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Abstract #3246

No-Reference Quality Assessment of MRIs for Clinical Application

Ke Lei1, Shreyas Vasanawala2, and John Pauly1
1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States

We proposed a CNN model that automatically assesses image quality within seconds after a scan is finished to reduce the number of patient recalls and inadequate images. Our model is deployed to the clinics where it alerts technicians to take action for low-quality images while the patient is still in the scanner. Our model achieves super-human performance on assessing perceptual noise level in 2D fast spin echo (FSE) MRIs. It can also be used to automatically guide other computational processes, like training of a denoising model or choice of a regularization weight for reconstruction.

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