Partial Fourier (PF) acquisition allows to reduce TE in single-shot echo-planar imaging in order to increase signal-to-noise ratio (SNR) in diffusion-weighted imaging (DWI). However, when applying it to motion-prone liver DWI, conventional PF reconstruction methods fail since they rely on smoothness priors of the phase. This work proposes to use an unrolled network architecture which aims to estimate a more appropriate regularization by learned recurrent convolutions. It can be shown that reconstructions produced by the network are superior in terms of quantitative measures as well as qualitative impression compared to conventional methods which tend to introduce artifacts.
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