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Abstract #0929

Model-Based Single-Shot EPI Reconstruction with Sparsity Regularization

Uten Yarach1,2, Matt A Bernstein1, John Huston III1, Myung-Ho In1, Daehun Kang1, Yunhong Shu1, Erin Gray1, Nolan Meyer1, and Joshua D Trzasko1

1Department of Radiology, Mayo Clinic, Rochester, MN, United States, 2Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand

Echo planar imaging (EPI) is widely used clinically for its speed, but is known to be sensitive to non-idealities like B0 field inhomogeneity, eddy currents, and gradient nonlinearity. Such non-idealities are not typically managed during image reconstruction, resulting in geometrically distorted images. Post-processing corrections (e.g., image-based interpolation) usually tend to degrade resolution. In this work, a comprehensive model-based reconstruction framework that prospectively and simultaneously accounts for non-idealities in accelerated single-shot EPI acquisitions is proposed. Sparsity regularization is also incorporated to mitigate noise amplification. The proposed algorithm is demonstrated on brain MRI data acquired on a compact 3T MRI system.

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