Convolutional Neural Networks (CNNs) are highly effective tools for image reconstruction problems. Typically, CNNs are trained on large amounts of images, but, perhaps surprisingly, even without any training data, CNNs such as the Deep Image Prior and Deep Decoder achieve excellent imaging performance. Here, we build on those works by proposing an un-trained CNN for accelerated MRI along with performance-enhancing steps including enforcing data-consistency and combining multiple reconstructions. We show that the resulting method i) achieves reconstruction performance almost on par with baseline as well as state-of-the-art trained CNNs, but without any training, and ii) significantly outperforms competing sparsity-based approaches.
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