Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge

• Published in: Magnetic resonance in medicine Vol. 79; no. 3; pp. 1661 - 1673
• Main Authors: Langkammer, Christian, Schweser, Ferdinand, Shmueli, Karin, Kames, Christian, Li, Xu, Guo, Li, Milovic, Carlos, Kim, Jinsuh, Wei, Hongjiang, Bredies, Kristian, Buch, Sagar, Guo, Yihao, Liu, Zhe, Meineke, Jakob, Rauscher, Alexander, Marques, José P., Bilgic, Berkin
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.03.2018
• Subjects: Adult; Algorithms; assessment; Blood vessels; Brain - diagnostic imaging; Brain Mapping - methods; challenge; Conditioning; Conspicuity; Data acquisition; dipole inversion; Errors; Female; Frequency dependence; Humans; Image acquisition; Image Processing, Computer-Assisted - methods; Image quality; Magnetic permeability; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mapping; Orientation behavior; quantitative susceptibility mapping; Reconstruction; reconstruction algorithms; Substantia grisea; assessment; reconstruction algorithms; challenge; quantitative susceptibility mapping; dipole inversion
• ISSN: 0740-3194; 1522-2594; 1522-2594
• DOI: 10.1002/mrm.26830

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), high‐frequency 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. Magn Reson Med 79:1661–1673, 2018. © 2017 International Society for Magnetic Resonance in Medicine.