There is no unadulterated result, you are doing less sampling and relying on ML to fill the gaps. So you either have the ML reconstructed result or a subsampled MRI.
Healthcare is an area where we need good and clean data as much as possible, let’s use ML reconstruction somewhere else.
A lot of the problem comes from the use of generative neural networks. If the prior is that the reconstructed images should "look" a certain way, then the algorithm will favor that. Some of our colleagues did early work with DL and got scared off of generative models due to finding issues with nonphysical results (read: broken layers of cortex in the brain, completely non-physical anatomy) that these models can generate from the undersampled raw data.
That said, there are other great ways to incorporate DL into MRI other than recon. I'm more interested in the use of DL for image segmentation, feature detection, potentially denoising, or other techniques on the image processing side. Those make a lot more sense as "top down" tasks that are well suited for neural networks.
If your question is literally “you understand how it works?” the answer is yes, I do.
If your question is more nuanced to mean “do you really really know how it works, meaning you could work on it tomorrow?” the answer is no, it is not my field.
Could please share a link to the work you are referring to? I would really appreciate it (not ironically, it would be truly appreciated).
I know we can work around subsampling, actually we have always been very good at it since our sampling data back in the day was way smaller than what we refer as subsampled today.
MRI is completely adulterated at every stage. Algorithms and filters make the final result palatable. The raw data is a k-space data file. It’s not really human readable (though you can spot noise spikes etc).
Healthcare is an area where we need good and clean data as much as possible, let’s use ML reconstruction somewhere else.