6 ms·

Whole-body magnetic resonance imaging at 0.05 Tesla

187 points by Jimega36 2y ago

parpfish 2y ago
> Each protocol was designed to have a scan time of 8 minutes or less with an image resolution of approximately 2×2×8 mm³ very cool, but is it clinically useful if one edge of your voxel is 8mm?
- hesdeadjim 2y ago
  Easier to scale up than down once you have a starting point like this.
- nullc 2y ago
  8mm slice thickness isn't particularly at odds with what is commonly done on commercial machines, though usually there is a second transverse scan (which can't be readily fused due to patient movement). But even if it were, plenty of interesting structures are many centimeters in size, a thousand fold decrease in costs from eliminating cryogenic / high power magnets could be very useful.
  - parpfish 2y ago
    the structures are many centimeters, but I assume that the sort of anomalies you'd be looking for in a clinical scan aren't going to be that large. if you had a fracture/tumor/damage-of-some-type that's small enough to fit between those slices and you didn't get the slices lined up just right the scan would miss it, no?
xyst 2y ago
“Tesla” is a unit of measure for magnetic strength; and not the car manufacturer.
- throwup238 2y ago
  I figured they were using 1/20th of Nikola Tesla's cadaver. That's the only logical interpretation of that headline.
  - xyst 2y ago
    lmao, same bro. I reading the paper and only then I discovered it’s about the viability of a low powered MRI machine for diagnostic imaging. Particularly useful in poorer countries.
  - hinkley 2y ago
    The device is actually designed to scan 20 people at the same time. It’s cheap because you get a bulk discount on your scans.
Jimega36 2y ago
"The lower-power machine was much cheaper to manufacture and operate, more comfortable and less noisy for patients, and the final images after computational processing were as clear and detailed as those obtained by the high-power devices currently used in the clinical setting."
- tkzed49 2y ago
  300-1800W power draw seems impressive! It looks like standard machines are using something on the order of 25kW while scanning, which certainly sounds prohibitive for less developed infrastructure.
  - ChrisMarshallNY 2y ago
    Also, they need to keep vats of liquid helium around. Difficult stuff to store. I knew they needed cold gas, but liquid helium is crazy.
    - jahnu 2y ago
      There was some buzz years ago about using liquid nitrogen instead but I don’t know if it made it into widespread production https://www.wired.com/story/mri-magnet-cooling/ https://www.wired.com/story/mri-magnet-cooling/
      - nullc 2y ago
        Sounds like that's more about using a cryocooler to minimize the helium used-- but presumably that requires keeping the coils in a particularly hard vacuum to adequately insulate them. There is some research towards operating at liquid hydrogen temperatures -- but hydrogen has its own logistical challenges.
- BobbyTables2 2y ago
  A cardboard box containing preprinted scan results would also be even cheaper and faster. But some people actually like to have something that works.
brnt 2y ago
Medical imaging devices and medical devices in general are a racket. There are only a few companies and they are legal and lobbying departments first and foremost. This isn't the first time radical and radically cheaper prototypes have been proposed, but the unsolved bit it actually convincing anyone to buy. A colleague had a device and a veteran adviced him to 10x the price.
- londons_explore 2y ago
  > the unsolved bit it actually convincing anyone to buy. Surely a lot of small hospitals would jump at the chance at a small cheap MRI? I don't understand how the incumbents have much legal leverage here...
  - brnt 2y ago
    It's about insurance, certification of personnel, often these technicians are a cartel in an of themselves. Everybody loves the idea of cheaper stuff, but nobody is going to take a chance. Medicine is extremely conservative. Overly in my opinion.
  - whatevaa 2y ago
    You need a specialist to understand an MRI image. Maybe software will advance enough to change this, but it will be a slow progress. Also, carterls are definitely a thing. Radiologists will fight the software part.
alwa 2y ago
I can’t access the full paper, but from the abstract, is it accurate that they’re using ML techniques to synthesize higher-quality and higher-resolution imagery, and that’s the basis for their claim that it’s comparable to the output of a conventional MRI scan? Do clinicians really prefer that the computer make normative guesses to “clean up” the scan, versus working with the imagery reflecting the actual measurements and applying their own clinical judgment?
- deepsun 2y ago
  My understanding as well. That... will bias towards training data, and will miss more anomalies. And anomalies is the point of scanning.
- eig 2y ago
  I can say that most radiologists would not want a computer trying to fix poor scan data. If the underlying data is bad, they would have recommend an orthogonal imaging abnormality. "I don't know" is a possible response radiologists can give. Trying to add training data to "clean up" an image would bias the read towards "normal".
  - falcor84 2y ago
    To nitpick, wouldn't it by definition bias the read toward normal? I suppose the problem is more that you don't want to bias it to normal if it wasn't.
    - mminer237 2y ago
      The training data is going to have far more normal scans of any given part than it will abnormal.
  - bone_slide 2y ago
    Spot on. When I can't interpret a study due to artifact, I say that in my report. Let's say there's a CTA chest that is limited because the patient breathed while the scan was being acquired, I need to let the ordering clinician know that the study is not diagnostic, and recommend an alternative. If AI eliminates the artifact by filling in expected but not actually acquired data, I am screwed and the patient is screwed.
habosa 2y ago
This is remarkable. 1800W is like a fancy blender, amazing to be able to do a useful MRI at that power. For anyone who is unaware, a standard MRI machine is about 1.5T (so 30x the magnetic strength) and uses 25kW+. For special purposes you may see machines up to 7T, you can imagine how much power they need and how sensitive the equipment is. Lowering the barriers to access to MRIs would have a massive impact on effective diagnosis for many conditions.
