Conventional Magnetic Resonance Imaging (MRI) is a prolonged procedure. Therefore, it’s beneficial to reduce scan time as it improves patient experience and reduces scanning cost. While many approaches have been proposed for obtaining high quality reconstruction images using under-sampled k-space data, deep learning has started to show promising results when compared with conventional methods. In this paper, we propose a Variational Feedback Network (VFN) for accelerated MRI reconstruction. Specifically, we extend the previously proposed variational network with recurrent neural network (RNN). Quantitative and qualitative evaluations demonstrate that our proposed model performs superiorly against other compared methods on MRI reconstruction.
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