Comment by elektropionir

It is just weird that papers like this can be published. "Deep learning signal prediction effectively eliminated EMI signals, enabling clear imaging without shielding." - this means that they have found a way to remove random noise, which if true, should be the truly revolutionary claim in this paper. If the "EMI" is not random you can just filter it so you don't need what they are doing. If it isn't random, whatever they are doing can "predict" the noise, they even use the word in that sentence. They are claiming that they can replace physical filtering of noise before it corrupts the signal (shielding) with software "removal" of noise after it has already corrupted the signal. This is simply not possible without loss of information (i.e. resolution). The images that they get from standard Fourier Transform reconstruction are still pretty noisy so on top they "enhance" the reconstruction by running it through a neural net. At that point they don't need the signal - just tell the network what you want to see. The fact that there are no validation scans using known phantoms is telling.