Motion artefacts remain a common problem in MRI. Deep learning presents a solution for motion correction requiring no modifications to image acquisition. This work investigates incorporating multichannel MRI data for motion correction using a conditional generative adversarial network (cGAN). Correcting for motion artefacts in the single-channel images prior to coil combination improved performance compared to motion correction on coil-combined images. The model trained for simultaneous motion correction of multichannel data produced the worst result, likely a result of its limited modelling capacity (reduced due to memory limitations).
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