[davidhyde]

Playing devil's advocate here but can't machine learning be used to remove noise rather than add detail? Removing noise would reveal detail hidden in the data kind of like the result you get after applying a spectral filter to a fourier transformed image. For example: https://www.youtube.com/watch?v=s2K1JfNR7Sc

[halpert]

"Removing noise" is equivalent to adding detail.

[aix1]

Personally, I am not even sure that "ML vs non-ML" is a useful dichotomy. If method A can be rigorously demonstrated[*] to produce superior results to method B, does it even matter whether mathematically it is constructed out of fast Fourier transforms, Metz filters, layers of convolutions or whatever else?

[*] For example, by measuring the quality of reconstruction of a known image (e.g. a real or digital phantom) or, in the ideal world, by evaluating clinical outcomes.

[olliej]

The problem would be blurring or denoising meaningful information, but I don't know enough to say they don't do any denoising. I can imagine the data being noisy, but perhaps it isn't? shrug :D

[notarandomer]

Yes, accelerating MRI acquisition increases noise in the images as well as introducing aliasing artifacts. I think the issue is that some modern reconstruction methods (e.g. compressed sensing that was mentioned in the article) produce predictable biases, e.g. adding a risk of smoothing out details, but for ML we don't always know in what way it will bias the reconstructed image (add details, remove important information...), and I think that is what people often worry about.