Metal Artifact Reduction In Cone-Beam Extremity Images Using Gated Convolutions

Quality of cone-beam computed tomography (CBCT) images are marred by artifacts in the presence of metallic implants. Metal artifact correction is a challenging problem in CBCT scanning especially for large metallic objects. The appearance of artifacts also change greatly with the body part being scanned. Metal artifacts are more pronounced in orthopedic imaging, when metals are in close proximity of other high density materials, such as bones. Recently introduced mask incorporating deep learning networks for metal inpainting showed improvements over classical methods in CBCT image quality. However, generalization of results for more than one body part is still not investigated. We investigate, the use of gated convolutions for mask guidance inpainting to improve the filling of the corrupt metal area in projection domain. The neural network was trained with eight clinical metal affected datasets by incorporating data augmentation techniques. In the end, we validate our method on six clinical datasets. Our method shows promising results both in projections and reconstructed images.