Dr Hongfu Sun
Dr

Hongfu Sun

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Background

Dr Hongfu Sun completed his PhD in Biomedical Engineering at the University of Alberta in 2015, followed by postdoctoral training in Calgary until 2018. He joined the Imaging, Sensing and Biomedical Engineering team in the School of ITEE at UQ in 2019 and was awarded the ARC DECRA fellowship in 2021. His research interests include developing novel magnetic resonance imaging (MRI) contrast mechanisms, e.g. Quantitative Susceptibility Mapping (QSM), fast and multi-parametric MRI acquisitions, and advanced image reconstruction techniques, including deep learning and artificial intelligence, to advance medical imaging techniques for clinical applications.

Dr Sun is currently recruiting graduate students. Check out Available Projects for details. Open to both Domestic and International students.

Availability

Dr Hongfu Sun is:
Available for supervision

Fields of research

Biomedical and Clinical Sciences Biomedical engineering Biomedical instrumentation Engineering Image processing Information and Computing Sciences Neurosciences

Qualifications

  • Doctor of Philosophy, University of Alberta

Research interests

  • MR image processing through advanced optimization techniques and deep learning

    My research interest is to develop advanced image reconstruction and processing methods to solve some of the mathematical challenges in MRI research, such as the ill-posed inverse problem in Quantitative Susceptibility Mapping (QSM). Some of the approaches I am particularly interested in are (1) image optimization techniques such as image regularization and compressed sensing, and (2) machine learning and especially deep learning through the convolutional neural network (CNN) and transformers in vision.

  • Fast, multi-parametric, and quantitative MRI acquisition methods at ultra-high field

    A single MRI acquisition usually takes 3-5 minutes, and a standard clinical protocol may require a couple of them for complementary contrasts. My research interest is to significantly reduce the total scan time for each patient while maintaining the same amount of information for diagnosis, by (1) accelerating individual scans with parallel imaging techniques at ultra-high field, and (2) designing novel MRI sequences that can produce multi-contrast weighted images and multi-parametric quantitative maps from a single MR acquisition.

  • Brain imaging applications in neuroscience and neurological diseases

    MRI is one of the best tools to study the brain in vivo, thanks to its excellent soft tissue contrast and its versatile contrast mechanisms. I am interested in applying advanced and comprehensive image analysis on different MRI methods to better understand neuroscience, such as brain development in children and structural and functional connectivity of the brain, as well as some of the neurological diseases such as Alzheimer/Dementia, Multiple Sclerosis, Schizophrenia, and Stroke.

Research impacts

Dr Hongfu Sun is one of the early pioneers in developing a novel MRI technique - Quantitative Susceptibility Mapping (QSM), which is one of the most significant MRI contrast breakthroughs in recent years, that has demonstrated wide clinical applications in healthy, aging and diseased human brains, such as dementia, Alzheimer's disease, Parkinson's disease, multiple sclerosis, schizophrenia, stroke, etc. Since commencing at UQ, Dr Sun has extended his research topics to exploiting novel reconstruction algorithms using state-of-the-art deep learning-based artificial intelligence techniques.

Search Professor Hongfu Sun’s works on UQ eSpace

62 works between 2014 and 2025
All (62) Journal Article (54) Conference Publication (8)

61 - 62 of 62 works

2016

Journal Article

Importance of extended spatial coverage for quantitative susceptibility mapping of iron-rich deep gray matter

Elkady, Ahmed M., Sun, Hongfu and Wilman, Alan H. (2016). Importance of extended spatial coverage for quantitative susceptibility mapping of iron-rich deep gray matter. Magnetic Resonance Imaging, 34 (4), 574-578. doi: 10.1016/j.mri.2015.12.032

Importance of extended spatial coverage for quantitative susceptibility mapping of iron-rich deep gray matter

2015

Journal Article

Subcortical gray matter segmentation and voxel-based analysis using transverse relaxation and quantitative susceptibility mapping with application to multiple sclerosis

Cobzas, Dana, Sun, Hongfu, Walsh, Andrew J., Lebel, R. Marc, Blevins, Gregg and Wilman, Alan H. (2015). Subcortical gray matter segmentation and voxel-based analysis using transverse relaxation and quantitative susceptibility mapping with application to multiple sclerosis. Journal of Magnetic Resonance Imaging, 42 (6), 1601-1610. doi: 10.1002/jmri.24951

Subcortical gray matter segmentation and voxel-based analysis using transverse relaxation and quantitative susceptibility mapping with application to multiple sclerosis

Current funding

  • 2024 - 2026
    Disambiguating Parkinson's disease from disorders with mimicking symptoms using ultra-high-field (7 Tesla) multi-modal MRI
    NHMRC IDEAS Grants
    Open grant
  • 2023 - 2026
    Tissue Bio-physicochemical Quantification Using Magnetic Resonance Imaging
    ARC Discovery Projects
    Open grant

Past funding

  • 2023
    Translating state-of-the-art quantitative MRI techniques into clinical applications
    UQ Knowledge Exchange & Translation Fund
    Open grant
  • 2021 - 2024
    A novel, dictionary-free, multi-contrast MRI method for microscopic imaging
    ARC Discovery Early Career Researcher Award
    Open grant
  • 2020 - 2021
    Fast in vivo biometal imaging of the brain using MRI
    Research Donation Generic
    Open grant
  • 2020
    Imaging brain iron in Alzheimer's disease: Development, Validation and Clinical Implementation
    UQ Early Career Researcher
    Open grant

Availability

Dr Hongfu Sun is:
Available for supervision

Looking for a supervisor? Read our advice on how to choose a supervisor.

Available projects

  • MRI and deep learning methods development and applications at ultra-high field

    I am currently recruiting Master and PhD students to innovate on novel MRI methods and deep learning image reconstruction techniques that can be eventually applied to neuroscience and neurological diseases. We have an excellent and accessible MRI facility here at UQ, e.g. a state-of-the-art 3T Prisma and a prestigious 7T whole-body system (only two in Australia, the other one in UniMelb). The research projects will involve MRI physics, pulse sequence programming, image processing (e.g. deep learning), and image analysis. By the end of your graduate study, you will be an expert in MRI with comprehensive skills in maths, physics, computer programming, and artificial intelligence.

    https://graduate-school.uq.edu.au/project/developing-ai-based-mri-methods-microscopic-imaging

Supervision history

Current supervision

Completed supervision

Enquiries

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communications@uq.edu.au