ARC DECRA Research Fellow
Overview
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.
Research Interests
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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. -
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. -
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.
Qualifications
- Doctor of Philosophy, University of Alberta
Publications
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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
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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
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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
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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
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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
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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
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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
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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
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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
Grants
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Tissue Bio-physicochemical Quantification Using Magnetic Resonance Imaging
(2023–2026) ARC Discovery Projects
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Translating state-of-the-art quantitative MRI techniques into clinical applications
(2023) UQ Knowledge Exchange & Translation Fund
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A novel, dictionary-free, multi-contrast MRI method for microscopic imaging
(2021–2023) ARC Discovery Early Career Researcher Award
Supervision
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MR image processing through advanced optimisation techniques and deep learning
Doctor Philosophy
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MR image processing through advanced optimisation techniques and deep learning
Doctor Philosophy
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MRI methods development through deep learning
Doctor Philosophy
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
Publications
Featured Publications
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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
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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
-
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
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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
-
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
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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 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
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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
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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
Journal Article
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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. doi: 10.3389/fneur.2023.1148878
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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, 1-15. doi: 10.1002/mrm.29684
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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
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Quantitative susceptibility mapping changes relate to gait issues in Parkinson’s Disease
Nathoo, Nabeela, Gee, Myrlene, Nelles, Krista, Burt, Jacqueline, Sun, Hongfu, Seres, Peter, Wilman, Alan H., Beaulieu, Christian, Ba, Fang and Camicioli, Richard (2022). Quantitative susceptibility mapping changes relate to gait issues in Parkinson’s Disease. Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques, 1-22. doi: 10.1017/cjn.2022.316
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Yang, Runze, Hamilton, A. Max, Sun, Hongfu, Rawji, Khalil S., Sarkar, Susobhan, Mirzaei, Reza, Pike, G. Bruce, Yong, V. Wee. and Dunn, Jeff F. (2022). Detecting monocyte trafficking in an animal model of glioblastoma using R2* and quantitative susceptibility mapping. Cancer Immunology, Immunotherapy, 72 (3), 733-742. doi: 10.1007/s00262-022-03297-z
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Nakhid, Daphne, McMorris, Carly, Sun, Hongfu, Gibbard, William Benton, Tortorelli, Christina and Lebel, Catherine (2022). Brain volume and magnetic susceptibility differences in children and adolescents with prenatal alcohol exposure. Alcoholism: Clinical and Experimental Research, 46 (10), 1797-1807. doi: 10.1111/acer.14928
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Gao, Yang, Xiong, Zhuang, Fazlollahi, Amir, Nestor, Peter J., Vegh, Viktor, Nasrallah, Fatima, Winter, Craig, Pike, G. Bruce, Crozier, Stuart, Liu, Feng and Sun, Hongfu (2022). Instant tissue field and magnetic susceptibility mapping from MRI raw phase using Laplacian enhanced deep neural networks. NeuroImage, 259 119410, 1-13. doi: 10.1016/j.neuroimage.2022.119410
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Zhu, Xuanyu, Gao, Yang, Liu, Feng, Crozier, Stuart and Sun, Hongfu (2022). BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources. Zeitschrift fur Medizinische Physik. doi: 10.1016/j.zemedi.2022.08.001
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Brain iron and mental health symptoms in youth with and without prenatal alcohol exposure
Nakhid, Daphne, McMorris, Carly A., Sun, Hongfu, Gibbard, Ben, Tortorelli, Christina and Lebel, Catherine (2022). Brain iron and mental health symptoms in youth with and without prenatal alcohol exposure. Nutrients, 14 (11) 2213, 1-18. doi: 10.3390/nu14112213
