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Abstract #0410

A Neural Network Application for Fast Simultaneous Muscle T2-Water and Fat Fraction Mapping from Multi-Spin-Echo Acquisitions

Marco Barbieri1, Melissa T. Hooijmans2, Garry E. Gold1,3, Feliks Kogan1, and Valentina Mazzoli1
1Radiology, Stanford University, Stanford, CA, United States, 2Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, Netherlands, 3Bioengineering, Stanford University, Stanford, CA, United States


Muscle T2 relaxometry can be used to monitor disease activity in neuromuscular disorders. Dictionary matching of multi-echo-spin-echo (MESE) data is the gold-standard method to estimate the T2 of the myocitic component (T2-water) because of its ability to correct for multiple confounding factors, but suffers from a high computational burden. This work proposes a neural network (NN) approach for fast muscle T2-water mapping with subject-specific T2-fat calibration to overcome computational limitations of the dictionary method. The method was validated in-vivo against the standard dictionary approach. The NN application outperformed the dictionary approach in computational resources (x140 faster) while retaining quantitative accuracy.

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