- deleted 2y ago
  [deleted]
- _vaporwave_ 2y ago
  This reminded me of the recent request for startups proposal by Surbhi Sarna “A way to end cancer”. The proposal states that we already have a way (MRI) to diagnose cancer at very early stages where treatment is feasible but cost and scaling need to be tackled to make it widely accessible. Something like this low power MRI could be a key part of enabling a transformation of cancer treatment.
imjonse 2y ago
The key is cheaper device combined with deep learning.
- toomuchtodo 2y ago
  Could you use the deep learning to improve the device to reduce the need for deep learning to fill in the gaps from traditional devices? Essentially teaching an algorithm to build a better, more simple imaging device in a pid loop?
arkades 2y ago
I have a hard time picturing the radiologist whose reputation and malpractice rely on catching small anomalies being comfortable using a machine predicated on inferring the image contents.
blegr 2y ago
Does this make MRIs safe for some people who wouldn’t qualify due to metal implants? Or at least reduce the risk of accidents?
- rasmus1610 2y ago
  Probably yes. But most medical implants today are MRI-scannable anyway. Even patients with pacemakers can be scanned today with proper preparation.
- boxed 2y ago
  Safe to use, but it's not going to be able to see anything, as it's AI hallucination.
RivieraKid 2y ago
Well in theory you use a neural net to can generate realistic MRI images with 0 Tesla.
- Toutouxc 2y ago
  I love how succinct this argument is, and yet it contains everything.
  - deleted 2y ago
    [deleted]
cornholio 2y ago
> We conducted imaging on healthy volunteers, capturing brain, spine, abdomen, lung, musculoskeletal, and cardiac images. Deep learning signal prediction effectively eliminated EMI signals, enabling clear imaging without shielding. So essentially, the neural net was trained to what a healthy MRI looks like and would, when exposed to abnormal structures, correct them away as EMI noise leading to wrong diagnostics? I won't be very dismissive of this approach and probably deep learning has a strong role to play in improving medical imaging. But this paper is far, far from sufficient to prove it. At a minimum, it would require mixed healthy / abnormal patients with particularities that don't exist in the training set, and each diagnostic reconfirmed later on a high resolution machine. You need to actually prove the algorithm does not distort the data, because an MRI that hallucinates a healthy patient is much more dangerous than no MRI at all.
- rossant 2y ago
  Seems like a huge and obvious red flag to me indeed. I can't imagine how the authors managed to not even mention the issue in the abstract. If the model is trained on healthy scans, well, yes, it will spit out healthy scans. The whole point of clinical radiology is to get enough precision to detect (potentially subtle) anomalies.
- nullc 2y ago
  I don't think that (necessarily) says what you think it says. You can read that as saying that the DL eliminated the background noise rather than saying that the system was conditioned on images of healthy people. From that it may well have been conditioned on just an empty machine or neutral test samples. If so, there may be a good reason to suspect that it isn't likely to create artifacts that look like or mask anatomical structures.
  - cornholio 2y ago
    You can read it like that, but they surely didn't prove it works like that and the burden of proof is squarely on them. Realistically, the training set is most likely MRIs of similar tissues and would be naturally biased towards healthy structures. Even the remotest possibility of a hallucination should be addressed and disproved for such an application but they make no mention of it, just "OMG magic ENHANCE button!".
eig 2y ago
A few months ago there were articles going around about how Samsung galaxy phones were upscaling images of the Moon using AI [0]. Essentially, the model was artificially adding landmarks and details based on its training set when the real image quality was too poor to make out details. Needless to say, AI upscaling as described in this article would be a nightmare for radiologists. 90% of radiology is confirming the absence of disease when image quality is high, and asking for complementary studies when image quality is low. With AI enhanced images that look "normal", how can the radiologist ever say "I can confirm there is no brain bleed" when the computer might be incorrectly adding "normal" details when compensating for poor image quality? [0] - https://news.ycombinator.com/item?id=35136167 https://news.ycombinator.com/item?id=35136167
- atoav 2y ago
  This is one aspect about machine learning models I keep discussing with non-technical passengers of the AI-hype-train: They are (in their current form) unsitable for applications where correctness is absolutely critical.
  - teaearlgraycold 2y ago
    I don’t know enough to make absolute statements here, but deep learning models can beat out human experts at discerning between signal and noise. Using that to guess at data and then hand it off to humans gives you the worst of both worlds. Two error probabilities multiplied together. But to simply render a verdict on whether a condition exists I’d trust a proven algorithm.
    - coffeebeqn 2y ago
      There are a lot of models that are simply good at that without hallucinating nonsense. LLMs are a specific thing with their own tradeoffs and goals. If you have a ML model that says how much does this microscope photo look like an anomaly in this persons blood on a scale from 0-100 it can certainly do better than a human.
    - atoav 2y ago
m3kw9 2y ago
It may miss some scans because there could be special cases which the model wasn’t trained with and would predict a different result/error. Maybe it’s acceptable in places where you may not even get a chance to be diagnosed
BobbyTables2 2y ago
Enhance!
ryankrage77 2y ago
