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A low-cost and shielding-free ultra-low-field brain MRI scanner

nature.com

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Re: A low-cost and shielding-free ultra-low-field brain MRI scanner

#21

Post by cute_boi »

egocodedinsol wrote: post #13

“There are approximately seven scanners per million inhabitants and over 90% are concentrated in high-income countries. We describe an ultra-low-field brain MRI scanner that operates using a standard AC power outlet and is low cost to build.” This is fantastic. What a sentence to get to write.

Yea, the way they turned the bleak situation into something sanguine is fascinating. I still remember how expensive is MRI. I used to earn like $150 usd and the cost was around $200 usd. I hope such inequality shall perish in the future, so people can at least get proper treatment.

Re: A low-cost and shielding-free ultra-low-field brain MRI scanner

#22

Post by scratcheee »

robertlagrant wrote: post #14
scratcheee wrote: post #12

Earlier quoted context omitted.

Exactly my thoughts too. I'm fine with a simple noise-removal pass, but if the AI is context-aware, what's to stop it saying "hmm, this brain would look more like a normal brain if I remove these tumors". Obviously, they'll test for that, but that only handles common cases they concider, it's always going to be a risk for more unusual sceanrios, and the danger with altering the data is that anyone looking at the resu…

> a simple noise-removal pass Even that is inventing data, no?

Yes, but context is key.

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.

Re: A low-cost and shielding-free ultra-low-field brain MRI scanner

#23

Post by rexreed »

tshaddox wrote: post #20
rexreed wrote: post #11

So you're saying we have sufficient trust in the same sort of NN technology that confuses 8's and 0's in OCR text will be used to impute image data which might or might not exist? Sure, NN's are great at "filling in the gaps" and colorizing pictures based on what might be assumed, but when accuracy matters, does this approach truly work? EDIT: I just want to point out that the original subject title of the post on HN…

Do you have any data on the reliability of OCR systems used in production? I don’t have any such data, but given that the USPS was using OCR to sort mail over 50 years ago I would be surprised if these systems aren’t incredibly accurate.

From: https://research.aimultiple.com/ocr-technology/

"There are still no OCR tools that work at human level in most applications"

and also from my personal experience working with this technology every day. There are many more mistakes in OCR even with printed material than might be expected.

There is a major problem with Xerox Scanners and the 8's and 0's issue I reference.

Re: A low-cost and shielding-free ultra-low-field brain MRI scanner

#24

Post by phkahler »

a-dub wrote: post #5

woah. woah. woah. hold on a second here... are we comfortable enough with understanding all of the behavior of deep learning models to where we can confidently put them in the pipeline for diagnostic clinical imaging? 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 convin…

I'm kind of thinking the opposite. Image analysis is where you don't want AI. Noise removal is further upstream (I'm assuming) and if it fails wouldn't it cause significant artifacts (blur for example) in the images?

It would be helpful to see results with and without this correction, or even with varying degrees of it.

Re: A low-cost and shielding-free ultra-low-field brain MRI scanner

#26

Post by VikingCoder »

a-dub wrote: post #5

woah. woah. woah. hold on a second here... are we comfortable enough with understanding all of the behavior of deep learning models to where we can confidently put them in the pipeline for diagnostic clinical imaging? 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 convin…

That's a valid risk.

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.

Re: A low-cost and shielding-free ultra-low-field brain MRI scanner

#27

Post by isoprophlex »

rexreed wrote: post #11

So you're saying we have sufficient trust in the same sort of NN technology that confuses 8's and 0's in OCR text will be used to impute image data which might or might not exist? Sure, NN's are great at "filling in the gaps" and colorizing pictures based on what might be assumed, but when accuracy matters, does this approach truly work? EDIT: I just want to point out that the original subject title of the post on HN…

See other comments. The nn is used to clear up electromagnetic inference as there's no shielding cage. It's not anything lik a superresolution approach on the processed voxel data.

Re: A low-cost and shielding-free ultra-low-field brain MRI scanner

#29

Post by VikingCoder »

MauranKilom wrote: post #6
a-dub wrote: post #5

woah. woah. woah. hold on a second here... are we comfortable enough with understanding all of the behavior of deep learning models to where we can confidently put them in the pipeline for diagnostic clinical imaging? 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 convin…

Agreed, but I believe "using AI for inference from sparse observations" is unfortunately a thing already.

That's not "unfortunate."

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.

Re: A low-cost and shielding-free ultra-low-field brain MRI scanner

#30

Post by rexreed »

isoprophlex wrote: post #27
rexreed wrote: post #11

So you're saying we have sufficient trust in the same sort of NN technology that confuses 8's and 0's in OCR text will be used to impute image data which might or might not exist? Sure, NN's are great at "filling in the gaps" and colorizing pictures based on what might be assumed, but when accuracy matters, does this approach truly work? EDIT: I just want to point out that the original subject title of the post on HN…

See other comments. The nn is used to clear up electromagnetic inference as there's no shielding cage. It's not anything lik a superresolution approach on the processed voxel data.

Ok good clarification as the title of the post seems to imply much more than just fixing interference. As always, the article subject is the hook that gets you in and then you realize it's not as might have been expected.
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