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Abstract #3570

No-Reference Assessment of Perceptual Noise Level Defined by Human Calibration and Image Rulers

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

We propose accessing the MRI quality, perceptual noise level in particular, during a scan to stop it when the image is good enough. A convolutional neural network is trained to map an image to a perceptual score. The label score for training is a statistical estimation of error standard deviation calibrated with radiologist inputs. Image rulers for different scan types are used in the inference phase to determine a flexible classification threshold. Our proposed training and inference methods achieve a 89% classification accuracy. The same framework can be used to tune the regularization parameter for compressed-sensing reconstructions.

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