Low-dimensional manifold constrained disentanglement network for metal artifact reduction in CT images
Commonly implanted medical devices containing metal parts (i.e., dental fillings, coils, hip replacements) generate streaks in computed tomography (CT) images, thereby impeding diagnosis and interfering with radiation therapy planning. Inventors at RPI created a novel technique to boost the efficacy of neural networks for metal artifact reduction (MAR) in CT images. Currently, deep neural network-based techniques need to be trained on synthetic, paired images. Unfortunately, these images may not accurately reflect clinical reality and technical factors.