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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords