• Christian Langkammer, Ferdinand Schweser, Karin Shmueli, Christian Kames, Xu Li, Li Guo, Carlos Milovic, Jinsuh Kim, Hongjiang Wei, Kristian Bredies, Sagar Buch, Yihao Guo, Zhe Liu, Jakob Meineke, Alexander Rauscher, José P Marques, and Berkin Bilgic.
• Department of Neurology, Medical University of Graz, Graz, Austria.
• Magn Reson Med. 2018 Mar 1; 79 (3): 1661-1673.

PurposeThe 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.MethodsGradient-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), high-frequency error norm (HFEN), and the error in selected white and gray matter regions.ResultsTwenty-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.ConclusionBecause 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. Magn Reson Med 79:1661-1673, 2018. © 2017 International Society for Magnetic Resonance in Medicine.© 2017 International Society for Magnetic Resonance in Medicine.

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