Dr Hongfu Sun
Dr

Hongfu Sun

Email: 

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

55 works between 2014 and 2024
All (55) Journal Article (50) Conference Publication (5)

1 - 20 of 55 works

Featured

2020

Journal Article

Editorial for “Deep‐Learning Detection of Cancer Metastasis to the Brain on MRI”

Sun, Hongfu (2020). Editorial for “Deep‐Learning Detection of Cancer Metastasis to the Brain on MRI”. Journal of Magnetic Resonance Imaging, 52 (4) jmri.27131, 1237-1238. doi: 10.1002/jmri.27131

Editorial for “Deep‐Learning Detection of Cancer Metastasis to the Brain on MRI”

Featured

2019

Journal Article

Extracting more for less: multi‐echo MP2RAGE for simultaneous T 1 ‐weighted imaging, T 1 mapping, mapping, SWI, and QSM from a single acquisition

Sun, Hongfu, Cleary, Jon O., Glarin, Rebecca, Kolbe, Scott C., Ordidge, Roger J., Moffat, Bradford A. and Pike, G. Bruce (2019). Extracting more for less: multi‐echo MP2RAGE for simultaneous T 1 ‐weighted imaging, T 1 mapping, mapping, SWI, and QSM from a single acquisition. Magnetic Resonance in Medicine, 83 (4) mrm.27975, 1178-1191. doi: 10.1002/mrm.27975

Extracting more for less: multi‐echo MP2RAGE for simultaneous T 1 ‐weighted imaging, T 1 mapping, mapping, SWI, and QSM from a single acquisition

Featured

2018

Journal Article

Whole head quantitative susceptibility mapping using a least-norm direct dipole inversion method

Sun, Hongfu, Ma, Yuhan, MacDonald, M. Ethan and Pike, G. Bruce (2018). Whole head quantitative susceptibility mapping using a least-norm direct dipole inversion method. NeuroImage, 179, 166-175. doi: 10.1016/j.neuroimage.2018.06.036

Whole head quantitative susceptibility mapping using a least-norm direct dipole inversion method

Featured

2018

Journal Article

Quantitative susceptibility mapping for following intracranial hemorrhage

Sun, Hongfu, Klahr, Ana C., Kate, Mahesh, Gioia, Laura C., Emery, Derek J., Butcher, Kenneth S. and Wilman, Alan H. (2018). Quantitative susceptibility mapping for following intracranial hemorrhage. Radiology, 288 (3), 830-839. doi: 10.1148/radiol.2018171918

Quantitative susceptibility mapping for following intracranial hemorrhage

Featured

2017

Journal Article

Structural and functional quantitative susceptibility mapping from standard fMRI studies

Sun, H., Seres, P. and Wilman, A. H. (2017). Structural and functional quantitative susceptibility mapping from standard fMRI studies. NMR in Biomedicine, 30 (4) e3619, e3619. doi: 10.1002/nbm.3619

Structural and functional quantitative susceptibility mapping from standard fMRI studies

Featured

2016

Journal Article

Quantitative susceptibility mapping using a superposed dipole inversion method: Application to intracranial hemorrhage

Sun, Hongfu, Kate, Mahesh, Gioia, Laura C., Emery, Derek J., Butcher, Kenneth and Wilman, Alan H. (2016). Quantitative susceptibility mapping using a superposed dipole inversion method: Application to intracranial hemorrhage. Magnetic Resonance in Medicine, 76 (3), 781-791. doi: 10.1002/mrm.25919

Quantitative susceptibility mapping using a superposed dipole inversion method: Application to intracranial hemorrhage

Featured

2015

Journal Article

Quantitative susceptibility mapping using single-shot echo-planar imaging

Sun, Hongfu and Wilman, Alan H. (2015). Quantitative susceptibility mapping using single-shot echo-planar imaging. Magnetic Resonance in Medicine, 73 (5), 1932-1938. doi: 10.1002/mrm.25316

Quantitative susceptibility mapping using single-shot echo-planar imaging

Featured

2015

Journal Article

Validation of quantitative susceptibility mapping with Perls' iron staining for subcortical gray matter

