Purpose: The aim of the 2016 quantitative susceptibility mapping (QSM) reconstruction challenge was to test the ability of various QSM algorithms to recover the underlying susceptibility from phase data faithfully. Methods: Gradient-echo images of a healthy volunteer acquired at 3T in a single orientation with 1.06 mm isotropic resolution. A reference susceptibility map was provided, which was computed using the susceptibility tensor imaging algorithm on data acquired at 12 head orientations. Susceptibility maps calculated from the single orientation data were compared against the reference susceptibility map. Deviations were quantified using the following metrics: root mean squared error (RMSE), structure similarity index (SSIM), highfrequency error norm (HFEN), and the error in selected white and gray matter regions. Results: Twenty-seven submissions were evaluated. Most of the best scoring approaches estimated the spatial frequency content in the ill-conditioned domain of the dipole kernel using compressed sensing strategies. The top 10 maps in each category had similar error metrics but substantially different visual appearance. Conclusion: Because QSM algorithms were optimized to minimize error metrics, the resulting susceptibility maps suffered from over-smoothing and conspicuity loss in fine features such as vessels. As such, the challenge highlighted the need for better numerical image quality criteria.

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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 1.5T and 3T / 3D multi-echo 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 (SPURS) in healthy subjects (n=5). Reproducibility was assessed over 4 scanners at 2 field strengths from 2 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 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 approximately 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). 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.