Orientation-Shared Convolution Representation for CT Metal Artifact Learning
This work addresses metal artifacts in CT scans, which impair clinical treatment, by introducing a novel method that improves performance and generalization, though it appears incremental in the context of deep-learning-based MAR tasks.
The paper tackles metal artifact reduction in CT images by proposing an orientation-shared convolution representation that adapts to rotationally symmetrical streaking patterns, achieving superior detail preservation compared to current methods on synthesized and clinical datasets.
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}