Abstract

This study evaluates the impact of liver steatosis on the discriminative ability for liver fibrosis and inflammation using a novel Dixon water-only fat-corrected Look-Locker T1 mapping sequence, compared with a standard shortened Modified Look-Locker Inversion Recovery (shMOLLI) sequence, with the aim of overcoming the limitation of steatosis-related confounding in liver T1 mapping. 3 T magnetic resonance imaging of the liver including the 2 T1 mapping sequences and proton density fat fraction (PDFF) was prospectively performed in 24 healthy volunteers and 38 patients with histologically proven liver fibrosis evaluated within 90 days of liver biopsy. Paired Mann-Whitney test compared sequences between participants with and without significant liver steatosis (PDFF cutoff 10%), and unpaired Kruskal-Wallis test compared healthy volunteers to patients with early (F0-2) and advanced (F3-4) liver fibrosis, as well as low (A0-1) and marked (A2-3) inflammatory activity. Univariate and multivariate logistic regression models assessed the impact of liver steatosis on both sequences. Dixon_W T1 was higher than shMOLLI T1 in participants without steatosis (median 896 ms vs 890 ms, P = 0.04), but lower in participants with liver steatosis (median 891 ms vs 973 ms, P < 0.001). Both methods accurately differentiated between volunteers and patients with early and advanced fibrosis (Dixon_W 849 ms, 910 ms, 947 ms, P = 0.011; shMOLLI 836 ms, 918 ms, 978 ms, P < 0.001), and those with mild and marked inflammation (Dixon_W 849 ms, 896 ms, 941 ms, P < 0.01; shMOLLI 836 ms, 885 ms, 978 ms, P < 0.001). Univariate logistic regression showed slightly lower performance of the Dixon_W sequence in differentiating fibrosis (0.69 vs 0.73, P < 0.01), compensated by adding liver PDFF in the multivariate model (0.77 vs 0.75, P < 0.01). Dixon water-only fat-corrected Look-Locker T1 mapping accurately identifies liver fibrosis and inflammation, with less dependency on liver steatosis than the widely adopted shMOLLI T1 mapping technique, which may improve its predictive value for these conditions.

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