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Abstract #1343

A hybrid EPG-hGLLiM method for accurate fat-signal modeling in skeletal muscle T2 mapping

Pierre-Yves Baudin1,2, Ericky Caldas de Almeida Araujo1,2, Harmen Reyngoudt1,2, and Benjamin Marty1,2
1NMR Laboratory, Institute of Myology, Neuromuscular Investigation Center, Paris, France, 2NMR Laboratory, CEA, DRF, IBFJ, MIRCen, Paris, France


In order to improve the reliability of the muscle T2 as a clinical outcome in clinical studies of neuromuscular disorders, we investigate the accuracy of the fat signal modeling in transverse relaxometry from multi-spin echo imaging. A new approach for muscle T2/fat-fraction mapping is proposed, combining a water signal dictionary with water T2/B1-dependent entries created from EPG simulations, and a hybrid Gaussian Locally Linear Mapping (hGLLiM) to provide B1-dependent fat signals. Preliminary results on a dataset of healthy controls, DMD and IBM patients are consistent with common knowledge, but interesting divergences are found when compared to another recent approach.

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