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Yang, Zhiqi, Lin, Daiying, Chen, Xiaofeng, Qiu, Jinming, Li, Shengkai, Huang, Ruibin, Yang, Zhijian, Sun, Hongfu, Liao, Yuting, Xiao, Jianning, Tang, Yanyan, Chen, Xiangguang, Zhang, Sheng and Dai, Zhuozhi (2022). Distinguishing COVID-19 from influenza pneumonia in the early stage through CT imaging and clinical features. Frontiers in Microbiology, 13 847836, 1-9. doi: 10.3389/fmicb.2022.847836
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Zhu, Xuanyu, Gao, Yang, Liu, Feng, Crozier, Stuart and Sun, Hongfu (2022). Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning. Zeitschrift fur Medizinische Physik, 32 (2), 188-198. doi: 10.1016/j.zemedi.2021.06.004
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Gao, Yang, Cloos, Martijn, Liu, Feng, Crozier, Stuart, Pike, G. Bruce and Sun, Hongfu (2021). Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction. NeuroImage, 240 118404, 1-13. doi: 10.1016/j.neuroimage.2021.118404
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Liu, Xinwen, Wang, Jing, Sun, Hongfu, Chandra, Shekhar S, Crozier, Stuart and Liu, Feng (2021). On the regularization of feature fusion and mapping for fast MR multi-contrast imaging via iterative networks. Magnetic resonance imaging, 77, 159-168. doi: 10.1016/j.mri.2020.12.019
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Gao, Yang, Zhu, Xuanyu, Moffat, Bradford A., Glarin, Rebecca, Wilman, Alan H., Pike, G. Bruce, Crozier, Stuart, Liu, Feng and Sun, Hongfu (2021). xQSM: quantitative susceptibility mapping with octave convolutional and noise-regularized neural networks. NMR in Biomedicine, 34 (3) e4461, e4461. doi: 10.1002/nbm.4461
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Atypical presentations of coronavirus disease 2019 (COVID-19) from onset to readmission
Yang, Zhiqi, Chen, Xiaofeng, Huang, Ruibin, Li, Shengkai, Lin, Daiying, Yang, Zhijian, Sun, Hongfu, Liu, Guorui, Qiu, Jinming, Tang, Yanyan, Xiao, Jianning, Liao, Yuting, Wu, Xianheng, Wu, Renhua, Chen, Xiangguang and Dai, Zhuozhi (2021). Atypical presentations of coronavirus disease 2019 (COVID-19) from onset to readmission. BMC Infectious Diseases, 21 (1) 127, 127. doi: 10.1186/s12879-020-05751-8
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MacDonald, M. Ethan, Williams, Rebecca J., Rajashekar, Deepthi, Stafford, Randall B., Hanganu, Alexadru, Sun, Hongfu, Berman, Avery J.L., McCreary, Cheryl R., Frayne, Richard, Forkert, Nils D. and Pike, G. Bruce (2020). Age-related differences in cerebral blood flow and cortical thickness with an application to age prediction. Neurobiology of Aging, 95, 131-142. doi: 10.1016/j.neurobiolaging.2020.06.019
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Dai, Zhuozhi, Kalra, Sanjay, Mah, Dennell, Seres, Peter, Sun, Hongfu, Wu, Renhua and Wilman, Alan H. (2020). Amide signal intensities may be reduced in the motor cortex and the corticospinal tract of ALS patients. European Radiology, 31 (3), 1401-1409. doi: 10.1007/s00330-020-07243-4
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Quantification of brain oxygen extraction fraction using QSM and a hyperoxic challenge
Ma, Yuhan, Mazerolle, Erin L., Cho, Junghun, Sun, Hongfu, Wang, Yi and Pike, G. Bruce (2020). Quantification of brain oxygen extraction fraction using QSM and a hyperoxic challenge. Magnetic Resonance in Medicine, 84 (6) mrm.28390, 3271-3285. doi: 10.1002/mrm.28390
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Chen, Xiaofeng, Tang, Yanyan, Mo, Yongkang, Li, Shengkai, Lin, Daiying, Yang, Zhijian, Yang, Zhiqi, Sun, Hongfu, Qiu, Jinming, Liao, Yuting, Xiao, Jianning, Chen, Xiangguang, Wu, Xianheng, Wu, Renhua and Dai, Zhuozhi (2020). A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study. European Radiology, 30 (9), 4893-4902. doi: 10.1007/s00330-020-06829-2
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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
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On the value of QSM from MPRAGE for segmenting and quantifying iron-rich deep gray matter
Naji, Nashwan, Sun, Hongfu and Wilman, Alan H. (2020). On the value of QSM from MPRAGE for segmenting and quantifying iron-rich deep gray matter. Magnetic Resonance in Medicine, 84 (3) mrm.28226, 1486-1500. doi: 10.1002/mrm.28226
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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
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Ma, Yuhan, Sun, Hongfu, Cho, Junghun, Mazerolle, Erin L., Wang, Yi and Pike, G. Bruce (2019). Cerebral OEF quantification: a comparison study between quantitative susceptibility mapping and dual‐gas calibrated BOLD imaging. Magnetic Resonance in Medicine, 83 (1) mrm.27907, 68-82. doi: 10.1002/mrm.27907
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Elkady, Ahmed M., Cobzas, Dana, Sun, Hongfu, Seres, Peter, Blevins, Gregg and Wilman, Alan H. (2019). Five year iron changes in relapsing-remitting multiple sclerosis deep gray matter compared to healthy controls. Multiple Sclerosis and Related Disorders, 33, 107-115. doi: 10.1016/j.msard.2019.05.028
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Rapid quantitative susceptibility mapping of intracerebral hemorrhage
De, Ashmita, Sun, Hongfu, Emery, Derek J., Butcher, Kenneth S. and Wilman, Alan H. (2019). Rapid quantitative susceptibility mapping of intracerebral hemorrhage. Journal of Magnetic Resonance Imaging, 51 (3) jmri.26850, 712-718. doi: 10.1002/jmri.26850
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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
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Elkady, Ahmed M., Cobzas, Dana, Sun, Hongfu, Blevins, Gregg and Wilman, Alan H. (2018). Discriminative analysis of regional evolution of iron and myelin/calcium in deep gray matter of multiple sclerosis and healthy subjects. Journal of Magnetic Resonance Imaging, 48 (3), 652-668. doi: 10.1002/jmri.26004
