I'm not clear which scenario you are alluding to. It the expectation figuring out only how to sparsely sample the same area or how to quickly sample the larger area so that a detailed scan can be taken after that?
It is known that we can reconstruct MR images at full fidelity- with no loss of information- by randomly sampling "k-space" at something like 10% of the usual sampling rate. This leads to much faster acquisitions. I believe Siemens has a product based on this technology that is currently going to market- https://usa.healthcare.siemens.com/magnetic-resonance-imagin...
One issue, though, is that truly random sampling isn't great from a practical point of view. Sampling patterns are constrained by other equipment considerations. There is also the issue of noise.
It is known that we can reconstruct MR images at full fidelity- with no loss of information- by randomly sampling "k-space" at something like 10% of the usual sampling rate. This leads to much faster acquisitions. I believe Siemens has a product based on this technology that is currently going to market- https://usa.healthcare.siemens.com/magnetic-resonance-imagin...
One issue, though, is that truly random sampling isn't great from a practical point of view. Sampling patterns are constrained by other equipment considerations. There is also the issue of noise.
Machine learning for MR (and CT, and PET/SPECT, and...) is an active area of research, eg https://arxiv.org/pdf/1705.06869.pdf