And yet, for the reasons I stated, this is exactly what FastMRI aims to do. Speed up the scan. There will be no way for Radiologists to oversee the reconstruction and make sure subtle abnormalities are preserved.
There is just too much redundancy in MRI data, and initiatives such as FastMRI are fundamental for us to learn what the limits are of feasible acceleration.
Also, some MRI scans take forever and cannot be used in vulnerable populations because of, e.g., breath holds, the need to stand still, etc. The image quality, perhaps counter-intuitively, in some situations improves with acceleration.
Can you explain why mapping from raw space gets rid of any of the concerns I raised?
It’s interesting research for sure. I hope it stays far away from actual clinical use for a while, for the reasons I highlighted. I’d like to see convnets work alongside radiologists for a while and prove robustness to dcanner changes in the wild before we start shoving them deep in the stack where radiologists can’t review what’s happening.
Radiologists don’t usually oversee reconstruction currently even when it’s real-time (whatever that means in MR, generally it means immediately after acquisition).
Exactly and that’s why this is not a good first place for convnets to be used in the workflow of a radiologist. They should be working alongside the radiologist, not somewhere where it’s impossible for the radiologist to examine what happened.
Yes we do (I’m and MR tech) and it’s a part of trouble shooting on GE scanners. There is also a weird bug on a release I use of the Philips platform that allows a visualisation too.
Working with the data in this form is not an everyday thing but it is useful.
K-space data is also saved for reconstructions and processing later on, though everyone prefers to avoid that as it’s horrible and lots of storage is required.
I’ve also worked at a university site where the raw data was collected and used on a daily basis, but that is presumably less common.
