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.
The question is rather: does this feature improve diagnoses? Sure, the images look nicer now. But that's not why they are being created. MRI images are made for inspection by trained radiologists who are already filtering out artifacts. So is this tool better at this job, or does it actually worsen the ability of the radiologists to read the images like those xerox scans?
Maybe I'm a bit paranoid, idk. After all, diffusion MRI is already being used for surgical planning even though it has several shortcomings. But in that instance there are probably no good alternatives, while here the alternative is the trained eye of a radiologist.
They didn't perform best on the types they liked best. This wasn't a great study in terms of power, but it was interesting.
I've met plenty of rad-oncs and radiologists who are convinced they can "read through the noise" just fine, and want consistent imaging more than artifact reduction. I'm not sure how empirically this has ever been tested.
The advantages they gave were in every other way (physical storage, availability, duplication, speed at which they could be accessed etc).
The issue was, radiologist had to deal with a choice of different post-processing of this data. The processing they said they liked best (somewhat consistently) was not the processing that they performed best on, empirically (somewhat consistently).
This is related to the issue of evaluating the value of ML post processing, we could see a similar effect there. After all one school of thought was that preference was in some sense driving by familiarity rather than what they were actually able to discriminate.
FWIW IQ evaluation in MRI is a somewhat problematic thing anyway, but acceleration certainly tends to make it worse in some ways. It's not obvious how effective various mitigation approaches are.