Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence
Motivation: High-resolution arterial spin labeling (ASL) imaging is time-consuming, limiting its clinical applications in studying small brain structures.
Goal(s): To reconstruct high-resolution ASL images from 8-time accelerated ASL image acquisition, an under-sampled non-Cartesian k-space sampling.
Approach: We proposed an attention-based deep learning (DL) model.
Results: The proposed DL model can successfully reconstruct 8-fold under-sampled, non-cartesian, multi-coil data from k-space.
Impact: Our proposed attention-based deep learning model can reconstruct under-sampled non-cartesian multi-coil data in k-space and thereby significantly decrease long MRI acquisition time required for high-resolution ASL MRI imaging, which may enable clinical applications in studying small brain structures.
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