Abstract #1052

A data-driven method for automatic regularization selection in a hybrid DL-SENSE reconstruction

Zahra Hosseini¹, Thorsten Feiweier², John Conklin³, Stephan Kannengiesser², Marcel Dominik Nickel², Min Lang³, Azadeh Tabari³, Augusto Lio Concalves Filho³, Wei-Ching Lo⁴, Maria Gabriela Figueiro Longo³, Michael Lev³, Pamela Schaefer³, Otto Rapalino³, Susie Huang³, Stephen Cauley⁵, and Bryan Clifford⁴

¹MR R&D Collaboration, Siemens Medical Solutions USA, Atlanta, GA, United States, ²Siemens Healthcare GmbH, Erlangen, Germany, ³Department of Radiology, Massachusetts General Hospital, Boston, MA, United States, ⁴MR R&D Collaboration, Siemens Medical Solutions USA, Boston, MA, United States, ⁵Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States

Synopsis

The integration of deep learning priors into regularized CG-SENSE reconstructions enables high quality MR images to be generated from noisy, undersampled data. The regularization parameter in these methods can be tuned to control the level of denoising, allowing a network to generalize to novel SNR conditions without retraining. However, manual tuning of the regularization parameter can be time consuming. This work presents a data-driven method for automatic regularization selection using commonly acquired noise calibration data. Results indicate the method generalizes across clinically relevant imaging scenarios and provides diagnostically equivalent image quality to that obtained by manual parameter tuning.

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.

Click here for more information on becoming a member.