Non-contrast cardiovascular magnetic resonance detection of myocardial fibrosis in Duchenne muscular dystrophy

Background Duchenne muscular dystrophy (DMD) leads to progressive cardiomyopathy. Detection of myocardial fibrosis with late gadolinium enhancement (LGE) by cardiovascular magnetic resonance (CMR) is critical for clinical management. Due to concerns of brain deposition of gadolinium, non-contrast methods for detecting and monitoring myocardial fibrosis would be beneficial. Objectives We hypothesized that native T1 mapping and/or circumferential (epsilon(cc)) and longitudinal (epsilon(ls)) strain can detect myocardial fibrosis. Methods 156 CMRs with gadolinium were performed in 66 DMD boys and included: (1) left ventricular ejection fraction (LVEF), (2) LGE, (3) native T1 mapping and myocardial tagging (epsilon(cc-tag) measured using harmonic phase analysis). LGE was graded as: (1) presence/absence by segment, slice, and globally; (2) global severity from 0 (no LGE) to 4 (severe); (3) percent LGE using full width half maximum (FWHM). epsilon(ls) and epsilon(cc) measured using feature tracking. Regression models to predict LGE included native T1 and either epsilon(cc-tag) or epsilon(ls) and epsilon(cc) measured at each segment, slice, and globally. Results Mean age and LVEF at first CMR were 14 years and 54%, respectively. Global epsilon(ls) and epsilon(cc) strongly predicted presence or absence of LGE (OR 2.6 [1.1, 6.0], p = 0.029, and OR 2.3 [1.0, 5.1], p = 0.049, respectively) while global native T1 did not. Global epsilon(cc), epsilon(ls), and native T1 predicted global severity score (OR 2.6 [1.4, 4.8], p = 0.002, OR 2.6 [1.4, 6.0], p = 0.002, and OR 1.8 [1.1, 3.1], p = 0.025, respectively). epsilon(ls) correlated with change in LGE by severity score (n = 33, 3.8 [1.0, 14.2], p = 0.048) and epsilon(cc-tag) correlated with change in percent LGE by FWHM (n = 34, OR 0.2 [0.1, 0.9], p = 0.01). Conclusions Pre-contrast sequences predict presence and severity of LGE, with epsilon(ls) and epsilon(cc) being more predictive in most models, but there was not an observable advantage over using LVEF as a predictor. Change in LGE was predicted by epsilon(ls) (global severity score) and epsilon(cc-tag) (FWHM). While statistically significant, our results suggest these sequences are currently not a replacement for LGE and may only have utility in a very limited subset of DMD patients.