Overview
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
Qualifications
- Doctor of Philosophy, University of Alberta
Research interests
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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.
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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.
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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.
Works
Search Professor Hongfu Sun’s works on UQ eSpace
2025
Journal Article
Quantitative susceptibility mapping via deep neural networks with iterative reverse concatenations and recurrent modules
Li, Min, Chen, Chen, Xiong, Zhuang, Liu, Yin, Rong, Pengfei, Shan, Shanshan, Liu, Feng, Sun, Hongfu and Gao, Yang (2025). Quantitative susceptibility mapping via deep neural networks with iterative reverse concatenations and recurrent modules. Medical Physics, 52 (6), 4341-4354. doi: 10.1002/mp.17747
2025
Journal Article
Integrative radiomics analysis of peri-tumoral and habitat zones for predicting major pathological response to neoadjuvant immunotherapy and chemotherapy in non-small cell lung cancer
Han, Dan, Zhao, Junfeng, Hao, Shaoyu, Fu, Shenbo, Wei, Ran, Zheng, Xin, Zhao, Qian, Liu, Chengxin, Sun, Hongfu, Fu, Chengrui, Wang, Zhongtang, Huang, Wei and Li, Baosheng (2025). Integrative radiomics analysis of peri-tumoral and habitat zones for predicting major pathological response to neoadjuvant immunotherapy and chemotherapy in non-small cell lung cancer. Translational Lung Cancer Research, 14 (4). doi: 10.21037/tlcr-2024-1131
2025
Journal Article
Using EnKF data assimilation to improve predictions of volcanic ash dispersion
Weng, Zefeng, Zhu, Lin, Li, Jun, Zhang, Yiran, Liu, Xuyan, Su, Wu, Sun, Hongfu and Li, Xinyu (2025). Using EnKF data assimilation to improve predictions of volcanic ash dispersion. Journal of Geophysical Research-Atmospheres, 130 (8) e2024JD042215, 1-21. doi: 10.1029/2024JD042215
2025
Journal Article
MRF-mixer: a simulation-based deep learning framework for accelerated and accurate magnetic resonance fingerprinting reconstruction
Ding, Tianyi, Gao, Yang, Xiong, Zhuang, Liu, Feng, Cloos, Martijn A. and Sun, Hongfu (2025). MRF-mixer: a simulation-based deep learning framework for accelerated and accurate magnetic resonance fingerprinting reconstruction. Information, 16 (3) 218, 1-16. doi: 10.3390/info16030218
2025
Conference Publication
Training-free medical image inverses via bi-level guided diffusion models
Askari, Hossein, Roosta, Fred and Sun, Hongfu (2025). Training-free medical image inverses via bi-level guided diffusion models. 2025 Winter Conference on Applications of Computer Vision-WACV, Tucson, AZ, United States, 28 February-4 March 2025. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/wacv61041.2025.00018
2025
Conference Publication
GateFuseNet: an adaptive 3D multimodal neuroimaging fusion network for Parkinson's disease diagnosis
Jin, Rui, Chen, Chen, Liu, Yin, Wu, Peng, Sun, Hongfu, Zheng, Ruiqing, Zeng, Min, Li, Min and Gao, Yang (2025). GateFuseNet: an adaptive 3D multimodal neuroimaging fusion network for Parkinson's disease diagnosis. 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Wuhan, China, 15-18 December 2025. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/BIBM66473.2025.11356155
2025
Conference Publication
DeepRelaxo: a generalizable self-supervised method for brain R2* mapping
Prima, Samiha, Xiong, Zhuang, Wilman, Alan H. and Sun, Hongfu (2025). DeepRelaxo: a generalizable self-supervised method for brain R2* mapping. ISMRM 2025, Honolulu, HI, United States, 10-15 May 2025. Berkeley, CA, United States: International Society for Magnetic Resonance in Medicine. doi: 10.58530/2025/5144
2024
Journal Article
Accurate location describe and management of lymph node recurrence after esophagectomy for thoracic esophageal squamous cell carcinoma: a retrospective cohort study
Zhao, Qian, Sun, Jinglong, Zheng, Feng, Fu, Chengrui, Sun, Hongfu, Liu, Chengxin, Wang, Zhongtang, Huang, Wei, Wang, Ruozheng and Li, Baosheng (2024). Accurate location describe and management of lymph node recurrence after esophagectomy for thoracic esophageal squamous cell carcinoma: a retrospective cohort study. International Journal of Surgery, 110 (6), 3440-3449. doi: 10.1097/JS9.0000000000001242
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
2024
Journal Article
MH2AFormer: an efficient multiscale hierarchical hybrid attention with a transformer for bladder wall and tumor segmentation