Sun, Hongfu, Walsh, Andrew J., Lebel, R. Marc, Blevins, Gregg, Catz, Ingrid, Lu, Jian-Qiang, Johnson, Edward S., Emery, Derek J., Warren, Kenneth G. and Wilman, Alan H. (2015). Validation of quantitative susceptibility mapping with Perls' iron staining for subcortical gray matter. NeuroImage, 105, 486-492. doi: 10.1016/j.neuroimage.2014.11.010

Validation of quantitative susceptibility mapping with Perls' iron staining for subcortical gray matter

Featured

2014

Journal Article

Background field removal using spherical mean value filtering and Tikhonov regularization

Sun, Hongfu and Wilman, Alan H. (2014). Background field removal using spherical mean value filtering and Tikhonov regularization. Magnetic Resonance in Medicine, 71 (3), 1151-1157. doi: 10.1002/mrm.24765

Background field removal using spherical mean value filtering and Tikhonov regularization

2024

Journal Article

Targeting cancer stress-associated hyperinsulinemia and abnormal behavior mitigates lung carcinoma in postmenopausal mouse: Intervention function of peimine

Li, Bobo, Guo, Xiaokang, Yu, Jiaqi, Sun, Hongfu, Zhao, Xiaoming, Sun, Yan, Dai, Xianling, Kuang, Qin, Ling, Jimao and Liu, Jie (2024). Targeting cancer stress-associated hyperinsulinemia and abnormal behavior mitigates lung carcinoma in postmenopausal mouse: Intervention function of peimine. Journal of Functional Foods, 121 106400, 106400. doi: 10.1016/j.jff.2024.106400

Targeting cancer stress-associated hyperinsulinemia and abnormal behavior mitigates lung carcinoma in postmenopausal mouse: Intervention function of peimine

2024

Journal Article

Correction: The therapeutic effect of radiotherapy combined with systemic therapy compared to radiotherapy alone in patients with simple brain metastasis after first-line treatment of limited-stage small cell lung cancer: a retrospective study

Gao, Xinyu, Liu, Tingting, Fan, Min, Sun, Hongfu, Zhou, Shixuan, Zhou, Yuxin, Zhu, Haolin, Zhang, Ru, Li, Zhanyuan and Huang, Wei (2024). Correction: The therapeutic effect of radiotherapy combined with systemic therapy compared to radiotherapy alone in patients with simple brain metastasis after first-line treatment of limited-stage small cell lung cancer: a retrospective study. World Journal of Surgical Oncology, 22 (1) 252. doi: 10.1186/s12957-024-03531-1

Correction: The therapeutic effect of radiotherapy combined with systemic therapy compared to radiotherapy alone in patients with simple brain metastasis after first-line treatment of limited-stage small cell lung cancer: a retrospective study

2024

Journal Article

Neoadjuvant radiation target volume definition in esophageal squamous cell cancer: a multicenter recommendations from Chinese experts

Han, Dan, Dong, Jinling, Wang, Qifeng, Li, Baosheng, Liu, Jun, Liu, Hui, Qiu, Bo, Zhang, Wencheng, Yang, Hong, Shen, Wenbin, Zhang, Yaowen, Zhu, Xiangzhi, Wang, Yi, Wu, Lei, Sun, Hongfu and Huang, Wei (2024). Neoadjuvant radiation target volume definition in esophageal squamous cell cancer: a multicenter recommendations from Chinese experts. BMC Cancer, 24 (1) 1086. doi: 10.1186/s12885-024-12825-2

Neoadjuvant radiation target volume definition in esophageal squamous cell cancer: a multicenter recommendations from Chinese experts

2024

Conference Publication

Fast controllable diffusion models for undersampled MRI reconstruction

Jiang, Wei, Xiong, Zhuang, Liu, Feng, Ye, Nan and Sun, Hongfu (2024). Fast controllable diffusion models for undersampled MRI reconstruction. 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece, 27-30 May 2024. Piscataway, NJ, United States: IEEE. doi: 10.1109/isbi56570.2024.10635891

