{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T10:21:03Z","timestamp":1791973263721,"version":"4.3.4"},"reference-count":48,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2016,1,27]],"date-time":"2016-01-27T00:00:00Z","timestamp":1453852800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["NMR in Biomedicine"],"published-print":{"date-parts":[[2016,4]]},"abstract":"<jats:p>Skeletal muscle inflammation\/necrosis and fat infiltration are strong indicators of disease activity and progression in many neuromuscular disorders. They can be assessed by muscle T<jats:sub>2<\/jats:sub> relaxometry and water\u2010fat separation techniques, respectively. In the present work, we exploited differences between water and fat T<jats:sub>1<\/jats:sub> and T<jats:sub>2<\/jats:sub> relaxivities by applying a bi\u2010component extended phase graph (EPG) fitting approach to simultaneously quantify the muscle water T<jats:sub>2<\/jats:sub> and fat fraction from standard multi\u2010slice multi\u2010echo (MSME) acquisitions in the presence of stimulated echoes. Experimental decay curves were adjusted to the theoretical model using either an iterative non\u2010negative least\u2010squares (NNLS) procedure or a pattern recognition approach. Twenty\u2010two patients (age, 49 \u00b1 18 years) were selected to cover a large range of muscle fat infiltration. Four cases of chronic or subchronic juvenile dermatomyositis (age, 8 \u00b1 3 years) were investigated before and 3 months following steroid treatment. For control, five healthy volunteers (age, 25 \u00b1 2 years) were recruited. All subjects underwent the MSME sequence and EPG fitting procedure. The EPG fitting algorithm allowed a precise estimation of water T<jats:sub>2<\/jats:sub> and fat fraction in diseased muscle, even in the presence of large B<jats:sub>1<\/jats:sub><jats:sup>+<\/jats:sup> inhomogeneities. In the whole cohort of patients, there was no overall correlation between water T<jats:sub>2<\/jats:sub> values obtained with the proposed method and the fat fraction estimated inside muscle tissues (R<jats:sup>2<\/jats:sup> = 0.02). In the patients with dermatomyositis, there was a significant decrease in water T<jats:sub>2<\/jats:sub> (\u20104.09 \u00b1 3.7 ms) consequent to steroid treatment. The pattern recognition approach resulted in a 20\u2010fold decrease in processing time relative to the iterative NNLS procedure. The fat fraction derived from the EPG fitting approach correlated well with the fat fraction derived from a standard three\u2010point Dixon method (\u22481.5% bias). The bi\u2010component EPG fitting analysis is a precise tool to monitor muscle tissue disease activity and is able to handle bias introduced by fat infiltration and B<jats:sub>1<\/jats:sub><jats:sup>+<\/jats:sup> inhomogeneities. 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