most places call this type of thing "clinical decision support". nobody in their right mind wants to remove the human doctors from the process... yet.
There is just too much redundancy in MRI data, and initiatives such as FastMRI are fundamental for us to learn what the limits are of feasible acceleration. Also, some MRI scans take forever and cannot be used in vulnerable populations because of, e.g., breath holds, the need to stand still, etc. The image quality, perhaps counter-intuitively, in some situations improves with acceleration.
It’s interesting research for sure. I hope it stays far away from actual clinical use for a while, for the reasons I highlighted. I’d like to see convnets work alongside radiologists for a while and prove robustness to dcanner changes in the wild before we start shoving them deep in the stack where radiologists can’t review what’s happening.
Such that i am not sure the risks are the same as say convolution nets reconstructing large brain structures.
K-space data is also saved for reconstructions and processing later on, though everyone prefers to avoid that as it’s horrible and lots of storage is required.
I’ve also worked at a university site where the raw data was collected and used on a daily basis, but that is presumably less common.
Rather than dreaming about that, the focus should definitely be on "clinical decision support", i.e. "something useful that will save a radiologist some time and won't just get in the way". Not too many examples of that exist right now. Even speech-to-text is not a solved problem in their domain.