Keywords: Image Reconstruction, AI/ML Image Reconstruction
Motivation: Complex-valued neural networks have shown remarkable results in MR image reconstruction. However, these approaches have relied on extensions of real-valued activation functions to perform these activations, which might not be optimal.
Goal(s): To explore the effect of learning data-driven non-linear activation functions on the performance of complex-valued neural networks for MR image reconstruction.
Approach: We train networks using spline-based activations and compare them to networks using conventional complex-valued activation functions. Finally, we evaluate the effects of the new hyperparameters that learnable activation functions offer.
Results: Spline-based activation functions are superior to conventional activation functions while maintaining model robustness.
Impact: Spline-based complex-valued neural networks might improve image quality and enable further acceleration of MRI acquisitions. These results can better help to diagnose patients based on MRI exams while improving patient comfort by reducing the MRI acquisition time.
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