I think this could be useful as a starting point for diagnostics - a cheaper, lower-power device massively lowers the barrier to entry to getting an MRI scan, even if it's not fully reliable. If it does find something, that's evidence a higher-quality scan is worth the resources. In short, use the worse device to take a quick look, if it finds anything, then take a closer look. If it doesn't find anything, carry on with the normal procedure.
- mnau 2y ago
  Is cost of machines really barrier? I can get MRI for $400-$500 as a self payer (Eastern Europe, i.e. if i just wanted it, not that doctor would say he wants it). I read a paper few years ago about utilization rate, machine/service cost, how many machines per citizen/hospital... They were running day and night. Cursory glance at other countries also reveal sensible prices. Unless it gets to a point of ultra sound machine(i.e. machine in a the consulting room a doctor can use in 10 minutes), I don't think it will decrease price much.
Aurornis 2y ago
The idea sounds great, but the examples they provide aren’t encouraging for the usefulness of the technique: > The brain images showed various brain tissues whereas the spine images revealed intervertebral disks, spinal cord, and cerebrospinal fluid. Abdominal images displayed major structures like the liver, kidneys, and spleen. Lung images showed pulmonary vessels and parenchyma. Knee images identified knee structures such as cartilage and meniscus. Cardiac cine images depicted the left ventricle contraction and neck angiography revealed carotid arteries. Maybe there’s more to it that I’m missing, but this sounds like the main accomplishment is being able to identify that different tissues are present. Actually getting diagnostic information out of imagining requires more detail, and I’m not sure how much this could provide.
sitkack 2y ago
The application of a system like this could be as augmentation to imagers like CT and ultrasound. Because of its up resolution techniques and lower raw resolution (2x2x8mm), it might not be used for early cancer detection. But it looks really useful in a trauma center or for guiding surgery, etc. These same techniques could also be applied to CT scans, I could see a multi sensor scanner that did both CT and NMRI use super low power, potentially even battery powered. Regardless, this is super neat. > We developed a highly simplified whole-body ultra-low-field (ULF) MRI scanner that operates on a standard wall power outlet without RF or magnetic shielding cages. This scanner uses a compact 0.05 Tesla permanent magnet and incorporates active sensing and deep learning to address electromagnetic interference (EMI) signals. We deployed EMI sensing coils positioned around the scanner and implemented a deep learning method to directly predict EMI-free nuclear magnetic resonance signals from acquired data. To enhance image quality and reduce scan time, we also developed a data-driven deep learning image formation method, which integrates image reconstruction and three-dimensional (3D) multiscale super-resolution and leverages the homogeneous human anatomy and image contrasts available in large-scale, high-field, high-resolution MRI data.
rasmus1610 2y ago
I'm a radiologist and very sceptic about low-field MRI + ML actually replacing normal high-field MRI for standard diagnostic purposes. But in a emergency setting or especially for MRI-guided interventions these low-field MRIs can really play a significant role. Combining these low-field MRIs with rapid imaging techniques makes me really excited about what interventional techniques become possible.
- sitkack 2y ago
  There is an opinion piece in the same issue that agrees with you. https://www.science.org/doi/10.1126/science.adp0670 https://www.science.org/doi/10.1126/science.adp0670 > This machine costs a fraction of current clinical scanners, is safer, and needs no costly infrastructure to run (2). Although low-field machines are not capable of yielding images that are as detailed as those from high-field clinical machines, the relatively low manufacturing and operational costs offer a potential revolution in MRI technology as a point-of-care screening tool. I don't think this machine is being billed as replacement to high-field machines.
  - xattt 2y ago
    > I don't think this machine is being billed as replacement to high-field machines. Countries where health regulation is less developed are likely to see misrepresentation where this form of MRI will be equated to full-field MRI by snake oil salesmen.
    - nullc 2y ago
      The US government bought divining rods to detect IED's in Iraq. Dumb stuff happens, but we achieve so much in spite of it.
      - ahaferburg 2y ago
        You mean this? https://en.wikipedia.org/wiki/ADE_651 https://en.wikipedia.org/wiki/ADE_651 If you did, the US government didn't buy those, but Iraq did.
modeless 2y ago
Wow, this seems like it could be a DIY project! I know people are complaining about the AI stuff but look at the images before AI enhancement. They look pretty awesome already!
w10-1 2y ago
With a voxel size of 2x2x8mm^3, this would do what X-rays/CT's do now, and a bit more (but likely not replace high-energy MRI's? I'm not understanding how they rival high-energy accuracy in-silico, but that's how the paper's written) In the acute setting, faster and more ergonomic imaging could be big. E.g., in a purpose-build brain device, if first responders had a machine that tells hemorrhagic vs ischemic stroke, it would be easier to get within the tPA time window. If it included the neck, you could assess brain and spine trauma before transport (and plan immobilization accordingly).
bilsbie 2y ago
How big of a deal is this? Isn’t it basically a 10/10? Seems like it could open up MRI’s to everyone.
- boxed 2y ago
  Not a big deal at all. You can make an "MRI" with literally zero magnetic field strength like this. Just make an AI hallucinate the entire super crisp image! This is what this paper is basically doing it seems. "Look how clear the image is!" yea, because it's not real, it's AI generated garbage.
elektropionir 2y ago
