The most famous would probably be the IG Nobel winning study that detected brain activity in a store-bought salmon:
https://blogs.scientificamerican.com/scicurious-brain/ignobe...
https://www.discovermagazine.com/mind/fmri-gets-slap-in-the-...
Later studies called into question the results of between 10% and 40% of historic fMRI studies:
https://blogs.warwick.ac.uk/nichols/entry/bibliometrics_of_c...
MRI's becoming commonplace, even if it were every 3 years instead of annually would be a useful tool to improve health outcomes across the board.
A store-bought dead salmon.
I am assuming that most salmons bought in stores are dead but that particular detail is rather relevant here.
Also that had me laughing, what a great move.
The "images" that are presented in fMRI studies and that contain false positives are representing results of statistical tests (t-values, and f-values after correction) not the contents of voxels. So the false positive rate of an fMRI has very little to do with the accuracy of a voxel's content in a structural MRI.
whether you call it "SnR improvement" or "additive noise cancellation", it is undeniably adulteration of the signal.
looking at the supplementary information, it looks like this paper was reviewed by mr-physicists. i think it also should have been reviewed by ml experts as well.
1. Measure outside interference sources 2. Measure MRI of "nothing" 3. Use ML to estimate f(interference) = noise 4. Subtract estimated noise from signal
So the noise removal process has no awareness of brains, skeletons, etc.
"To tackle the EMI signals from the external environments and internal low-cost electronics during scanning, we developed a deep learning driven EMI cancellation scheme"
So it's kind of using deep learning to improve the SnR in the RF reception. Of course this could theoretically also lead to "fantasy voxels" but due to the nature of MRI decoding, I'm willing to guess that bad predictions of the EMI interference will not show up as unnoticeable alterations of realistic tissue imaging but rather as artefacts all over the volume, like you normally see in clinical MRIs that weren't taken 100% optimally.