k-space deep learning (DL) is emerging as an alternative to the conventional image domain DL for accelerated MRI. Typically, DL requires training on large amounts of data, which is unaccessible in clinical. This paper proposes to present an untrained k-space deep generative model (DGM) to interpolate missing data. Specifically, missing data is interpolated by a carefully designed untrained generator, of which the output layer conforms the MR image multichannel prior, while the architecture of other layers implicitly captures k-space statistics priors. Furthermore, we prove that the proposed method guarantees enough accuracy bounds for interpolated data under commonly used sampling patterns.
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