It is just weird that papers like this can be published. "Deep learning signal prediction effectively eliminated EMI signals, enabling clear imaging without shielding." - this means that they have found a way to remove random noise, which if true, should be the truly revolutionary claim in this paper. If the "EMI" is not random you can just filter it so you don't need what they are doing. If it isn't random, whatever they are doing can "predict" the noise, they even use the word in that sentence. They are claiming that they can replace physical filtering of noise before it corrupts the signal (shielding) with software "removal" of noise after it has already corrupted the signal. This is simply not possible without loss of information (i.e. resolution). The images that they get from standard Fourier Transform reconstruction are still pretty noisy so on top they "enhance" the reconstruction by running it through a neural net. At that point they don't need the signal - just tell the network what you want to see. The fact that there are no validation scans using known phantoms is telling.
- op00to 2y ago
  It would suck if lesions or tumors look like noise.
  - fnordpiglet 2y ago
    Except there are other uses for an MRI and something that doesn’t require super conductors would be pretty awesome and deployable to places that lack the infra to support a complex machine depending on near absolute zero temperatures and the associated complexities.
    - Dylan16807 2y ago
      The same criticism applies to all uses. It would suck if a bad bolt looks like noise, etc. If the technique is fundamentally broken, then it won't work in any situation.
- MrLeap 2y ago
  Remember the early atomic age when people were doing wild shit like adding radium to your toothpaste so you can brush your teeth in the dark? This is that, but again, with AI.
tiahura 2y ago
Wouldn't ai ultrasound be more useful?
rhindi 2y ago
There are some non-ML based approaches for ultra low field MRI that are starting to work: https://drive.google.com/file/d/1m7K1W--UOUecDPlm7KqFYzfkoewZtlRl/view https://drive.google.com/file/d/1m7K1W--UOUecDPlm7KqFYzfkoew... . You can still add AI on top of course, but at least you get a better signal to noise ratio to start with!
jaquer_1 2y ago
[dead]
cashsterling 2y ago
I can't read the full article but low-T MRI is potentially a big deal IMO because a 0.05T magnetic coil can be air or water-cooled but higher T-magnets (like 1.5 and 3T MRI magnets) have to use superconducting wire and thus must be cooled to sub 60K temperatures (even down to sub 10K) using Helium refrigeration cycles. I worked for a time at a company that made MRI calibration standards (among many other things). helium refrigeration cycle equals: - elaborate and expensive cryogenic engineering in the MRI overall design. - lots of power for the helium refrigeration cycle. - requirements for pure helium supply chain, which is not possible in many parts of the world, including areas of Europe, North America, etc.
- pbmonster 2y ago
  > low-T MRI is potentially a big deal IMO because a 0.05T magnetic coil can be air or water-cooled Even better, they just use a big permanent magnet.
bone_slide 2y ago
As a practicing radiologist, I think this is great. We can have AI enabled MRI scanners hallucinating images, read by AI interpreting systems hallucinating reports!
- rossant 2y ago
  And then we have systems hallucinating patients, data, and entire medical publications! The future is here.
boxed 2y ago
That Science let this through peer review and editor filtering is pretty damning. This wouldn't pass 5 minutes of review here on Hacker News.
- sigmoid10 2y ago
  That's because people on Hacker News don't know shit. Everyone here who has a superficial understanding of deep learning and believes they know what an MRI is comes up with extremely strong opinions, but it is very obvious that noone here has even read the full article beyond the abstract (or even the title). There are legitimate questions and concerns you can raise about this article, but not a single one of them is found in this comment section.
  - boxed 2y ago
    I mean, we can wait and see for reproduction/retraction. I would bet no reproduction possible or outright retraction.
    - sigmoid10 2y ago
      This paper is actually the culmination of a series of developments in the field over the past decade. Anyone who was following the subject was not surprised by it. Yours and all the other comments here are nothing but a testimony to the presumptuousness of HN. It's also funny how posts about modern physics usually just yield admissions that people know nothing of the subject, but when the topic is AI or medicine, everyone is suddenly a research scientist.
- formerly_proven 2y ago
  > This wouldn't pass 5 minutes of review here on Hacker News. Neither did Dropbox.
p0w3n3d 2y ago
Low power MRI can be a salvation to people who have some metal inside their body. Of course imaging those parts might be still impossible, but maybe other parts can be imaged
SergeAx 2y ago
> applying machine learning to the output of a lower-power MRI device So, we get worse SNR data from the device and then enhance it with compressed knowledge from millions of past MRI images? Isn't it like shooting the movie with Grandpa's 8mm camera and then enhancing and upscaling it like those folks on YouTube do with historical footage?
theginger 2y ago
Awkward, I was expecting to be reading an article about Tesla moving into the medical industry. Who's idea was it to name the unit of magnetic flux density after a car company? This is worse than that time I ended up reading an article about a shallow river crossing
rwmj 2y ago
AI upscaling, nope. Medical images aren't even allowed to be stored in lossy compressed formats because of the danger of creating artifacts.
el_don_almighty 2y ago
This problem has already been solved by the MRI machines at your local airport. Cost and performance are not the issues. For $25 your luggage gets an MRI that automatically differentiates between organic molecules in seconds. How easily could this be adapted for free annual screenings at the mall? But that's not the objective, and so your research is doomed The medical equipment industry will not suffer fools who don't understand 'regulatory capture' and 'rent seeking.' Those hospital machines are expensive and rare for reasons that have very little to do with cost or performance

