[HN Gopher] Whole-body magnetic resonance imaging at 0.05 Tesla
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Whole-body magnetic resonance imaging at 0.05 Tesla
Author : Jimega36
Score : 114 points
Date : 2024-05-12 15:20 UTC (7 hours ago)
(HTM) web link (www.science.org)
(TXT) w3m dump (www.science.org)
| Jimega36 wrote:
| "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 wrote:
| 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 wrote:
| 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 wrote:
| 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/
| nullc wrote:
| 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 wrote:
| A cardboard box containing preprinted scan results would also
| be even cheaper and faster.
|
| But some people actually like to have something that works.
| parpfish wrote:
| > Each protocol was designed to have a scan time of 8 minutes or
| less with an image resolution of approximately 2x2x8 mm3
|
| very cool, but is it clinically useful if one edge of your voxel
| is 8mm?
| hesdeadjim wrote:
| Easier to scale up than down once you have a starting point
| like this.
| nullc wrote:
| 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 wrote:
| 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 wrote:
| "Tesla" is a unit of measure for magnetic strength; and not the
| car manufacturer.
| throwup238 wrote:
| I figured they were using 1/20th of Nikola Tesla's cadaver.
| That's the only logical interpretation of that headline.
| xyst wrote:
| 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 wrote:
| 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.
| brnt wrote:
| 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 wrote:
| > 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 wrote:
| 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.
| alwa wrote:
| 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 wrote:
| My understanding as well. That... will bias towards training
| data, and will miss more anomalies. And anomalies is the point
| of scanning.
| eig wrote:
| 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 wrote:
| 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.
| bone_slide wrote:
| 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.
| j7ake wrote:
| They already use computers to make guesses to clean up the
| scan.
|
| A core part of processing MRI is the compressed sensing
| algorithm .
| habosa wrote:
| 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.
| _vaporwave_ wrote:
| 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 wrote:
| The key is cheaper device combined with deep learning.
| toomuchtodo wrote:
| 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 wrote:
| 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 wrote:
| 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 wrote:
| Probably yes. But most medical implants today are MRI-scannable
| anyway. Even patients with pacemakers can be scanned today with
| proper preparation.
| RivieraKid wrote:
| Well in theory you use a neural net to can generate realistic MRI
| images with 0 Tesla.
| Toutouxc wrote:
| I love how succinct this argument is, and yet it contains
| everything.
| cornholio wrote:
| > 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 wrote:
| 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.
| eig wrote:
| 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
| atoav wrote:
| 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 wrote:
| 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 wrote:
| 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.
| BobbyTables2 wrote:
| The Samsung phone wasn't a technological advancement, it was
| sheer fraud.
|
| A camera is supposed to take pictures of what it sees.
|
| Imagine going to a restaurant, ordering French onion soup, and
| getting a bowl of brown food coloring in water.
| sieste wrote:
| > Imagine going to a restaurant, ordering French onion soup,
| and getting a bowl of brown food coloring in water.
|
| Welcome to England!
| GaylordTuring wrote:
| I know this isn't Reddit, but haha. Take my upvote!
| seanmcdirmid wrote:
| I still remember the zoom and enhance joke they played on
| red dwarf. Parody has become reality.
| willis936 wrote:
| Immortalized in Super Troopers (2001).
|
| https://youtu.be/KiqkclCJsZs
| zarmin wrote:
| It's kinda like the classic Ebay scam where you buy a picture
| of the item instead of the item.
| falcor84 wrote:
| Yes, or the increasingly common Amazon one, where you get
| an AI-generated summary of the book, instead of the actual
| book.
| pavlov wrote:
| _> "A camera is supposed to take pictures of what it sees."_
|
| Feels like that's just a matter of expectations.
|
| A phone used to be a device for voice communications. It's
| right there in the Greek etymology, "phone" for sound. But
| 95% of what people do today on devices called phones is
| something else than voice.
|
| Similarly, if people start using cameras more to produce
| images of things they want rather than what exists in front
| of the lens, then that's eventually what a camera will mean.
| Snapchat thinks of themselves as a camera company, but the
| images captured within their apps are increasingly
| synthesized.
|
| (The etymology of "camera" already points to a journey of
| transformation. A photographic camera isn't a literal room,
| as the camera obscura once was.)
