[ska]

Worse, there is a whole family of parameters.

It's worth thinking of an MRI as a programmable machine for doing certain types of physics experiments.

Sometimes you have an area of interest, sometimes you don't. A lot of the practical (i.e. clinical level, not research work) on specific areas of interest is still in coil design, since body coils often don't do well.

There are all sorts of things that make it difficult (e.g. imaging is in frequency domain, localizing things with gradients can be time consuming in ways not entirely directly related to clarity, etc.)

This sort of thing is addressing issues that come up with acceleration techniques that rely on redundancy in the sampled space to "cheat" and not capture everything. The obvious concern with a ML approach here is that it may replace something interesting with something more normal.

I'd hate to be the one tasked with V&V for this, honestly.