187 points by Jimega36 2y ago

> Each protocol was designed to have a scan time of 8 minutes or less with an image resolution of approximately 2×2×8 mm³ very cool, but is it clinically useful if one edge of your voxel is 8mm?

hesdeadjim 2y ago
Easier to scale up than down once you have a starting point like this.
nullc 2y ago
8mm slice thickness isn't particularly at odds with what is commonly done on commercial machines, though usually there is a second transverse scan (which can't be readily fused due to patient movement). But even if it were, plenty of interesting structures are many centimeters in size, a thousand fold decrease in costs from eliminating cryogenic / high power magnets could be very useful.
- parpfish 2y ago
  the structures are many centimeters, but I assume that the sort of anomalies you'd be looking for in a clinical scan aren't going to be that large. if you had a fracture/tumor/damage-of-some-type that's small enough to fit between those slices and you didn't get the slices lined up just right the scan would miss it, no?

xyst 2y ago

“Tesla” is a unit of measure for magnetic strength; and not the car manufacturer.

throwup238 2y ago
I figured they were using 1/20th of Nikola Tesla's cadaver. That's the only logical interpretation of that headline.
- xyst 2y ago
  lmao, same bro. I reading the paper and only then I discovered it’s about the viability of a low powered MRI machine for diagnostic imaging. Particularly useful in poorer countries.
- hinkley 2y ago
  The device is actually designed to scan 20 people at the same time. It’s cheap because you get a bulk discount on your scans.

Jimega36 2y ago

"The lower-power machine was much cheaper to manufacture and operate, more comfortable and less noisy for patients, and the final images after computational processing were as clear and detailed as those obtained by the high-power devices currently used in the clinical setting."

tkzed49 2y ago
300-1800W power draw seems impressive! It looks like standard machines are using something on the order of 25kW while scanning, which certainly sounds prohibitive for less developed infrastructure.
- ChrisMarshallNY 2y ago
  Also, they need to keep vats of liquid helium around. Difficult stuff to store. I knew they needed cold gas, but liquid helium is crazy.
  - jahnu 2y ago
    There was some buzz years ago about using liquid nitrogen instead but I don’t know if it made it into widespread production https://www.wired.com/story/mri-magnet-cooling/ https://www.wired.com/story/mri-magnet-cooling/
    - nullc 2y ago
      Sounds like that's more about using a cryocooler to minimize the helium used-- but presumably that requires keeping the coils in a particularly hard vacuum to adequately insulate them. There is some research towards operating at liquid hydrogen temperatures -- but hydrogen has its own logistical challenges.
BobbyTables2 2y ago
A cardboard box containing preprinted scan results would also be even cheaper and faster. But some people actually like to have something that works.

brnt 2y ago

Medical imaging devices and medical devices in general are a racket. There are only a few companies and they are legal and lobbying departments first and foremost. This isn't the first time radical and radically cheaper prototypes have been proposed, but the unsolved bit it actually convincing anyone to buy. A colleague had a device and a veteran adviced him to 10x the price.

londons_explore 2y ago
> the unsolved bit it actually convincing anyone to buy. Surely a lot of small hospitals would jump at the chance at a small cheap MRI? I don't understand how the incumbents have much legal leverage here...
- brnt 2y ago
  It's about insurance, certification of personnel, often these technicians are a cartel in an of themselves. Everybody loves the idea of cheaper stuff, but nobody is going to take a chance. Medicine is extremely conservative. Overly in my opinion.
- whatevaa 2y ago
  You need a specialist to understand an MRI image. Maybe software will advance enough to change this, but it will be a slow progress. Also, carterls are definitely a thing. Radiologists will fight the software part.

alwa 2y ago

I can’t access the full paper, but from the abstract, is it accurate that they’re using ML techniques to synthesize higher-quality and higher-resolution imagery, and that’s the basis for their claim that it’s comparable to the output of a conventional MRI scan? Do clinicians really prefer that the computer make normative guesses to “clean up” the scan, versus working with the imagery reflecting the actual measurements and applying their own clinical judgment?

deepsun 2y ago
My understanding as well. That... will bias towards training data, and will miss more anomalies. And anomalies is the point of scanning.
eig 2y ago
I can say that most radiologists would not want a computer trying to fix poor scan data. If the underlying data is bad, they would have recommend an orthogonal imaging abnormality. "I don't know" is a possible response radiologists can give. Trying to add training data to "clean up" an image would bias the read towards "normal".
- falcor84 2y ago
  To nitpick, wouldn't it by definition bias the read toward normal? I suppose the problem is more that you don't want to bias it to normal if it wasn't.
  - mminer237 2y ago
    The training data is going to have far more normal scans of any given part than it will abnormal.
- bone_slide 2y ago
  Spot on. When I can't interpret a study due to artifact, I say that in my report. Let's say there's a CTA chest that is limited because the patient breathed while the scan was being acquired, I need to let the ordering clinician know that the study is not diagnostic, and recommend an alternative. If AI eliminates the artifact by filling in expected but not actually acquired data, I am screwed and the patient is screwed.

