Keywords: Image Reconstruction, Image ReconstructionModel-based deep learning algorithms offer high quality reconstructions for accelerated acquisitions. Training the regularization parameter λ can lead to instabilities during training. In this work, we evaluated effect of fixing λ parameter while training. We observed no difference in image quality when the network was trained with a fixed λ parameter when the fixed value was equal to the value learned from training. We observed that IQ is dependent on the fixed λ values used during training. Furthermore, we observed that tuning the λ parameter during inference adapts the framework to the SNR of the testing dataset, yielding improved performance.
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