Motion‐robust, high‐SNR liver fat quantification using a 2D sequential acquisition with a variable flip angle approach
- 3 April 2020
- journal article
- research article
- Published by Wiley in Magnetic Resonance in Medicine
- Vol. 84 (4) , 2004-2017
Abstract
Purpose: Chemical shift encoded (CSE)‐MRI enables quantification of proton‐density fat fraction (PDFF) as a biomarker of liver fat content. However, conventional 3D Cartesian CSE‐MRI methods require breath‐holding. A motion‐robust 2D Cartesian sequential method addresses this limitation but suffers from low SNR. In this work, a novel free breathing 2D Cartesian sequential CSE‐MRI method using a variable flip angle approach with centric phase encoding (VFA‐centric) is developed to achieve fat quantification with low bias, high SNR, and minimal blurring.Methods: Numerical simulation was performed for variable flip angle schedule design and preliminary evaluation of VFA‐centric method, along with several alternative flip angle designs. Phantom, adults (n = 8), and children (n = 27) were imaged at 3T. Multi‐echo images were acquired and PDFF maps were estimated. PDFF standard deviation was used as a surrogate for SNR.Results: In both simulation and phantom experiments, the VFA‐centric method enabled higher SNR imaging with minimal bias and blurring artifacts. High correlation (slope = 1.00, intercept = 0.04, = 0.998) was observed in vivo between the proposed VFA‐centric method obtained PDFF and reference PDFF (free breathing low‐flip angle 2D sequential acquisition). Further, the proposed VFA‐centric method (PDFF standard deviation = 1.5%) had a better SNR performance than the reference acquisition (PDFF standard deviation = 3.3%) with P < .001.Conclusions: The proposed free breathing 2D Cartesian sequential CSE‐MRI method with variable flip angle approach and centric‐ordered phase encoding achieved motion robustness, low bias, high SNR compared to previous 2D sequential methods, and low blurring in liver fat quantification.Keywords
Funding Information
- National Institutes of Health (UL1‐TR000427, R41‐EB025729, R01‐DK117354, R01‐DK100651, K24‐DK102595, R01‐DK083380, R01‐DK088925)
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