habosa 2y ago

This is remarkable. 1800W is like a fancy blender, amazing to be able to do a useful MRI at that power. For anyone who is unaware, a standard MRI machine is about 1.5T (so 30x the magnetic strength) and uses 25kW+. For special purposes you may see machines up to 7T, you can imagine how much power they need and how sensitive the equipment is. Lowering the barriers to access to MRIs would have a massive impact on effective diagnosis for many conditions.

deleted 2y ago
[deleted]
_vaporwave_ 2y ago
This reminded me of the recent request for startups proposal by Surbhi Sarna “A way to end cancer”. The proposal states that we already have a way (MRI) to diagnose cancer at very early stages where treatment is feasible but cost and scaling need to be tackled to make it widely accessible. Something like this low power MRI could be a key part of enabling a transformation of cancer treatment.

imjonse 2y ago

The key is cheaper device combined with deep learning.

toomuchtodo 2y ago
Could you use the deep learning to improve the device to reduce the need for deep learning to fill in the gaps from traditional devices? Essentially teaching an algorithm to build a better, more simple imaging device in a pid loop?

arkades 2y ago

I have a hard time picturing the radiologist whose reputation and malpractice rely on catching small anomalies being comfortable using a machine predicated on inferring the image contents.

blegr 2y ago

Does this make MRIs safe for some people who wouldn’t qualify due to metal implants? Or at least reduce the risk of accidents?

rasmus1610 2y ago
Probably yes. But most medical implants today are MRI-scannable anyway. Even patients with pacemakers can be scanned today with proper preparation.
boxed 2y ago
Safe to use, but it's not going to be able to see anything, as it's AI hallucination.

RivieraKid 2y ago

Well in theory you use a neural net to can generate realistic MRI images with 0 Tesla.

Toutouxc 2y ago
I love how succinct this argument is, and yet it contains everything.
- deleted 2y ago
  [deleted]

cornholio 2y ago

> We conducted imaging on healthy volunteers, capturing brain, spine, abdomen, lung, musculoskeletal, and cardiac images. Deep learning signal prediction effectively eliminated EMI signals, enabling clear imaging without shielding. So essentially, the neural net was trained to what a healthy MRI looks like and would, when exposed to abnormal structures, correct them away as EMI noise leading to wrong diagnostics? I won't be very dismissive of this approach and probably deep learning has a strong role to play in improving medical imaging. But this paper is far, far from sufficient to prove it. At a minimum, it would require mixed healthy / abnormal patients with particularities that don't exist in the training set, and each diagnostic reconfirmed later on a high resolution machine. You need to actually prove the algorithm does not distort the data, because an MRI that hallucinates a healthy patient is much more dangerous than no MRI at all.

rossant 2y ago
Seems like a huge and obvious red flag to me indeed. I can't imagine how the authors managed to not even mention the issue in the abstract. If the model is trained on healthy scans, well, yes, it will spit out healthy scans. The whole point of clinical radiology is to get enough precision to detect (potentially subtle) anomalies.
nullc 2y ago
I don't think that (necessarily) says what you think it says. You can read that as saying that the DL eliminated the background noise rather than saying that the system was conditioned on images of healthy people. From that it may well have been conditioned on just an empty machine or neutral test samples. If so, there may be a good reason to suspect that it isn't likely to create artifacts that look like or mask anatomical structures.
- cornholio 2y ago
  You can read it like that, but they surely didn't prove it works like that and the burden of proof is squarely on them. Realistically, the training set is most likely MRIs of similar tissues and would be naturally biased towards healthy structures. Even the remotest possibility of a hallucination should be addressed and disproved for such an application but they make no mention of it, just "OMG magic ENHANCE button!".

eig 2y ago

A few months ago there were articles going around about how Samsung galaxy phones were upscaling images of the Moon using AI [0]. Essentially, the model was artificially adding landmarks and details based on its training set when the real image quality was too poor to make out details. Needless to say, AI upscaling as described in this article would be a nightmare for radiologists. 90% of radiology is confirming the absence of disease when image quality is high, and asking for complementary studies when image quality is low. With AI enhanced images that look "normal", how can the radiologist ever say "I can confirm there is no brain bleed" when the computer might be incorrectly adding "normal" details when compensating for poor image quality? [0] - https://news.ycombinator.com/item?id=35136167 https://news.ycombinator.com/item?id=35136167

atoav 2y ago
This is one aspect about machine learning models I keep discussing with non-technical passengers of the AI-hype-train: They are (in their current form) unsitable for applications where correctness is absolutely critical.
- teaearlgraycold 2y ago
  I don’t know enough to make absolute statements here, but deep learning models can beat out human experts at discerning between signal and noise. Using that to guess at data and then hand it off to humans gives you the worst of both worlds. Two error probabilities multiplied together. But to simply render a verdict on whether a condition exists I’d trust a proven algorithm.
  - coffeebeqn 2y ago
    There are a lot of models that are simply good at that without hallucinating nonsense. LLMs are a specific thing with their own tradeoffs and goals. If you have a ML model that says how much does this microscope photo look like an anomaly in this persons blood on a scale from 0-100 it can certainly do better than a human.
  - atoav 2y ago

m3kw9 2y ago

It may miss some scans because there could be special cases which the model wasn’t trained with and would predict a different result/error. Maybe it’s acceptable in places where you may not even get a chance to be diagnosed

BobbyTables2 2y ago

Enhance!

