If you can get by with sampling only a subset of this space and approximate/reconstruct the rest with a mathematical model, yet yield reasonable accuracy (wrt diagnosis or other criterion) relative to full sampling, the MRI session will be a lot faster because you don't need to acquire all the data you did before.
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