Keywords: Arterial Spin Labelling, Arterial spin labelling
Motivation: Weighted delay methods and mean squared error (MSE) fitting have limited capabilities in estimating ATT and CBF. The performance of deep learning models is constrained by the differences in orders of parameters within loss functions.
Goal(s): To develop a novel loss function to overcome the limitations of MSE in estimating ATT and CBF.
Approach: A CRLB-informed MSE was proposed. The accuracy of CBF and ATT was compared on a 3D CNN trained with MSE loss and CRLB-informed MSE loss. The performance of the proposed model was assessed on ASL with reduced PLDs and repetitions.
Results: CRLB-informed MSE loss significantly improved CBF estimation.
Impact: A CNN model with CRLB-informed MSE loss offered improved accuracy and robustness in CBF estimation and in handling outliers, which may provide an efficient method to quantify ASL images.
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