.,} a clear interpretability for the MAR task. Furthermore, our ACDNet can automatically learn the prior for artifact-free CT images via training data and adaptively adjust the representation kernels for each input CT image based on its content. Hence, our method inherits the clear interpretability of model-based methods and maintains the powerful representation ability of learning-based methods. Comprehensive experiments executed on synthetic and clinical datasets show the superiority of our ACDNet in terms of effectiveness and model generalization. {\color{blue}{{\textit{Code is available at {\url{https://github.com/hongwang01/ACDNet}}.}}}}

翻译：在深层神经网络的巨大成功激励下,基于学习的方法在计算断层成像图像中的金属工艺品减少(MAR)方面获得了有希望的绩效。然而,大多数现有方法不那么强调建模和将这一具体的MAR任务内在知识嵌入其网络设计。针对这一问题,我们提议建立一个适应性综合字典网络(ACDNet),利用基于模型和基于学习的方法。具体地说,我们探索金属工艺品先前的结构,例如非本地重复的立体模式,并将其编码为明确的加权古典字典模型模型。然后,一个简单有效的算法经过仔细设计,以解决模型。通过将拟议的算法的每一个迭代子步都引入网络模块,我们明确地将先前的结构嵌入一个深层网络,\emph{i{e.e.}为MAR任务提供明确的解释。此外,我们的ACDNet模型可以通过培训数据和调整基于可调整的基于可调控性缩缩略图的缩缩缩缩缩略图框,在内容上保持了模型的精确性能度。我们采用的方法,在Ablusblicaloralalalalalalalexal exalisal exismal exal exal exismation ex