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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