
Massive MRI dataset released as part of ongoing AI project - laurex
https://www.radiologybusiness.com/topics/artificial-intelligence/nyu-facebook-release-knee-mri-dataset-ai
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aabajian
I'm a radiology resident with a master's in computer science. It's great that
healthcare organizations are finally releasing larger datasets. There's much
progress to be made in medical ML/AI.

However, my attitude towards radiology AI startups has changed quite a bit
since starting residency. There really isn't a sustainable business model that
involves selling standalone AI products, or even software suites. I think AI
is a feature for the scanner, the PACS or for existing dictation software.
That is, I think PowerScribe or GE would help a lot of radiologists by
integrating AI features into their existing tech stacks.

I also think the only feasible exit strategy for radiology AI/ML startups is
to get bought out for their tech talent and algorithms. I don't know of any
hospitals/clinics/imaging centers that are buying AI products directly from
startups. There's a ton of hype, but I'm not aware of a single positive-cash-
flow radiology AI company.

~~~
sna1l
It seems like reducing the cost/making MRIs more comfortable would be much
more useful.

Presumably if the machines didn't cost 3-5 million, with huge energy
requirements, etc, you could get scanned a lot more often which probably be
better than just using AI/ML?

If you were able to scan cancer patients cheaply and quickly for a much lower
cost and faster, I'd assume that would have a significant impact.

~~~
FireBeyond
MRIs are typically (from my perspective working in the health insurance
industry) profit centers for those who own them.

Which leads to perverse incentives, like MRI manufacturers assisting
physicians with setting up imaging consortiums, promising recoupment and
profits within 18 months. Perhaps unsurprisingly, such doctors order imaging
notably more often than others.

Low field MRI machines are around $1M. $3M gets you a brand new, state of the
art 3 tesla machine.

~~~
mtgx
Sounds like an obvious "disruption strategy" for a startup here. Why hasn't
anyone tried that yet?

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jayess
Many states limit supply of MRI machines through a "certificate of need"
program where you have to convince a regulatory body that you should be
allowed to own one.

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FireBeyond
That's part of the issue. MRI manufacturers "assisting" people through the
process of this certificate.

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nl
I think either of these are much more useful links:

[http://fastmri.org/](http://fastmri.org/)

[https://fastmri.med.nyu.edu/](https://fastmri.med.nyu.edu/)

Looks like a decent effort, FAIR and NYU involved, open source baseline system
etc. Still have to apply for access to the dataset though.

~~~
arikr
Agreed. @dang could you look at possibly swapping the link out with the
fastmri.org link?

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perone
This is for MRI reconstruction, it has no other labels or annotations, as far
as I know, only the raw data in k-space and the reconstruction. It's also only
for knee.

~~~
AboutTheWhisles
How are they expecting 'faster' MRIs from this? Are they searching for a way
to hone in on the important parts to take more detailed scans?

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jhj
The data is sampled in the Fourier domain. A complete scan (frequency up to a
desired Nyquist limit) takes a long time for the MRI machine to acquire all of
these samples.

If you can get by with sampling only a subset of this space and
approximate/reconstruct the rest with a mathematical model, yet yield
reasonable accuracy (wrt diagnosis or other criterion) relative to full
sampling, the MRI session will be a lot faster because you don't need to
acquire all the data you did before.

~~~
AboutTheWhisles
I'm not clear which scenario you are alluding to. It the expectation figuring
out only how to sparsely sample the same area or how to quickly sample the
larger area so that a detailed scan can be taken after that?

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lp251
The former.

It is known that we can reconstruct MR images at full fidelity- with _no_ loss
of information- by randomly sampling "k-space" at something like 10% of the
usual sampling rate. This leads to much faster acquisitions. I believe Siemens
has a product based on this technology that is currently going to market-
[https://usa.healthcare.siemens.com/magnetic-resonance-
imagin...](https://usa.healthcare.siemens.com/magnetic-resonance-imaging/mri-
technologies/speed-technologies/compressed-sensing)

One issue, though, is that truly random sampling isn't great from a practical
point of view. Sampling patterns are constrained by other equipment
considerations. There is also the issue of noise.

Machine learning for MR (and CT, and PET/SPECT, and...) is an active area of
research, eg
[https://arxiv.org/pdf/1705.06869.pdf](https://arxiv.org/pdf/1705.06869.pdf)

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dontreact
I think this is a dangerous research direction under-regulated by the FDA. In
order to get this sort of thing approved you just have to prove it doesn’t
affect the diagnosis of a small set of abnormalities. The power of these
models is enormous. They could potentially recognize and “smooth” out only
certain abnormalities and there is no real way to guarantee that they won’t do
that without testing it on all abnormalities.

~~~
dontreact
I just spent 20% of my time at RSNA arguing with people doing similar things
and everyone seems to be happy to jump over the FDA’s existing bar for
reconstruction algorithms. However previous reconstruction algorithms weren’t
universal function approximators with the potential to exhibit abnormality-
specific behavior.

We know very well these models have the capacity to recognize certain
abnormalities or learn to model the normal state of anatomy. There is also the
danger of the fact that deep learning powered reconstruction will not work
alongside a radiologist like other AI for medical imaging applications such as
nodule detection. This means we won’t find the problem with FDA’s low
regulatory bar until patients start dying.

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andbberger
This makes me so happy I've been looking for good MRI datasets...

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optymizer
A few years ago, we at Harvard have released a high quality data set of 1500
people with MRI images and behavioral information. You can get the dataset [1]
and/or read the Nature paper [2].

[1] [https://www.neuroinfo.org/gsp/](https://www.neuroinfo.org/gsp/)

[2]
[https://www.nature.com/articles/sdata201531](https://www.nature.com/articles/sdata201531)

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kakoni
Has someone done any work using ResNet architectures with mri/xray images?
Would be interested to here hows that working.

~~~
nl
Yes.

Plenty of Kaggle competitions in this space.

Generally UNet/RetiniaNet for finding the object of interest, then classifiers
built on ResNet (or variants) feature extractor works well.

Here's a good overview from the recent Pneumonia detection challenge (chest
XRay images): [https://www.kaggle.com/c/rsna-pneumonia-detection-
challenge/...](https://www.kaggle.com/c/rsna-pneumonia-detection-
challenge/discussion/70421)

