[elektropionir]

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.

[MrLeap]

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]

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]

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?

[azalemeth]

Zero-boil-off "dry" magnets have been widely used for the last decade -- we engineered away the thousands of litres of liquid helium in exchange for bigger electricity bills and some added complexity (and arguably cost). They basically put the cryocompressor/cold head on a large heatsinked plate and use helium gas as a working fluid to cool it and through conduction the rest of the magnet. The supercon wire has a critical T/B/Ic surface and (to my knowledge) they essentially accept worse Ic in exchange for higher Tc.

The cold head vibration can introduce a bit more B0 drift per day, but it's not practically a problem.

Regarding artefacts, one of the other reasons that MRI rooms are expensive are the Faraday cages. They do help. Not just in terms of noise floors but because there tends to be a lot of intermittent RF transmission from people like paramedics. Did you know a) that the international mayday frequency is 121.5 MHz, b) that overhead helicopter flights may transmit with kW of RF on that frequency, c) that the larmour frequency of protons at ~2.9T is 121.5 MHz, d) that Siemens "3T" magnets are routinely around 2.9T, and e) the voltage of the signal you detect in MR is micro to millivolt at best? I've seen spurious peaks in spectra from this.

The DL method the paper talks about "may work", but as the OP says this is deeply unsatisfactory for a whole host of reasons and is, in my overly sarky opinion, a bit like fixing a wall with rising damp by putting a television in front of it showing a beautiful, high resolution picture of a brick wall in the same colour.

[nullc]

> I've seen spurious peaks in spectra from this.

Perhaps a standard bit of kit for an imaging room ought to be a receiver at the operating frequency outside of the room that can pause the sequence when a potential jammer is active, and log the event so that you could potentially make a report to the relevant authorities (perhaps encourage them to keep the transmitters off near your facility).

Pausing the sequence is also not so much of an option when contrast was just administered either, I guess.

(I suppose if the signal weren't so hot that it was saturating the ADCs there might be some opportunity to subtract it off... but that's starting to sound like another ten thousand phd-educated hours of labour mentioned up thread)

[bone_slide]

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.

[op00to]

It would suck if lesions or tumors look like noise.

[fnordpiglet]

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]

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.