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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