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QSM reconstruction challenge 2.0: Design and report of results.

null nullBerkin BilgicChristian LangkammerJosé P MarquesJakob MeinekeCarlos MilovicFerdinand Schweser
Published in: Magnetic resonance in medicine (2021)
The synthetic data provide a consistent framework to test the accuracy and robustness of QSM algorithms in the presence of noise, calcifications and minor voxel dephasing effects. Total Variation-based algorithms produced the best results among all metrics. Future QSM challenges should assess whether this good performance with synthetic datasets translates to more realistic scenarios, where background fields and dipole-incompatible phase contributions are included.
Keyphrases
  • machine learning
  • deep learning
  • big data
  • climate change
  • electronic health record
  • air pollution
  • artificial intelligence