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Abstract #2744

Consensus ADMM for Distributed, Constrained Reconstruction with Low-Rank Subspace and Phase Priors

Mark Nishimura1, Daniel Abraham1, Congyu Liao2, Xiaozhi Cao2, Shreyas Vasanawala2, John Pauly1, and Kawin Setsompop2
1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States

Synopsis

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.

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Keywords