ryankrage77 2y ago

I think this could be useful as a starting point for diagnostics - a cheaper, lower-power device massively lowers the barrier to entry to getting an MRI scan, even if it's not fully reliable. If it does find something, that's evidence a higher-quality scan is worth the resources. In short, use the worse device to take a quick look, if it finds anything, then take a closer look. If it doesn't find anything, carry on with the normal procedure.

mnau 2y ago
Is cost of machines really barrier? I can get MRI for $400-$500 as a self payer (Eastern Europe, i.e. if i just wanted it, not that doctor would say he wants it). I read a paper few years ago about utilization rate, machine/service cost, how many machines per citizen/hospital... They were running day and night. Cursory glance at other countries also reveal sensible prices. Unless it gets to a point of ultra sound machine(i.e. machine in a the consulting room a doctor can use in 10 minutes), I don't think it will decrease price much.

Aurornis 2y ago

The idea sounds great, but the examples they provide aren’t encouraging for the usefulness of the technique: > The brain images showed various brain tissues whereas the spine images revealed intervertebral disks, spinal cord, and cerebrospinal fluid. Abdominal images displayed major structures like the liver, kidneys, and spleen. Lung images showed pulmonary vessels and parenchyma. Knee images identified knee structures such as cartilage and meniscus. Cardiac cine images depicted the left ventricle contraction and neck angiography revealed carotid arteries. Maybe there’s more to it that I’m missing, but this sounds like the main accomplishment is being able to identify that different tissues are present. Actually getting diagnostic information out of imagining requires more detail, and I’m not sure how much this could provide.

sitkack 2y ago

The application of a system like this could be as augmentation to imagers like CT and ultrasound. Because of its up resolution techniques and lower raw resolution (2x2x8mm), it might not be used for early cancer detection. But it looks really useful in a trauma center or for guiding surgery, etc. These same techniques could also be applied to CT scans, I could see a multi sensor scanner that did both CT and NMRI use super low power, potentially even battery powered. Regardless, this is super neat. > We developed a highly simplified whole-body ultra-low-field (ULF) MRI scanner that operates on a standard wall power outlet without RF or magnetic shielding cages. This scanner uses a compact 0.05 Tesla permanent magnet and incorporates active sensing and deep learning to address electromagnetic interference (EMI) signals. We deployed EMI sensing coils positioned around the scanner and implemented a deep learning method to directly predict EMI-free nuclear magnetic resonance signals from acquired data. To enhance image quality and reduce scan time, we also developed a data-driven deep learning image formation method, which integrates image reconstruction and three-dimensional (3D) multiscale super-resolution and leverages the homogeneous human anatomy and image contrasts available in large-scale, high-field, high-resolution MRI data.

rasmus1610 2y ago

I'm a radiologist and very sceptic about low-field MRI + ML actually replacing normal high-field MRI for standard diagnostic purposes. But in a emergency setting or especially for MRI-guided interventions these low-field MRIs can really play a significant role. Combining these low-field MRIs with rapid imaging techniques makes me really excited about what interventional techniques become possible.

sitkack 2y ago
There is an opinion piece in the same issue that agrees with you. https://www.science.org/doi/10.1126/science.adp0670 https://www.science.org/doi/10.1126/science.adp0670 > This machine costs a fraction of current clinical scanners, is safer, and needs no costly infrastructure to run (2). Although low-field machines are not capable of yielding images that are as detailed as those from high-field clinical machines, the relatively low manufacturing and operational costs offer a potential revolution in MRI technology as a point-of-care screening tool. I don't think this machine is being billed as replacement to high-field machines.
- xattt 2y ago
  > I don't think this machine is being billed as replacement to high-field machines. Countries where health regulation is less developed are likely to see misrepresentation where this form of MRI will be equated to full-field MRI by snake oil salesmen.
  - nullc 2y ago
    The US government bought divining rods to detect IED's in Iraq. Dumb stuff happens, but we achieve so much in spite of it.
    - ahaferburg 2y ago
      You mean this? https://en.wikipedia.org/wiki/ADE_651 https://en.wikipedia.org/wiki/ADE_651 If you did, the US government didn't buy those, but Iraq did.

modeless 2y ago

Wow, this seems like it could be a DIY project! I know people are complaining about the AI stuff but look at the images before AI enhancement. They look pretty awesome already!

w10-1 2y ago

With a voxel size of 2x2x8mm^3, this would do what X-rays/CT's do now, and a bit more (but likely not replace high-energy MRI's? I'm not understanding how they rival high-energy accuracy in-silico, but that's how the paper's written) In the acute setting, faster and more ergonomic imaging could be big. E.g., in a purpose-build brain device, if first responders had a machine that tells hemorrhagic vs ischemic stroke, it would be easier to get within the tPA time window. If it included the neck, you could assess brain and spine trauma before transport (and plan immobilization accordingly).

bilsbie 2y ago

How big of a deal is this? Isn’t it basically a 10/10? Seems like it could open up MRI’s to everyone.

boxed 2y ago
Not a big deal at all. You can make an "MRI" with literally zero magnetic field strength like this. Just make an AI hallucinate the entire super crisp image! This is what this paper is basically doing it seems. "Look how clear the image is!" yea, because it's not real, it's AI generated garbage.

