• Ramin Jafari, Sujit Sheth, Pascal Spincemaille, Thanh D Nguyen, Martin R Prince, Yan Wen, Yihao Guo, Kofi Deh, Zhe Liu, Daniel Margolis, Gary M Brittenham, Andrea S Kierans, and Yi Wang.
• Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.
• J Magn Reson Imaging. 2019 Sep 1; 50 (3): 725-732.

BackgroundAccurate measurement of the liver iron concentration (LIC) is needed to guide iron-chelating therapy for patients with transfusional iron overload. In this work, we investigate the feasibility of automated quantitative susceptibility mapping (QSM) to measure the LIC.PurposeTo develop a rapid, robust, and automated liver QSM for clinical practice.Study TypeProspective.Population13 healthy subjects and 22 patients.Field Strength/Sequences1.5 T and 3 T/3D multiecho gradient-recalled echo (GRE) sequence.AssessmentData were acquired using a 3D GRE sequence with an out-of-phase echo spacing with respect to each other. All odd echoes that were in-phase (IP) were used to initialize the fat-water separation and field estimation (T2 *-IDEAL) before performing QSM. Liver QSM was generated through an automated pipeline without manual intervention. This IP echo-based initialization method was compared with an existing graph cuts initialization method (simultaneous phase unwrapping and removal of chemical shift, SPURS) in healthy subjects (n = 5). Reproducibility was assessed over four scanners at two field strengths from two manufacturers using healthy subjects (n = 8). Clinical feasibility was evaluated in patients (n = 22).Statistical TestsIP and SPURS initialization methods in both healthy subjects and patients were compared using paired t-test and linear regression analysis to assess processing time and region of interest (ROI) measurements. Reproducibility of QSM, R2 *, and proton density fat fraction (PDFF) among the four different scanners was assessed using linear regression, Bland-Altman analysis, and the intraclass correlation coefficient (ICC).ResultsLiver QSM using the IP method was found to be ~5.5 times faster than SPURS (P < 0.05) in initializing T2 *-IDEAL with similar outputs. Liver QSM using the IP method were reproducibly generated in all four scanners (average coefficient of determination 0.95, average slope 0.90, average bias 0.002 ppm, 95% limits of agreement between -0.06 to 0.07 ppm, ICC 0.97).Data ConclusionUse of IP echo-based initialization enables robust water/fat separation and field estimation for automated, rapid, and reproducible liver QSM for clinical applications.Level Of Evidence1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:725-732.© 2019 International Society for Magnetic Resonance in Medicine.

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