Keywords: Image Reconstruction, Image Reconstruction
Motivation: Spatiotemporally-undersampled sequences help to accelerate scans and push resolution limits. However, reconstruction of this data is slow and memory-intensive.
Goal(s): We aim to accelerate such reconstructions by splitting our objective into independent components and parallelizing the optimization across multiple compute devices.
Approach: We implement fast, GPU-accelerated proximal operators for data consistency, Locally-Low-rank, and S-LORAKS. We use a technique called Consensus ADMM to fuse them together in a distributed fashion.
Results: Our reconstruction enables reconstruction of high-resolution 3D magnetic resonance fingerprinting datasets and quantitative maps quickly and with good accuracy.
Impact: By adding more sophisticated prior knowledge to the reconstruction, we can further accelerate the scan, shortening scan times while maintaining quality.Our techniques are also quite general and apply broadly to a wide variety of reconstruction problems.
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