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Comment by 01acheru | Hacker News Reader

Can’t wait to do an MRI and hear the doc say “You’re all set, good to go!”, only to discover that I actually had a tumor but that really clever ML algorithm thought that it was noise and should’ve been smoothed out…

I don’t want to be part of it, thanks

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I am surprised you are so certain in this statement. There is always a tradeoff between scan time and image quality. Clinical scans often have thick slices to keep scan times reasonable. Using advanced reconstruction methods, e.g. ML, you can get thinner slices in the same scan time. How would you balance the benefit of getting higher resolution in the same time as a standard lower resolution scan time if the higher resolution scan was regularized with a neural network? The doctor might miss small tumors due to low resolution too. I understand your concern, but I wouldn't dismiss it so outright.

Note, I am biased because I research MRI acquisition and reconstruction methods and I am rolling out trials of fast MRI methods (that use some ML in the reconstruction) to find out how robust the methods actually are in practice.

Well, I really hope you succeed on your experiments, but right now it seems to me that ML introduces an error factor we still cannot precisely account for.

It looks like you are proposing some kind of mixed approach, not a simple “less data, faster scan, ML to the rescue”. I understand how MRI works but you are surely and obviously much more knowledgeable than me, so I simply wish you luck!

My problem with the article comes from reading that someone telling about using generative models on health data, I don’t think is time for this yet.

Thanks! I agree taht generative models on their own are definitely more risky than methods that combine ML for regularization along with data consistency terms that force the reconstructions to be consistent with the acquired data.

My opinion is pretty much worthless but I think this is a much more sensible approach, using the strengths of ML but putting constraints on the outputs.

Just a simple question: to achieve a guarantee that those results are at least equal or better than the ones we have now on our battle tested setups, shouldn’t we use the same sampling we use on a “default” MRI? I mean… using those reconstruction algorithms to try to achieve a better result, without downsampling so that a standard reconstruction can still be performed to be checked against?

"Default MRI" (i.e. fully sampled) should definitely be acquired when possible when testing this out to compare to gold standard. But the benefit of using ML methods in the fully sampled case would be minimal (maybe some denoising), whereas they have a much larger effect when acquiring highly undersampled data that traditional reconstruction methods fail at. It's also not always possible to get fully sampled reference data. For example in functional MRI you might not be able to get matched fully sampled data because the benefit of undersampling is in improving the temporal resolution. These cases are definitely more researchy and less clinical though, and in my work we add a 2 minute highly undersampled scan to current standard protocols and compare what we can reconstruct from our 2 minute scan compared with the fully sampled (but often lower resolution) standard scans.

> "Default MRI" (i.e. fully sampled) should definitely be acquired when possible when testing

It seems unlikely you wouldn’t appreciate this already, but clinical MRI has not fully sampled in a long time. Between the old and the new - reduced phase resolution (image plane and slice plane) parallel imaging, compressed sense (or sensing), reduced frequency resolution with partial echo techniques, high reconstruction max trim with low acquisition matrix, the list is quite long.

The changes in resulting artefacts as acceleration techniques change (eg high compressed sense values) is a bit of a change to how people work. Very digital looking artefacts are just gross.

Thanks for your work! We need more speed.

Thanks for the comment! You're right, I was oversimplifying saying that default MRI would be fully sampled. My main point still stands, you can't just chuck ML onto current protocols to do a direct comparison with ML and more conventional reconstruction methods to give clinicians access to both and expect an improvement (beyond potentially denoising) because the conventional scans are already very good at what they do. Where ML can help is in cases where we can't produce conventional scans (e.g. with very short scan times or high temporal resolution).

Those striving for shorter scan times (eg functional) go to such massive lengths that it blew my mind when I encountered it on a research magnet. Every millisecond counted.

Watching the mental gymnastics used to deal with multi band/simultaneous multi slice (or whatever vendors call it) with functional MRI was impressive to me.

Using ML to work out voxel results in such low spacial resolution scans has got to be scary.

You'll be surprised how many well-known doctors miss a ton of non-obvious anomalies in scan results. There's no way a doctor would have seen all prior records of confirmed diagnosis and their corresponding scans.

In an ideal world, a deep learning algorithm should provide an independent report of potential features of interest to a doctor to let him know if something that he could have missed. However, I hope it stays in the intended role and doesn't make the doctor less careful.

I totally agree with you on this one, but it is not the topic at hand. The article is not about using ML for feature detection, but on enhancing subsampled data.

I actually find what you are saying it to be a much better usage of ML in this sector.

Why would the ML algorithm necessarily change the scan. The radiologist could still look at the unadulterated MRI.

There is no unadulterated result, you are doing less sampling and relying on ML to fill the gaps. So you either have the ML reconstructed result or a subsampled MRI.

Healthcare is an area where we need good and clean data as much as possible, let’s use ML reconstruction somewhere else.

Interesting. Would love to see an example of a tumor so small a radiologist could see it but that a ML algorithm would smooth out

It is not about big or small, I don’t think you understand how ML work.

And by the way, tumors can be really small.

Out of interest, do you understand how MRI reconstruction works?

If your question is literally “you understand how it works?” the answer is yes, I do.

If your question is more nuanced to mean “do you really really know how it works, meaning you could work on it tomorrow?” the answer is no, it is not my field.

Not obvious the human chosen function for reconstruction is necessarily better than the ML one. The human function doesn't save all the data either.

A lot of the problem comes from the use of generative neural networks. If the prior is that the reconstructed images should "look" a certain way, then the algorithm will favor that. Some of our colleagues did early work with DL and got scared off of generative models due to finding issues with nonphysical results (read: broken layers of cortex in the brain, completely non-physical anatomy) that these models can generate from the undersampled raw data.

That said, there are other great ways to incorporate DL into MRI other than recon. I'm more interested in the use of DL for image segmentation, feature detection, potentially denoising, or other techniques on the image processing side. Those make a lot more sense as "top down" tasks that are well suited for neural networks.

There are definitely ways to work well with subsampled data, see Lester Mackey's recent work.

Could please share a link to the work you are referring to? I would really appreciate it (not ironically, it would be truly appreciated).

I know we can work around subsampling, actually we have always been very good at it since our sampling data back in the day was way smaller than what we refer as subsampled today.

http://stanford.edu/~lmackey/papers/thinning-slides.pdf

Thanks!

MRI is completely adulterated at every stage. Algorithms and filters make the final result palatable. The raw data is a k-space data file. It’s not really human readable (though you can spot noise spikes etc).

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