Inspired by recent usage of Deep Image Prior (DIP) in the field of MRI that utilizes a powerful low-level image prior from a neural network architecture itself without any training dataset, we conduct k-space extrapolation using the deep prior for Gibbs-ringing removal in order to build general Gibbs-ringing correction algorithm without dependency on the dataset. We further improved the existing deep prior with the addition of anti-aliasing layers. The proposed deep prior method outperformed conventional non-learning methods quantitively and qualitatively in numerical simulations and in-vivo data.
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