Keywords: Bone/Skeletal, Low-Field MRI
Motivation: There has been significant development in ultralow-field (ULF) MRI for low-cost, shielding-free, and point-of-care extremity applications. However, its image quality remains poor, and scan times are long.
Goal(s): We aim to advance the speed and quality of knee ULF MRI using 2D partial Fourier sampling and deep learning image formation.
Approach: A fast acquisition and deep learning reconstruction framework to accelerate knee MRI at 0.05 tesla was proposed.
Results: 3D deep learning leverages high-field knee anatomy data to enhance image quality, reduce artifacts and noise, and improve spatial resolution.
Impact: The method effectively overcomes the low-signal barrier, reconstructing fine anatomical structures at 0.05 Tesla that are reproducible within subjects and consistent across two protocols. It enables rapid, high-quality ULF MRI for potential point-of-care applications.
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