
GPUs a ‘Game Changer’ for Nuking Noise in Nuclear Imaging - bcaulfield
https://blogs.nvidia.com/blog/2019/03/22/mediso-medical-monte-carlo-gpus-noise-nuclear-imaging-gtc/
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PaulHoule
What I find funny about this is that there was a huge concern about image
compression algorithms introducing artifacts into medical images that would
affect diagnosis back in the 1980s, and even to this day.

Deep networks can learn to hallucinate rather well. I'm sure that they can
fill in what body structures are supposed to look like (they've seen them many
times) but not sure if they would help in diagnosis -- or if they would add
plausible-looking details that would breed complacency.

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robert_foss
bcaulfield: Are you a nVidia employee? You've been spamming 'groundbraking'
news about nVidia for days.

[https://news.ycombinator.com/submitted?id=bcaulfield](https://news.ycombinator.com/submitted?id=bcaulfield)

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CoolGuySteve
A quick google shows that he's the 'chief blogger' for NVidia.

IMO this kind of astroturfing shouldn't be allowed without some kind of
disclaimer even if the content is pretty interesting. There should at least be
something in his bio about it.

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dang
That's not astroturfing.

It's ok on HN to submit your own stuff as long as it's mixed with a variety of
other material that you ran across and personally found interesting.

Using HN _only_ to submit own material is less cool, but not something we
necessarily ban users for. The community tends to frown on and flag such
submissions though.

bcaulfield is obviously a legit HN user. The proportion is probably too
heavily on the Nvidia side, but that doesn't mean he should be hounded off HN
either.

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mhneu
This is just a way to calculate a statistical distribution over possible body
structures given an image. If the image is bad, the estimates will be bad.

Hopefully the probabilities of error are carefully quantified.

Edit: speeding up the MC estimates of absorbed dose, etc, (i.e. speeding the
treatment planning process) seems like a much more clear improvment.

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TTPrograms
The article explains that this is about better forward models for the
measurement process rather than priors on body structure. This makes sense for
systems with more complex measurement physics that common reconstructions
approximate (eg. CT, PET).

