Clinical evidence has shown that rib-suppressed chest X-rays (CXRs) can improve the reliability of pulmonary disease diagnosis.
More importantly, we show that using such a sinogram extrapolation module significantly improves the generalization capability of the model on unseen datasets (e. g., COVID-19 and LIDC datasets) when compared to existing approaches.
Recently, both supervised and unsupervised deep learning methods have been widely applied on the CT metal artifact reduction (MAR) task.
Metal artifact reduction (MAR) in computed tomography (CT) is a notoriously challenging task because the artifacts are structured and non-local in the image domain.
In contrast, there exists a wealth of artifact-free, high quality CT images with vertebra annotations.