Fast controllable diffusion models for undersampled MRI reconstruction

2024

Journal Article

Quantitative susceptibility mapping through model-based deep image prior (MoDIP)

Xiong, Zhuang, Gao, Yang, Liu, Yin, Fazlollahi, Amir, Nestor, Peter, Liu, Feng and Sun, Hongfu (2024). Quantitative susceptibility mapping through model-based deep image prior (MoDIP). NeuroImage, 291 120583, 120583. doi: 10.1016/j.neuroimage.2024.120583

Quantitative susceptibility mapping through model-based deep image prior (MoDIP)

2024

Journal Article

Plug-and-Play latent feature editing for orientation-adaptive quantitative susceptibility mapping neural networks

Gao, Yang, Xiong, Zhuang, Shan, Shanshan, Liu, Yin, Rong, Pengfei, Li, Min, Wilman, Alan H., Pike, G. Bruce, Liu, Feng and Sun, Hongfu (2024). Plug-and-Play latent feature editing for orientation-adaptive quantitative susceptibility mapping neural networks. Medical Image Analysis, 94 103160, 103160. doi: 10.1016/j.media.2024.103160

Plug-and-Play latent feature editing for orientation-adaptive quantitative susceptibility mapping neural networks

2023

Journal Article

Editorial: Imaging of neurometabolism

Zheng, Wenbin, Dai, Zhouzhi, Wu, Renhua and Sun, Hongfu (2023). Editorial: Imaging of neurometabolism. Frontiers in Neuroscience, 17 1286361, 1286361. doi: 10.3389/fnins.2023.1286361

Editorial: Imaging of neurometabolism

2023

Journal Article

MapFlow: latent transition via normalizing flow for unsupervised domain adaptation

Askari, Hossein, Latif, Yasir and Sun, Hongfu (2023). MapFlow: latent transition via normalizing flow for unsupervised domain adaptation. Machine Learning, 112 (8), 2953-2974. doi: 10.1007/s10994-023-06357-2

MapFlow: latent transition via normalizing flow for unsupervised domain adaptation

2023

Journal Article

Increased glymphatic system activity in patients with mild traumatic brain injury

Dai, Zhuozhi, Yang, Zhiqi, Li, Zhaolin, Li, Mu, Sun, Hongfu, Zhuang, Zerui, Yang, Weichao, Hu, Zehuan, Chen, Xiaofeng, Lin, Daiying and Wu, Xianheng (2023). Increased glymphatic system activity in patients with mild traumatic brain injury. Frontiers in Neurology, 14 1148878, 1-7. doi: 10.3389/fneur.2023.1148878

Increased glymphatic system activity in patients with mild traumatic brain injury

2023

Journal Article

Distortion‐corrected image reconstruction with deep learning on an MRI‐Linac

Shan, Shanshan, Gao, Yang, Liu, Paul Z. Y., Whelan, Brendan, Sun, Hongfu, Dong, Bin, Liu, Feng and Waddington, David E. J. (2023). Distortion‐corrected image reconstruction with deep learning on an MRI‐Linac. Magnetic Resonance in Medicine, 90 (3), 1-15. doi: 10.1002/mrm.29684

Distortion‐corrected image reconstruction with deep learning on an MRI‐Linac

2023

Journal Article

Affine transformation edited and refined deep neural network for quantitative susceptibility mapping

Xiong, Zhuang, Gao, Yang, Liu, Feng and Sun, Hongfu (2023). Affine transformation edited and refined deep neural network for quantitative susceptibility mapping. NeuroImage, 267 119842, 1-9. doi: 10.1016/j.neuroimage.2022.119842

Affine transformation edited and refined deep neural network for quantitative susceptibility mapping

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
  • 2021 - 2024
    A novel, dictionary-free, multi-contrast MRI method for microscopic imaging
    ARC Discovery Early Career Researcher Award
    Open grant

Past funding

  • 2023
    Translating state-of-the-art quantitative MRI techniques into clinical applications
    UQ Knowledge Exchange & Translation Fund
    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

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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