> We conducted imaging on healthy volunteers, capturing brain, spine, abdomen, lung, musculoskeletal, and cardiac images. Deep learning signal prediction effectively eliminated EMI signals, enabling clear imaging without shielding.<p>So essentially, the neural net was trained to what a healthy MRI looks like and would, when exposed to abnormal structures, correct them away as EMI noise leading to wrong diagnostics?<p>I won't be very dismissive of this approach and probably deep learning has a strong role to play in improving medical imaging. But this paper is far, far from sufficient to prove it. At a minimum, it would require mixed healthy / abnormal patients with particularities that don't exist in the training set, and each diagnostic reconfirmed later on a high resolution machine. You need to actually prove the algorithm does not distort the data, because an MRI that hallucinates a healthy patient is much more dangerous than no MRI at all.