Research

Primary Research Topics

• Advanced biomedical imaging:
  • – Develop biomedical image acquisition, reconstruction, and post-processing methods for structural and functional brain imaging
  • GAP
• “Physics” powered machine learning:
  • – Develop machine learning algorithms that enforce physical rules for biomedical image reconstruction and processing
  • GAP
• “Machine learning” powered software and hardware design:
  • – Design biomedical imaging software and hardware using machine learning algorithms
  • – Develop machine learning algorithms for biomedical imaging software and hardware
  • GAP
• Biomedical imaging hardware:
  • – Design new systems for biomedical imaging (e.g. MRI RF coils and gradient systems)
  • GAP
• Biophysical properties of the brain:
  • – Explore biophysical properties of the brain and novel image contrasts for future neuroimaging
  • GAP
• Application of novel biomedical imaging methodology to clinical and scientific studies
  • – Apply new methodology to clinical (e.g. Alzheimer’s disease or Parkinson’s disease) and scientific (e.g. neuroplasticity) applications

Research Highlights

• Machine learning powered MRI reconstruction
  • GAP
  • – QSMnet
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  • GAP
  • GAP
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  • – T2 mapping
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  • GAP
  • GAP
  • GAP
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• “Machine learning” powered software and hardware design:
  • – To be announced after ISMRM 2019 Montreal (May)
  • GAP
  • GAP
  • GAP
  • GAP
• Magnetic susceptibility mapping
  • GAP
  • – Source separation for positive and negative susceptibility
  • GAP
  • GAP
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  • – Susceptibility map weighted imaging for Nigrosome 1 imaging
  • G
  • GAP
  • GAP
  • AP
• Brain myelin imaging
  • GAP
  • – Gradient echo myelin water imaging
  • GAP
  • GAP
  • – G-ratio mapping
  • G
  • AP
  • – ViSTa myelin water weighted imaging
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