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

Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction