121–130 of 151 posts
Being able to use this around metal is also huge. It means you can use it on patients with metal implants or bullets in them, to guide surgery in real-time, in the surgical suite, at the same time as other medical instruments, at the patient's bedside instead of in a dedicated room that you have to transport the patient to, etc.
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This project doesn't use AI to improve the image, they use it to estimate the EMI noise from the surroundings. So they're not "filling in the gaps" in the actual resulting 3D voxel volume with fantasy voxels (which I hope will never ever fly in a clinical setting). "To tackle the EMI signals from the external environments and internal low-cost electronics during scanning, we developed a deep learning driven EMI cance…
the primary innovation is using deep learning to denoise the signal and the cost savings derived from being able to use a noisier signal. whether you call it "SnR improvement" or "additive noise cancellation", it is undeniably adulteration of the signal. looking at the supplementary information, it looks like this paper was reviewed by mr-physicists. i think it also should have been reviewed by ml experts as well.
1. Measure outside interference sources 2. Measure MRI of "nothing" 3. Use ML to estimate f(interference) = noise 4. Subtract estimated noise from signal
So the noise removal process has no awareness of brains, skeletons, etc.
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…
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…
1. Measure outside interference sources 2. Measure MRI of "nothing" 3. Use ML to estimate f(interference) = noise 4. Subtract estimated noise from signal
So the noise removal process has no awareness of brains, skeletons, etc.
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Some of the code is Matlab. I wonder if GNU octave is sufficient to run it. That would be a big savings for studying this.
I am surprised that there isn't open source alternatives to Simulink, Stateflow etc. One reason Matlab is popular in academia and industry is because someone who don't know C/C++ (mechanical engineers etc.) can use something like Simulink to program real-time systems on microcontrollers and FPGAs.
I think a bigger reason MATLAB is popular is that it works really reliably, it is very well documented, it has a ton of complex algorithms and functions built in (or available in toolkits at least) and the interactive GUI works very well.
There isn't anything anywhere that comes close to MATLAB's plotting abilities. And scientists do a hell of a lot of plotting.
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I'm glad that this is the approach that they are taking. There have been plenty of issues with fMRI false positives due to misconfigured software. The most famous would probably be the IG Nobel winning study that detected brain activity in a store-bought salmon: https://blogs.scientificamerican.com/scicurious-brain/ignobe... https://www.discovermagazine.com/mind/fmri-gets-slap-in-the-... Later studies called into que…
Thanks for the kind words. I am the first author of the "Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: An argument for multiple comparisons correction" paper. Happy to take any questions here. A link to the original poster: http://prefrontal.org/files/posters/Bennett-Salmon-2009.pdf
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This is a good comment and still underplays the cost of MRI. Getting a reasonable 3T setup going will be a lot more than US$1 million. Running costs are very high, with a scanner lifetime service contract being somewhere between 50% and 100% the original cost of the MRI scanner. Additionally, the scanner cost is only part the price. There is the Faraday cage, chilling, room setup, building strengthening, scanner inst…
Why so expensive? Couldn't you just site it way out in the boonies? If you put it on a giant purpose-built concrete cube, it seems like most of this stuff becomes cheaper.
Scanner location is absolutely a driver in where people get their imaging done. People want good parking and a nearby location.
If the building is being purpose built it is a lot easier, as retro fitting steel isn’t cheap.
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I had a friend automating a production line and he needed to detect the level of peanut butter in a vat. He settled on an ultrasonic device as basically anything else ended up caked in peanut butter. It was an impressive setup, all done in Arduino.
I wonder if its practical to DIY your own production line automation today with Arduino. Like if I had a simple product, could I buy stuff off the shelf and integrate it myself in my garage and end up with a little mini-factory? Exciting to think about.
Computation is rarely the problem.
It's all about sensors and actuators.
One of the best college projects ever was to make a "drink pouring machine". You have a half-dozen bottles of various alcohols and other liquids, a circular table, and a glass. Now, pour a martini. Now, pour a gimlet. etc.
What a nightmare! The liquids have different viscosities and consequent pour rates. And shutoff is rarely clean. And don't rotate too fast or you will mess up your glass position. It goes on ... and on ... and on.
Everyone who did that project came out ... changed.
At $20k estimated cost, you are getting into territory where the TAM of non-medical uses may be higher. Sports physio / trainers would kill to be able to do regular MRI on their athletes. Being able to do pre/post workout imaging, and the kind of training programs this level of visibility would unlock, are quite exciting. Assuming you can generalize from brain to whole-body, I think you could sell one of these to eve…
This might have some interesting industrial applications as well (checking composites for defects, food packaging, etc). There might be some other applications that are currently unknown as MRI machines are too expensive (eg: checking beams in bridges).
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The flip-side of that is that PET is vastly multiple orders of magnitude far more sensitive than MRI. So while PET may not be able to localize as well as MRI, it can detect smaller things if the targeting of the radioisotope is good.
Maybe the state of play has changed, as we scan for this indication in MR, not PET. Have you a link to something as my understanding is that small lesions are better found with MR? Or is this a rule that applies to high end research work and hasn’t hit clinical practice yet? Maybe a limitation of the isotopes used clinically?
FDG PET/CT is not used to stage intracranial metastases due to background brain activity significantly reducing sensitivity. You’re likely only detecting lesions 1cm or greater in the brain, or ones which demonstrate low metabolic activity and appear dark.
MRI is much more sensitive and specific and is the standard of care for staging in my practice and as per the NCCN clinical guidelines. I haven’t been to any institution or heard of one where PET is used for staging of brain metastases.
Not sure where this is coming from.