Rapid automated liver quantitative susceptibility mapping. Issue 3 (13th January 2019) - Jafari, Ramin; Sheth, Sujit; Spincemaille, Pascal; Nguyen, Thanh D.; Prince, Martin R.; Wen, Yan; Guo, Yihao; Deh, Kofi; Liu, Zhe; Margolis, Daniel; Brittenham, Gary M.; Kierans, Andrea S.; et al.
Abstract : Background: Accurate 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. Purpose: To develop a rapid, robust, and automated liver QSM for clinical practice. Study Type: Prospective. Population: 13 healthy subjects and 22 patients. Field Strength/Sequences: 1.5 T and 3 T/3D multiecho gradient‐recalled echo (GRE) sequence. Assessment: Data 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 Tests: IP 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). Results: Liver 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 Conclusion: Use 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 Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:725–732.