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Comment by l33tman | Hacker News Reader

This project doesn't use AI to improve the image, they use it to estimate the EMI noise from the surroundings. So they're not "filling in the gaps" in the actual resulting 3D voxel volume with fantasy voxels (which I hope will never ever fly in a clinical setting).

"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.

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I'm glad that this is the approach that they are taking. There have been plenty of issues with fMRI false positives due to misconfigured software.

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...

https://www.pnas.org/content/113/28/7900

> The most famous would probably be the IG Nobel winning study that detected brain activity in a store-bought salmon:

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.

Not sure the dead salmon is relevant. That paper is focused on false discovery in FUNCTIONAL MRI. Different can of fish. Most clinical work is structural MRI.

The chances of finding brain activity in a dead salmon are a bit lower than finding it in one that is alive.

Those false positives are because fmri runs countless statistical tests and the earlier "misconfigured software" wasn't running stringent enough multiple comparisons corrections. Basically the same issue in the classic "jelly bean causes acne" xkcd (https://xkcd.com/882/). The exact number depends on voxel size, temporal resolution, and experimental condition but is somewhere close to tens of thousands of tests.

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.

I prefer to think of that study as evidence for life after death.

Thanks for the kind words. I am the first author of the "Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: An argument for multiple comparisons correction" paper. Happy to take any questions here. A link to the original poster: http://prefrontal.org/files/posters/Bennett-Salmon-2009.pdf

It would be nice if the cost of an MRI was so low you would typically get a cheap one as part of your yearly physical and if anything popped up they could do it again in an expensive, high powered one to verify.

I've scanned about 300 people as part of my research career. The director of the imaging center reviewed every anatomical scan. From that group of 300 we informed about three people that they had an anomaly which should be examined by a doctor.

Yeah, that's what I'm talking about. Sure, it was 1% that needed further validation but that 1% is so much cheaper and easier to treat when its caught early vs. later on when it's noticed by the patient.

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.

You run into the most entertaining people here. Got any good fish recipes?

Marc Abrahams, organizer of the Ig Nobels, asked us for a salmon recipe to include in a cookbook they were publishing. We sent in a single page recipe for how to cook a salmon in an MRI scanner by overriding the safety protocols. That was fun to write.

https://www.amazon.com/Ig-Nobel-Cookbook-1/dp/1939385164

the primary innovation is using deep learning to denoise the signal and the cost savings derived from being able to use a noisier signal.

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.

Sure, but it's not like it has the potential for overfitting. From my layman's understanding, the process is this:

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.

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