elektropionir 2y ago

It is just weird that papers like this can be published. "Deep learning signal prediction effectively eliminated EMI signals, enabling clear imaging without shielding." - this means that they have found a way to remove random noise, which if true, should be the truly revolutionary claim in this paper. If the "EMI" is not random you can just filter it so you don't need what they are doing. If it isn't random, whatever they are doing can "predict" the noise, they even use the word in that sentence. They are claiming that they can replace physical filtering of noise before it corrupts the signal (shielding) with software "removal" of noise after it has already corrupted the signal. This is simply not possible without loss of information (i.e. resolution). The images that they get from standard Fourier Transform reconstruction are still pretty noisy so on top they "enhance" the reconstruction by running it through a neural net. At that point they don't need the signal - just tell the network what you want to see. The fact that there are no validation scans using known phantoms is telling.

op00to 2y ago
It would suck if lesions or tumors look like noise.
- fnordpiglet 2y ago
  Except there are other uses for an MRI and something that doesn’t require super conductors would be pretty awesome and deployable to places that lack the infra to support a complex machine depending on near absolute zero temperatures and the associated complexities.
  - Dylan16807 2y ago
    The same criticism applies to all uses. It would suck if a bad bolt looks like noise, etc. If the technique is fundamentally broken, then it won't work in any situation.
MrLeap 2y ago
Remember the early atomic age when people were doing wild shit like adding radium to your toothpaste so you can brush your teeth in the dark? This is that, but again, with AI.

tiahura 2y ago

Wouldn't ai ultrasound be more useful?

rhindi 2y ago

There are some non-ML based approaches for ultra low field MRI that are starting to work: https://drive.google.com/file/d/1m7K1W--UOUecDPlm7KqFYzfkoewZtlRl/view https://drive.google.com/file/d/1m7K1W--UOUecDPlm7KqFYzfkoew... . You can still add AI on top of course, but at least you get a better signal to noise ratio to start with!

jaquer_1 2y ago

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cashsterling 2y ago

I can't read the full article but low-T MRI is potentially a big deal IMO because a 0.05T magnetic coil can be air or water-cooled but higher T-magnets (like 1.5 and 3T MRI magnets) have to use superconducting wire and thus must be cooled to sub 60K temperatures (even down to sub 10K) using Helium refrigeration cycles. I worked for a time at a company that made MRI calibration standards (among many other things). helium refrigeration cycle equals: - elaborate and expensive cryogenic engineering in the MRI overall design. - lots of power for the helium refrigeration cycle. - requirements for pure helium supply chain, which is not possible in many parts of the world, including areas of Europe, North America, etc.

pbmonster 2y ago
> low-T MRI is potentially a big deal IMO because a 0.05T magnetic coil can be air or water-cooled Even better, they just use a big permanent magnet.

bone_slide 2y ago

As a practicing radiologist, I think this is great. We can have AI enabled MRI scanners hallucinating images, read by AI interpreting systems hallucinating reports!

rossant 2y ago
And then we have systems hallucinating patients, data, and entire medical publications! The future is here.

boxed 2y ago

That Science let this through peer review and editor filtering is pretty damning. This wouldn't pass 5 minutes of review here on Hacker News.

sigmoid10 2y ago
That's because people on Hacker News don't know shit. Everyone here who has a superficial understanding of deep learning and believes they know what an MRI is comes up with extremely strong opinions, but it is very obvious that noone here has even read the full article beyond the abstract (or even the title). There are legitimate questions and concerns you can raise about this article, but not a single one of them is found in this comment section.
- boxed 2y ago
  I mean, we can wait and see for reproduction/retraction. I would bet no reproduction possible or outright retraction.
  - sigmoid10 2y ago
    This paper is actually the culmination of a series of developments in the field over the past decade. Anyone who was following the subject was not surprised by it. Yours and all the other comments here are nothing but a testimony to the presumptuousness of HN. It's also funny how posts about modern physics usually just yield admissions that people know nothing of the subject, but when the topic is AI or medicine, everyone is suddenly a research scientist.
formerly_proven 2y ago
> This wouldn't pass 5 minutes of review here on Hacker News. Neither did Dropbox.

p0w3n3d 2y ago

Low power MRI can be a salvation to people who have some metal inside their body. Of course imaging those parts might be still impossible, but maybe other parts can be imaged

SergeAx 2y ago

> applying machine learning to the output of a lower-power MRI device So, we get worse SNR data from the device and then enhance it with compressed knowledge from millions of past MRI images? Isn't it like shooting the movie with Grandpa's 8mm camera and then enhancing and upscaling it like those folks on YouTube do with historical footage?

theginger 2y ago

Awkward, I was expecting to be reading an article about Tesla moving into the medical industry. Who's idea was it to name the unit of magnetic flux density after a car company? This is worse than that time I ended up reading an article about a shallow river crossing

rwmj 2y ago

AI upscaling, nope. Medical images aren't even allowed to be stored in lossy compressed formats because of the danger of creating artifacts.

el_don_almighty 2y ago

This problem has already been solved by the MRI machines at your local airport. Cost and performance are not the issues. For $25 your luggage gets an MRI that automatically differentiates between organic molecules in seconds. How easily could this be adapted for free annual screenings at the mall? But that's not the objective, and so your research is doomed The medical equipment industry will not suffer fools who don't understand 'regulatory capture' and 'rent seeking.' Those hospital machines are expensive and rare for reasons that have very little to do with cost or performance