[alwa]

I can’t access the full paper, but from the abstract, is it accurate that they’re using ML techniques to synthesize higher-quality and higher-resolution imagery, and that’s the basis for their claim that it’s comparable to the output of a conventional MRI scan?

Do clinicians really prefer that the computer make normative guesses to “clean up” the scan, versus working with the imagery reflecting the actual measurements and applying their own clinical judgment?

[eig]

I can say that most radiologists would not want a computer trying to fix poor scan data. If the underlying data is bad, they would have recommend an orthogonal imaging abnormality. "I don't know" is a possible response radiologists can give. Trying to add training data to "clean up" an image would bias the read towards "normal".

[bone_slide]

Spot on. When I can't interpret a study due to artifact, I say that in my report.

Let's say there's a CTA chest that is limited because the patient breathed while the scan was being acquired, I need to let the ordering clinician know that the study is not diagnostic, and recommend an alternative.

If AI eliminates the artifact by filling in expected but not actually acquired data, I am screwed and the patient is screwed.

[falcor84]

To nitpick, wouldn't it by definition bias the read toward normal? I suppose the problem is more that you don't want to bias it to normal if it wasn't.

[mminer237]

The training data is going to have far more normal scans of any given part than it will abnormal.

[deepsun]

My understanding as well. That... will bias towards training data, and will miss more anomalies. And anomalies is the point of scanning.

[j7ake]

They already use computers to make guesses to clean up the scan.

A core part of processing MRI is the compressed sensing algorithm .