Keywords: Susceptibility/QSM, Quantitative Susceptibility mapping, Orientation-Adaptive, Latent Feature Editing, OA-iQSM
Motivation: The performances of most DL-QSM methods are limited to MRI phase data of pure-axial acquisition orientation.
Goal(s): In this work, we would like to propose a novel DL-based end-to-end neural network for QSM reconstruction from phase data of arbitrary dipole orientations.
Approach: A novel Latent Feature Editing (LFE) module to learn the encoding of acquisition orientation vectors and seamlessly integrate them into the latent features of deep networks to make them orientation-adaptive.
Results: Both simulated and in vivo experiments demonstrate that the proposed LFE module can result in desirable QSM images at arbitrary oblique head orientations.
Impact: This work introduces a new DL paradigm, allowing researchers to develop innovative QSM methods without requiring a complete overhaul of their existing architectures.
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