A physician's $0.02 - The clinical relevance of FB's work is clearly stated in the blog post: "While state-of-the-art facilities today use 3 Tesla MRI machines, scanners with lower-strength magnets (1.5 Tesla, for example) are still commonly used around the world." Considering that a 1.5T MRI machine costs about $1M less than a comparable 3T model (+/- the cost of warranty, support, and installation), FB's work in this area has the potential to make a BIG positive impact on the lives of millions of patients. Which is why I will be cheering them on.<p>If they reproduce their results in other clinical settings, the immediate impact on patient care includes:
1) accelerating diagnosis (and treatment) for patients with traumatic brain injuries (by effectively up-scaling lower resolution scans)
2) healthcare providers in developing countries will effectively get a low-cost "upgrade" to their existing equipment
3) cancer patients in rural America could be monitored for treatment response in a setting that is closer to home (because rural communities tend to be resource-poor in terms of medical technology).<p>If we consider that a logical extension of their work could be to develop a compression algorithm for MRI data, then it's easy to see an even broader impact that includes: 1) connecting rural patients with high-quality radiologist services (i.e. remote MRI interpretations), and
2) decrease the cost of long-term storage, access, and retrieval for MRI data.<p>On the topic of FB's issues with privacy: I agree that FB has a long way to earn my trust as a doctor and a patient. That being said, it's important to give credit where credit is due. It seems that FB gained access to the imaging data by working collaboratively with NYU on this specific project. By comparison, it's an open secret among those of us in the biomedical informatics community that over the course of many years Google Cloud has quietly gained access to the personal health information of millions of Americans. So, when it comes to privacy concerns, it's important to avoid being myopic - the concern is valid, but the primary threat may not be as obvious as it first seems.
A lot of people here are rightly concerned about the dangers of falsely marking something as an artifact, but let me present additional data that will hopefully sway you a little bit...<p>If you need an MRI or a CT of an area adjacent to orthopedic implants, you are currently 100% SOL because distortion or reflection artifacts from the metal completely destroy the imagery across a medically significant distance. There are computational filtering techniques for reducing these artifacts, but, respectfully, they are still really terrible, and close to the implants you can't see shit. All advancements in this area short of inventing new imaging physics will most likely be purely computational corrections. Consider that.
I'm no fan of this. What if it treats a tumor as an artifact? This reminds me of the xerox scandal about broken OCR that erroneously deduplicated parts of images that had different contents.<p>This module might work well, but the modules by cheap competitors might have such behaviour, and it's extremely hard to test that an implementation is bug free.
Please don’t make sweeping, generalizing opinions on the implications of the work. It’s a subjective problem to solve, so if are not a radiologist who has first-hand experience with this issue, stop.<p>Here are the results from the paper:<p>The radiologists ranked our adversarial approach as better than the standard and dithering approaches with an aver-
age rank of 2.83 out of a possible 3. This result is statisti- cally significantly better than either alternative with p-values 1.09 × 10−11 and 2.18 × 10−11 respectively, and the adver- sarial approach was ranked as the best or tied for best in 85.8% of 120 total evaluations (95% CI: 0.78-0.91). The dithering approach is also statistically significantly better than the standard approach.
We also asked radiologists if banding was present (in any form) in the reconstructions in each case. This evaluation is highly subjective, as “banding” is hard to define in a pre- cise enough way to ensure consistency between evaluators. Considering each radiologist’s evaluation independently, on average banding is still reported to be present in 72.5% (95% CI: 0.62-0.82) of cases even with the adversarial learn- ing penalty. The radiologists were not consistent in their rankings; the overall percentages reported by the six radiol- ogists were 20%, 75%, 75%, 80%, 85%, and 100% for the adversarial reconstructions. In contrast, for the baseline and dithered reconstructions, only one radiologist reported less than 100% presence of banding for each method (80% and 85% presence respectively, from different radiologists).
We believe these numbers could be improved if more tuning went into the model; however, it’s also possible that features of the sub-sampled reconstructions generally may be con- fused with banding, and so any method using sub-sampling might be considered by radiologists as having banding. Sub- sampled reconstructions generally have cleaner regional boundaries and lower noise levels than the corresponding ground-truth.
Even without anything fancy, is there a speed vs clarity parameter(s) when doing an MRI? It seems an easy improvement would be to spend more time getting a clear picture of the specific area of interest, vs now where the whole scan seems to be done at full clarity.
No thanks. If it can remove artifacts, it can also introduce them. Nobody should be using this on patients. This is a straightforward misapplication of AI.
Isn't this basically SRGAN?<p>Edit: sorry I guess since there's an explicit rotation module it's closer to SRGAN+deformable convolutions.
Facebook has absolutely no reason to be doing work with healthcare. Sure they have great computing power and top engineering talent to figure out how to sell more ads, but the trade-off for any educational facility to freely hand over medical data (de-identified or not) is wreckless.
I just gave a talk on similar work being done in microscopy: <a href="https://leapmanlab.github.io/nihai/jan20/" rel="nofollow">https://leapmanlab.github.io/nihai/jan20/</a> .<p>The tl;dr (in microscopy but apparently also in mri) is AI imaging can evidently enable new concrete solutions to intractable imaging problems, but the failure modes are really treacherous. The example on slide 39, taken from another excellent review paper, does a great job illustrating the problem. I think these methods will get more trustworthy, but i wouldn't stake my life (or my paper's prestigious research results) on them at the moment.
This is a bad idea, neural nets upscale by "hallucinating" in the details. That's fine for videos for entertainment, not for medical imaging.<p>And this is distinctly different from compressed sensing which uses a high frequency and mathematical basis.
Does anyone know how this differs from what Subtle Medical is doing? <a href="https://subtlemedical.com/" rel="nofollow">https://subtlemedical.com/</a>