Agreement between region-of-interest- and parametric map-based hepatic proton density fat fraction estimation in adults with chronic liver disease. - Manning, Paul; Hamilton, Gavin; Wang, Kang; Park, Chulhyun; Hooker, Jonathan; Wolfson, Tanya; Gamst, Anthony; Haufe, William; Schlein, Alex; Middleton, Michael; Sirlin, Claude (Abdominal Radiology, 2017)
Purpose: To compare agreement between region-of-interest (ROI)- and parametric map-based methods of hepatic proton density fat fraction (PDFF) estimation in adults with known or suspected hepatic steatosis secondary to chronic liver disease over a range of imaging and analysis conditions. Materials and methods: In this IRB approved HIPAA compliant prospective single-site study, 31 adults with chronic liver disease undergoing clinical gadoxetic acid-enhanced liver magnetic resonance imaging at 3 T were recruited. Multi-echo gradient-echo imaging at flip angles of 10° and 50° was performed before and after administration of gadoxetic acid. Six echoes were acquired at successive nominally out-of-phase and in-phase echo times. PDFF was estimated with a nonlinear fitting algorithm using the first two, three, four, five, and (all) six echoes. Hence, 20 different imaging and analysis conditions were used (pre/post contrast x low/high flip angle x 2/3/4/5/6 echoes). For each condition, PDFF estimation was done in corresponding liver locations using two methods: a region-of-interest (ROI)-based method in which mean signal intensity values within ROIs were run through the fitting algorithm, and a parametric map-based method in which individual signal intensities were run through the fitting algorithm pixel by pixel. Agreement between ROI- and map-based PDFF estimation was assessed by Bland-Altman and intraclass correlation (ICC) analysis. Results: Depending on the condition and method, PDFF ranged from −2.52% to 45.57%. Over all conditions, mean differences between ROI- and map-based PDFF estimates ranged from 0.04% to 0.24%, with all ICCs ≥0.999. Conclusion: Agreement between ROI- and parametric map-based PDFF estimation is excellent over a wide range of imaging and analysis conditions.