[notarandomer]

I am surprised you are so certain in this statement. There is always a tradeoff between scan time and image quality. Clinical scans often have thick slices to keep scan times reasonable. Using advanced reconstruction methods, e.g. ML, you can get thinner slices in the same scan time. How would you balance the benefit of getting higher resolution in the same time as a standard lower resolution scan time if the higher resolution scan was regularized with a neural network? The doctor might miss small tumors due to low resolution too. I understand your concern, but I wouldn't dismiss it so outright.

Note, I am biased because I research MRI acquisition and reconstruction methods and I am rolling out trials of fast MRI methods (that use some ML in the reconstruction) to find out how robust the methods actually are in practice.

[01acheru]

Well, I really hope you succeed on your experiments, but right now it seems to me that ML introduces an error factor we still cannot precisely account for.

It looks like you are proposing some kind of mixed approach, not a simple “less data, faster scan, ML to the rescue”. I understand how MRI works but you are surely and obviously much more knowledgeable than me, so I simply wish you luck!

My problem with the article comes from reading that someone telling about using generative models on health data, I don’t think is time for this yet.

[notarandomer]

Thanks! I agree taht generative models on their own are definitely more risky than methods that combine ML for regularization along with data consistency terms that force the reconstructions to be consistent with the acquired data.