Li, Xiang, Wang, Jian, Wei, Haifeng, Cong, Jinyu, Sun, Hongfu, Wang, Pingping and Wei, Benzheng (2024). MH2AFormer: an efficient multiscale hierarchical hybrid attention with a transformer for bladder wall and tumor segmentation. IEEE Journal of Biomedical and Health Informatics, 28 (8), 4772-4784. doi: 10.1109/JBHI.2024.3397698
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, 1-11. doi: 10.1016/j.media.2024.103160
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
2024
Conference Publication
QSM Reconstruction of Arbitrary Dipole Orientations using an End-to-end Neural Network via Latent Feature Editing
Gao, Yang, Xiong, Zhuang, Shan, Shanshan, Li, Min, Wilman, Alan H, Pike, G. Bruce, Liu, Feng and Sun, Hongfu (2024). QSM Reconstruction of Arbitrary Dipole Orientations using an End-to-end Neural Network via Latent Feature Editing. 2024 ISMRM & ISMRT Annual Meeting, Singapore, 4-9 May 2024. Concord, CA United States: ISMRM. doi: 10.58530/2024/2453
2023
Journal Article
Do higher radiation doses improve survival for cervical esophageal squamous cell cancer patients treated with definitive chemoradiotherapy using intensity-modulated radiotherapy? A propensity-score matched analysis
Xie, Feihong, Liu, Tingting, Wang, Xinran, Dong, Jinling, Huang, Wei and Sun, Hongfu (2023). Do higher radiation doses improve survival for cervical esophageal squamous cell cancer patients treated with definitive chemoradiotherapy using intensity-modulated radiotherapy? A propensity-score matched analysis. Journal of Cancer Research and Therapeutics, 19 (6), 1582-1588. doi: 10.4103/jcrt.jcrt_321_23
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
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
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
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
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
2023
Conference Publication
QSM from the raw phase using an end-to-end neural network
Gao, Yang, Xiong, Zhuang, Fazlollahi, Amir, Nestor, Peter, Vegh, Viktor, Pike, G. Bruce, Crozier, Stuart, Liu, Feng and Sun, Hongfu (2023). QSM from the raw phase using an end-to-end neural network. ISMRM Annual Meeting, London, United Kingdom, 7-12 May 2022. Berkeley, CA, United States: International Society for Magnetic Resonance in Medicine. doi: 10.58530/2022/4740
Supervision
Availability
- Dr Hongfu Sun is:
- Available for supervision
Looking for a supervisor? Read our advice on how to choose a supervisor.
Available projects
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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
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Doctor Philosophy
MR image processing through advanced optimisation techniques and deep learning
Principal Advisor
Other advisors: Professor Fred Roosta
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Doctor Philosophy
MR image processing through advanced optimisation techniques and deep learning
Principal Advisor
Other advisors: Professor Feng Liu
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Doctor Philosophy
Development of novel deep learning methods for medical imaging
Principal Advisor
Other advisors: Professor Feng Liu, Dr Nan Ye
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Doctor Philosophy
Magnetic Resonance Image Processing with Artificial Intelligence
Associate Advisor
Other advisors: Associate Professor Craig Engstrom, Dr Shakes Chandra
Completed supervision
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2026
Doctor Philosophy
Deep learning-based methods for magnetic resonance fingerprinting reconstruction
Principal Advisor
Other advisors: Professor Feng Liu
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2025
Doctor Philosophy
Robust and generalizable deep Learning quantitative susceptibility mapping for human brains
Principal Advisor
Other advisors: Professor Feng Liu
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2026
Doctor Philosophy
Deep Neural Architectures for Robust Feature Localization in Computer Tomography and Spectroscopy
Associate Advisor
Other advisors: Professor Kwun Fong, Associate Professor Henry Marshall, Professor Feng Liu
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2024
Doctor Philosophy
Key Applications in Deep Learning Based Quantitative Susceptibility Mapping
Associate Advisor
Other advisors: Emeritus Professor Stuart Crozier, Professor Feng Liu
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2022
Doctor Philosophy
Deep Learning-based Quantitative Susceptibility Mapping: Methods Development and Applications
Associate Advisor
Other advisors: Emeritus Professor Stuart Crozier, Professor Feng Liu
Media
Enquiries
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