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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
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Hematocrit measurement with R2* and quantitative susceptibility mapping in postmortem brain
Walsh, A. J., Sun, H., Emery, D. J. and Wilman, A. H. (2018). Hematocrit measurement with R2* and quantitative susceptibility mapping in postmortem brain. American Journal of Neuroradiology, 39 (7), 1260-1266. doi: 10.3174/ajnr.A5677
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Elkady, Ahmed M., Cobzas, Dana, Sun, Hongfu, Blevins, Gregg and Wilman, Alan H. (2017). Progressive iron accumulation across multiple sclerosis phenotypes revealed by sparse classification of deep gray matter. Journal of Magnetic Resonance Imaging, 46 (5), 1464-1473. doi: 10.1002/jmri.25682
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Cognitive implications of deep gray matter iron in multiple sclerosis
Fujiwara, E., Kmech, J. A., Cobzas, D., Sun, H., Seres, P., Blevins, G. and Wilman, A. H. (2017). Cognitive implications of deep gray matter iron in multiple sclerosis. American Journal of Neuroradiology, 38 (5), 942-948. doi: 10.3174/ajnr.A5109
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Deep grey matter iron accumulation in alcohol use disorder
Juhas, Michal, Sun, Hongfu, Brown, Matthew R. G., MacKay, Marnie B., Mann, Karl F., Sommer, Wolfgang H., Wilman, Alan H., Dursun, Serdar M. and Greenshaw, Andrew J. (2017). Deep grey matter iron accumulation in alcohol use disorder. NeuroImage, 148, 115-122. doi: 10.1016/j.neuroimage.2017.01.007
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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
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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
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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
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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
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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
-
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
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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
Conference Publication
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Accelerating QSM using compressed sensing and deep neural network
Gao, Yang, Liu, Feng, Crozier, Stuart and Sun, Hongfu (2021). Accelerating QSM using compressed sensing and deep neural network. 2021 ISMRM & SMRT Annual Meeting & Exhibition, Online, 15-20 May 2021. Berkeley, CA, United States: International Society for Magnetic Resonance in Medicine.
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Liu, Xinwen, Wang, Jing, Tang, Fangfang, Sun, Hongfu, Liu, Feng and Crozier, Stuart (2020). Rapid region-of-interest MRI reconstruction using context-aware rapid region-of-interest MRI reconstruction using context-aware non-local U-net. ISMRM & SMRT Virtual Conference & Exhibition, 2020, Virtual, 8-14 August 2020.
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Sun, Hongfu, MacDonald, M. Ethan, Lebel, R. Marc and Pike, G. Bruce (2020). Simultaneous T1-weighted imaging, R2* mapping, and QSM from a multi-echo MPRAGE sequence using a radial fan-beam sampling scheme at 3 Tesla. ISMRM & SMRT Virtual Conference & Exhibition, Online, 8-14 August 2020. Berkeley, CA, United States: International Society for Magnetic Resonance in Medicine.
Grants (Administered at UQ)
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Tissue Bio-physicochemical Quantification Using Magnetic Resonance Imaging
(2023–2026) ARC Discovery Projects
-
Translating state-of-the-art quantitative MRI techniques into clinical applications
(2023) UQ Knowledge Exchange & Translation Fund
-
A novel, dictionary-free, multi-contrast MRI method for microscopic imaging
(2021–2023) ARC Discovery Early Career Researcher Award
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Fast in vivo biometal imaging of the brain using MRI
(2020–2021) Research Donation Generic
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Imaging brain iron in Alzheimer's disease: Development, Validation and Clinical Implementation
(2020) UQ Early Career Researcher
PhD and MPhil Supervision
Current Supervision
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MR image processing through advanced optimisation techniques and deep learning
Doctor Philosophy — Principal Advisor
Other advisors:
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MR image processing through advanced optimisation techniques and deep learning
Doctor Philosophy — Principal Advisor
Other advisors:
-
MRI methods development through deep learning
Doctor Philosophy — Principal Advisor
Other advisors:
-
MR image processing through advanced optimization techniques and deep learning
Doctor Philosophy — Principal Advisor
Other advisors:
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Solutions for reducing magnetic resonance image degradations and tissue heating at high-field MRI
Doctor Philosophy — Associate Advisor
Other advisors:
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Magnetic Resonance Image Processing with Artificial Intelligence
Doctor Philosophy — Associate Advisor
Other advisors:
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Combined Compressed sensing and machine learning/deep learning methods for rapid MRI
Doctor Philosophy — Associate Advisor
Completed Supervision
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Deep Learning-based Quantitative Susceptibility Mapping: Methods Development and Applications
(2022) Doctor Philosophy — Associate Advisor
Other advisors:
Possible Research Projects
Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.
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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
