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Abstract #2904

High-Resolution T1 Mapping using Parameter-Free Low Rank Denoising

Sebastian Weingärtner1,2,3, Steen Moeller2, Chetan Shenoy4, and Mehmet Akcakaya1,2

1Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States, 2Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States, 3Computer Assisted Clincial Medicine, Heidelberg University, Mannheim, MA, Germany, 4Department of Cardiology, University of Minnesota, Minneapolis, MN, United States

Myocardial T1 mapping has become increasingly established for tissue characterization in numerous cardiomyopathies. However, the commonly used end-diastolic single-shot imaging imposes restrictions on the spatio-temporal resolution. In this work, we explored increased parallel imaging accelerations and higher resolutions, in conjunction with an image denoising technique that exploits inter-dependencies between the multiple images using random matrix theory. Following parallel imaging reconstruction, common noise characteristics across the images are extracted from the singular value decomposition of a Gaussian random matrix and denoised using locally low-rank regularization. Application of this technique to SAPPHIRE T1 mapping shows no corruption of the T1 time and enables parallel imaging acceleration up to 4 with an in-plane resolution of 1.1x1.1mm2 at clinical image quality.

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