| rzzzt wrote:
| Taking this thought to its logical conclusion:
| https://bjoernkarmann.dk/project/paragraphica
| kyriakos wrote:
| small correction "phone" means voice not sound :)
| thsksbd wrote:
| Some of us want a record of what was, not a hallucination
| of what might have or could have been.
|
| Courts, for example. Forensic science was revolutionized by
| widespread adoption of photography leading to a reduction
| of the importance given to witnesses. Who also hallucinate
| what might have happened.
| eig wrote:
| An MRI machine is a fancy 3D camera. Is this "3D Deep-DSP
| Model" so different from the processing Samsung did on their
| phones?
| peddling-brink wrote:
| Samsung would replace a white circle with an image of the
| moon. Even calling it AI was a stretch.
| vhcr wrote:
| Where do you draw the line? RAW, HDR, photo stitching, blur
| removal?
| ed312 wrote:
| This is an excellent ponit, and I don't know where to
| exactly draw the line ("I know it when I see it"). I
| personally use "auto" (probably heuristic, maybe soon-ish
| AI-powered) features to adjust levels, color balance etc.
| Using AI to add things that are _not at all present_ in the
| original crossed the line into digital art vs photography
| for me.
| Toutouxc wrote:
| I draw the line where the original pixel values are still
| part of the input. As long as you're manipulating
| something that the camera captured, it's still
| photography, even if the math isn't the same for all
| pixels, or is AI powered.
|
| But IMO it's a point worth bringing up, most people have
| no idea how digital photography works and how difficult
| it is to measure, quantify and interpret the analog
| signal that comes from a camera sensor to even resemble
| an image.
| sneak wrote:
| There was the small complication of the fact that the
| moon texture that Samsung got caught putting onto moon-
| shaped objects in photos is, of course, the same side of
| the same moon.
| Dylan16807 wrote:
| None of those are adding data, assuming normal definitions
| of 'blur removal' and not the AI kind. So with those the
| line is very easy to draw.
| bawolff wrote:
| > A camera is supposed to take pictures of what it sees.
|
| If people wanted cameras to actually take what it sees, then
| we wouldn't have autofocus, photoshop or instagram filters.
|
| The goal of a cell phone camera is to capture what you are
| experiencing, not to literally record what light strikes the
| cmos chip.
| UberFly wrote:
| A camera takes a picture of what it sees. What comes next
| is a different thing all together.
| enriquto wrote:
| > A camera takes a picture of what it sees.
|
| _All_ images taken with digital cameras have been
| filtered by a pipeline of advanced algorithms. Nobody
| ever looks at "what the camera sees". What kind of
| savage would look at an image before demosaicing the
| Bayer pattern? (Except from the people who work in
| demosaicing, of course.)
| wtallis wrote:
| > If people wanted cameras to actually take what it sees,
| then we wouldn't have autofocus,
|
| Bad example. Autofocus makes changes to the light that goes
| into the camera, not just the data that comes out.
|
| > photoshop or instagram filters
|
| Bad examples. Those both give the user a before-and-after
| comparison so the user can decide what kind of alterations
| are reasonable or desirable.
| nullc wrote:
| The state of the art MRI stuff uses "compressed sensing" --
| essentially image completion in some domain or another.
| Presumably, carefully designed to not hallucinate details or
| one would hope.
|
| There isn't necessarily a particularly neutral choice here: the
| MRI scan isn't in the pixel domain, artifacts are going to be
| 'weird' looking-- e.g. edges that move during the scan ringing
| across the whole image.
| CooCooCaCha wrote:
| Compressed sensing is far more mathematically rigorous.
| nullc wrote:
| I don't think we know what's in the black box here. It
| could be an equivalent relatively unopinionated regularizer
| ("the pixel domain will be locally smooth, to the extent it
| has edges they're spatially contiguous") or it could be
| "just look up the most similar image from a library and
| present that instead" or anywhere in between. :)
| CooCooCaCha wrote:
| They specifically said they use deep learning which
| implies a sizeable neural network.
| m3kw9 wrote:
| 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 wrote:
| Enhance!
| ryankrage77 wrote:
| 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 wrote:
| 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 wrote:
| 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 wrote:
| 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 wrote:
| 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 wrote:
| There is an opinion piece in the same issue that agrees with
| you.
