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.