Computational filtering techniques are difficult for a good reason. In the case of CT, high density objects like metal implants produce beam hardening by preventing the low energy photons from reaching the detector. With adversarial training, you can train a network to recognize and remove the artifacts, but you won't be able to reconstruct structures for which there is no physical measurement.
There were similar discussions a few year ago when deep learning was not commonly used yet and compressed sensing was the hot topic of the moment. It can reconstruct MRI or CT images from limited data (and thus allows for quick MR scans or low dose CT) but you have to satisfy a sparsity condition that is seldom granted. There are a few use cases (like MR angiography) where the data is sparse enough and compressed sensing works great.
For deep learning techniques, you need to be very cautious about which structures your network may remove or introduce.
There were similar discussions a few year ago when deep learning was not commonly used yet and compressed sensing was the hot topic of the moment. It can reconstruct MRI or CT images from limited data (and thus allows for quick MR scans or low dose CT) but you have to satisfy a sparsity condition that is seldom granted. There are a few use cases (like MR angiography) where the data is sparse enough and compressed sensing works great.
For deep learning techniques, you need to be very cautious about which structures your network may remove or introduce.