"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.
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.
Reminds me of https://en.wikipedia.org/wiki/Xerox#Character_substitution_b... which was _so much_ worse than the equivilent OCR bug because it occured at the image level, where everyone expects errors to to produce noise, not contextly sensible and sharp _but wrong_ characters.
EDIT: based on other comments below, this is thankfully not the case, the AI just understands noise, it doesn't try to "fill in the blanks" based on how brains are supposed to look.
Even that is inventing data, no?
Denoising can on average improve the result, but sometimes it will be wrong.
Spotting when it goes wrong is potentially a difficult task, but generally the difficulty scales pretty clearly with the difficulty of understanding the original image anyway. If you can't spot when a denoising filter has screwed up, chances are you wouldn't have spotted anything interesting in the original image anyway.
But once an AI is context-aware things get way more complicated - it will try very hard to produce an image that doesn't _look_ wrong. Even if it goes wrong, it can go wrong and still succeed in managing to make an image that looks correct, it just no longer matches the real brain that was scanned. Perhaps it decided a tumor was just a smudge on the lense, and invented some brain to go behind it. An operator expecting to see brain and seeing brain wont think anything of it. When the patient dies, they may look back and say "wow, that tumor didn't exist at all just 3 days before! that should be impossible!".
tldr: Having an ai that might make mistakes is one thing, having an ai that can just invent exactly the data everyone is expecting to see is dangerous.
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.
You're asking a cost-benefit question.
The cost of an invalid diagnosis is indeed high.
The cost of no diagnosis at all is also high.
This device will not replace the MR at your local hospital. It will be the first MR device in hospitals that have never had one before.
I’ve scanned logs for forestry managers who want to look at something in their trees.
I’ve scanned old hearts that have been sitting in formalin for decades.
All are probably better at higher field strength but maybe some of that can be compensated for by scanning for longer? A log isn’t going to move, and a dead fish scan is likely only limited by the time it takes for it to rot.
I’d be scared of scanning unknown things, it might be a low field MRI, but it’s still a big magnet.
Given that my little podunk hospital in the midwest seems to have roughly 5x the worldwide average number of MRI machines, totally agree.
https://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres...
I have seen an alarming number of talks where someone proposes to algorithmically add Gado contrast or turn a T1 into a T2 image. In a few very specific contexts, this makes sense (e.g., aligning a T1 taken in one session with a T2 taken in another). Otherwise though, it seems dangerous to mistake a "real" image with the expected image given another one.
It would be helpful to see results with and without this correction, or even with varying degrees of it.
Sparse observations save lives. A quicker MR. Less X-Ray exposure.
It's totally valid to worry about validation, but to the degree you can validate image processing algorithms of any kind - AI or otherwise - they absolutely save lives.
Image processing saves lives.
i'm okay with using them for image analysis, but denoising and other image production tasks seems dangerous. how do you know what you're looking at is real as opposed to something that just looks convincing? (like deep neural nets are famous for producing)