论文标题
CT金属伪像学习的方向共享卷积表示
Orientation-Shared Convolution Representation for CT Metal Artifact Learning
论文作者
论文摘要
在X射线计算机断层扫描（CT）扫描过程中，带有患者的金属植入物通常会导致捕获的CT图像中的不良伪影，然后损害临床治疗。在这种金属伪影（MAR）任务下，现有的基于深度学习的方法已获得了有希望的重建性能。然而，仍然有一些进一步改善MAR性能和概括能力的空间，因为这项特定任务的基础知识尚未得到充分利用。在此，在本文中，我们仔细分析了金属伪像的特征，并提出了定向共享的卷积表示策略，以适应伪影的物理先验结构，即旋转对称的条纹模式。所提出的方法在理性上采用了基于傅立叶 - 曝光的滤波器滤波器参数化，在伪影建模中可以更好地将伪影与解剖组织分开，并提高模型的推广性。在合成和临床数据集上执行的全面实验显示了我们方法在当前代表性MAR方法之外的详细保存的优越性。代码将在\ url {https://github.com/hongwang01/coscnet}可用
During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts in the captured CT images and then impair the clinical treatment. Against this metal artifact reduction (MAR) task, the existing deep-learning-based methods have gained promising reconstruction performance. Nevertheless, there is still some room for further improvement of MAR performance and generalization ability, since some important prior knowledge underlying this specific task has not been fully exploited. Hereby, in this paper, we carefully analyze the characteristics of metal artifacts and propose an orientation-shared convolution representation strategy to adapt the physical prior structures of artifacts, i.e., rotationally symmetrical streaking patterns. The proposed method rationally adopts Fourier-series-expansion-based filter parametrization in artifact modeling, which can better separate artifacts from anatomical tissues and boost the model generalizability. Comprehensive experiments executed on synthesized and clinical datasets show the superiority of our method in detail preservation beyond the current representative MAR methods. Code will be available at \url{https://github.com/hongwang01/OSCNet}
