This is already theoretically a bit of a problem with conventional acceleration, which is arguably manageable through understanding of the physics.
I agree the validation and regulatory path is really problematic for less common presentations.
It's probably more interesting to use for artifact detection / QC issues, especially when you do it quickly enough to initiate a re-scan. More problematically but still interesting to do artifact removal. Acceleration is tricky though, for the reasons you mention.
The theoretical problems with deep convnets are far worse than with traditional reconstruction algorithms. They have a far greater capacity to hallucinate or have unexpected behavior on new inputs (see GANS, or the imagenet-c dataset where by simply adding a little motion blur they nuked resnet performance)
Got it! I’ve heard points similar to yours raised in defense of these sorts of projects. “What about sparse reconstruction why aren’t you worried about that”
I agree the validation and regulatory path is really problematic for less common presentations.
It's probably more interesting to use for artifact detection / QC issues, especially when you do it quickly enough to initiate a re-scan. More problematically but still interesting to do artifact removal. Acceleration is tricky though, for the reasons you mention.