| Current MR physicist / data scientist here. There seems to be a lot of misapprehension in this thread. First, this work is about taking data in the sensor domain ("k-space") and reconstructing it into an image. Doing this with partial k-space data and hand-coded heuristics is a completely standard part of the MRI research agenda and has been for quite some time. See, for example, http://mriquestions.com/k-space-trajectories.html. Further, several of these techniques have already made it into routine clinical work, and this acquisition-side stuff generally happens before the radiologist even sees the image (reliable acquisition is in the interaction of radiographer with the scanner manufacturer's software). There's also various claims here that seem to imply learned reconstruction inherently implies the risk of hallucinations without recourse. Naturally, one should be careful about this, but it's just a matter of careful cross validation: hold out examples of abnormal anatomy for the test set. There's other ways to attack this problem too: training can be done partly or mostly on synthetic data because we have reasonably good forward models of the physics. In this case, one could choose a wide variety of arbitrary synthetic anatomies during training, to further ally the fear of always hallucinating the "typical human brain" from any scan. Slow acquisition and image artifacts in MRI are a fact of life for people in the field and I believe there's huge scope for improvement if we had more intelligent reconstruction and acquisition. Ideally the reconstruction would feed dynamically back into the acquisition to gather more context as necessary; the MR machine is, after all, one giant programmable physics experiment. This is already done in a limited way, but in what I've seen it relies on a lot of hand-coded heuristics. And guess what's the logical step after hand-coded heuristics? Yes, learned models where you objectively optimize for a final result, rather than hand-coding based on a few examples. Final note - publicly releasing human data is a massive effort in data cleaning and careful anonymization. Not to mention that the acquisition of each sample is extraordinarily expensive. So bravo to these guys for going to the effort. |