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This is a typical misapplication of machine learning. It's important to realize what information can possibly be learned by a trained network. In this case, the
by lvs 7y ago
This is a typical misapplication of machine learning. It's important to realize what information can possibly be learned by a trained network. In this case, the only thing that can be learned is which components of Fourier space can be ignored for a given imaging problem. Such a question is far more rationally addressed by a deterministic algorithm, if the goal is to speed up acquisition for any specific anatomy. But to generally assume that an imaging protocol can ignore parts of frequency/phase space without generating artifacts is not only wrong, but very dangerous for patients. I can easily play with Fourier space and generate the appearance of pathological conditions that don't exist -- or disappear ones that do! Not good!
- nwah1 7y agoIf it is so easy, as you say, then if you can release examples where you do that using their training data, then you would probably get some acclaim, and I personally would applaud your effort.
- lvs 7y agoI'll spend time on this if I can, but for the moment just imagine that in frequency space, where MRI acquisition occurs, all I need to do to blank out an imaging anomaly is discard spatial frequencies that correspond to the principal spatial frequencies of the anomaly. This is analogous to blurring your speech by notch filtering the principal frequencies of your voice. With appropriate filtering, I can make your voice nearly indistinct from background noise, so that it appears not be there.