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Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge.

Christian LangkammerFerdinand SchweserKarin ShmueliChristian KamesXu LiLi GuoCarlos MilovicJinsuh KimHongjiang WeiKristian BrediesSagar BuchYihao GuoZhe LiuJakob MeinekeAlexander RauscherJosé P MarquesBerkin Bilgic
Published in: Magnetic resonance in medicine (2017)
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.
Keyphrases
  • image quality
  • magnetic resonance
  • high resolution
  • computed tomography
  • machine learning
  • air pollution
  • dual energy
  • contrast enhanced