论文标题
CT金属伪像还原的自适应卷积词典网络
Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction
论文作者
论文摘要
受深神经网络的巨大成功的启发，基于学习的方法在计算机断层扫描（CT）图像中获得了有希望的金属伪像（MAR）的表现。但是，大多数现有方法更加强调建模并嵌入本特定的MAR任务的内在先验知识中。在这个问题上，我们提出了一个自适应卷积词典网络（ACDNET），该网络利用基于模型的方法和基于学习的方法。具体而言，我们探讨了金属伪像的先前结构，例如非本地重复条纹模式，并将它们作为显式加权卷积词典模型进行编码。然后，仔细设计了一种简单的算法来解决该模型。通过将所提出算法的每个迭代取代将其展开到网络模块中，我们将先前的结构明确嵌入到一个深网中，\ emph {i.e。，}对MAR任务的明确解释性。此外，我们的ACDNET可以通过训练数据自动学习无伪影CT图像的先验，并根据其内容自适应地调整每个输入CT图像的表示内核。因此，我们的方法继承了基于模型的方法的明确解释性，并保持了基于学习的方法的强大表示能力。在合成和临床数据集上执行的综合实验在有效性和模型概括方面表明了我们的ACDNET的优越性。 {\ color {blue} {\ textit {代码可在{\ url {https://github.com/hongwang01/acdnet}}}}}}}}
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}.}}}}