|
| 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 wrote:
| > 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.
| bagels wrote:
| What is it about lower fields that means you cannot get a good
| image? Interference? Tissue movement in longer exposures? Why
| can't the device just integrate over a longer period of time?
| modeless wrote:
| 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 wrote:
| 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 wrote:
| How big of a deal is this? Isn't it basically a 10/10? Seems like
| it could open up MRI's to everyone.
| elektropionir wrote:
| 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 wrote:
| It would suck if lesions or tumors look like noise.
| fnordpiglet wrote:
| 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.
| MrLeap wrote:
| 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.
| azalemeth wrote:
| I'm a professional MR physicist. I genuinely think the
| profession is hugely up the hype curve with "AI" and to a far
| lesser extent low field. It's also worth saying that the
| rigorous, "proper" journal in the field is Magnetic Resonance
| in Medicine, run by the international society of magnetic
| resonance in medicine -- and that papers in nature or science
| generally nowadays tend to be at the extreme gimmicky end of
| the spectrum.
|
| A) Many MR reconstructions work by having a "physics model",
| typically in the form of a linear operator, acting upon the
| required data. The "OG" recon, an FT, is literally just a
| Fourier matrix acting on the data. Then people realised that
| it's possible to I) encode lots of artefacts, and ii)
| undersample k-space while using the spatial information using
| different physical rf coils, and shunt both these things into
| the framework of linear operators. This makes it possible to
| reconstruct it-- and Tikhonov regularisation became popular --
| so you have an equation like argmin _theta (yhat - X_1 X_2
| X_3.... X_n y) + lambda Laplace(y) to minimise, which does
| genuinely a fantastic job at the expense, usually, of non
| normal noise in the image. "AI" can out perform these
| algorithms a little, usually by having a strong prior on what
| the image is. I think it's helpful to consider this as some
| sort of upper bound on what there is to find. But as a warning,
| I've seen images of sneezes turned into knees with torn
| anterior cruciate ligaments, a matrix of zeros turned into
| basically the mean heart of a dataset, and a fuck ton of people
| talking bollocks empowered by AI. This isn't starting on
| diagnosis -- just image recon. The major driver is reducing
| scan time (=cost), required SNR (=sqrt(scan time)) or/and,
| rarely measuring new things that take too long. This almost
| falls into the second category
|
| The main conference in the field has just happened and
| ironically the closing plenty was about the risks of AI, as it
| happens.
|
| B) Low field itself has a few genuinely good advantages. The T2
| is longer, the risks to the patient with implants are lower,
| and the machines may be cheaper to make. I'm not sold on that
| last one at all. I personally think that the bloody cost of the
| scanner isn't the few km of superconducting wires in it -- it's
| the tens of thousands of phd-educated hours of labour that went
| into making the thing and their large infrastructure
| requirements, to say nothing of the requirements of the people
| who look at the pictures. There are about 100-250k scanners in
| the world and they mostly last about a decade in an institution
| before being recycled -- either as niobium titanium or as a
| scanner on a different continent (typically). Low field may
| help with siting and electricity, but comes at the cost of
| concomitant field gradients, reduced chemical shift dispersion,
| a whole set of different (complicated) artefacts, and the same
| load of companies profiteering from them.
| fnordpiglet wrote:
| Would it be easier to deploy devices like this to developing
| counties without the infrastructure to support liquid helium
| distribution? I imagine a much simpler device WRT exotic
| cooling and distribution of material requirements is a plus.
| Couple that with the scarcity and non-renewable nature of
| helium, maybe using devices like this at scale for gross MRI
| imagery makes sense?
|
| The AI used here as I read it is a generative approach trying
| to specifically compensate for EMI artifacts rather than a
| physics model and it likely wouldn't be doing macro changes
| like sneezes to knees, no?
| bone_slide wrote:
| As one of the people that look at the images, this is the
| best comment in the thread.
|
| Lots of AI nonsense permeating radiology right now, which
| seems to be fairly effective click bait and an easy way to
| generate hype and headlines.
| tiahura wrote:
| Wouldn't ai ultrasound be more useful?
| rhindi wrote:
| There are some non-ML based approaches for ultra low field MRI
| that are starting to work: 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!
| cashsterling wrote:
| 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.
| bone_slide wrote:
| 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!
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