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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 |
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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 acquisitionSun, 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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2018 Journal Article Whole head quantitative susceptibility mapping using a least-norm direct dipole inversion methodSun, 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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2018 Journal Article Quantitative susceptibility mapping for following intracranial hemorrhageSun, 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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2017 Journal Article Structural and functional quantitative susceptibility mapping from standard fMRI studiesSun, 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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2016 Journal Article Quantitative susceptibility mapping using a superposed dipole inversion method: Application to intracranial hemorrhageSun, 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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2015 Journal Article Quantitative susceptibility mapping using single-shot echo-planar imagingSun, 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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2015 Journal Article Validation of quantitative susceptibility mapping with Perls' iron staining for subcortical gray matterSun, 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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2014 Journal Article Background field removal using spherical mean value filtering and Tikhonov regularizationSun, 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 |
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2026 Journal Article SUSEP-Net: simulation-supervised and contrastive regularization-based deep neural networks for susceptibility source separationLi, Min, Chen, Chen, Li, Zhenghao, Liu, Yin, Shan, Shanshan, Wu, Peng, Rong, Pengfei, Liu, Feng, Pike, G.Bruce, H.Wilman, Alan, Sun, Hongfu and Gao, Yang (2026). SUSEP-Net: simulation-supervised and contrastive regularization-based deep neural networks for susceptibility source separation. Biomedical Signal Processing and Control, 128 111273, 111273. doi: 10.1016/j.bspc.2026.111273 |
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2026 Journal Article DeepRelaxo: fast mono-exponential magnitude brain R2* mapping with reduced echoes using self-supervised deep learningPrima, Samiha, Xiong, Zhuang, Wilman, Alan H. and Sun, Hongfu (2026). DeepRelaxo: fast mono-exponential magnitude brain R2* mapping with reduced echoes using self-supervised deep learning. Magnetic Resonance in Medicine, 96 (3), 1293-1302. doi: 10.1002/mrm.70405 |
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2026 Journal Article Advancing quantitative susceptibility mapping with 2.5D diffusion models for rapid intracranial hemorrhage quantificationXiong, Zhuang, Gao, Yang, Liu, Feng, Emery, Derek, Butcher, Ken, Wilman, Alan H. and Sun, Hongfu (2026). Advancing quantitative susceptibility mapping with 2.5D diffusion models for rapid intracranial hemorrhage quantification. Magnetic Resonance in Medicine, 96 (2), 596-610. doi: 10.1002/mrm.70358 |
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2026 Journal Article Highly undersampled MRI reconstruction via a single posterior sampling of diffusion modelsLiu, Jin, Lin, Qing, Xiong, Zhuang, Shan, Shanshan, Liu, Chunyi, Li, Min, Liu, Feng, Bruce Pike, G., Sun, Hongfu and Gao, Yang (2026). Highly undersampled MRI reconstruction via a single posterior sampling of diffusion models. IEEE Transactions on Medical Imaging, 45 (5), 2414-2428. doi: 10.1109/tmi.2026.3654585 |
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2026 Journal Article Breaking the limits of low‐field MRI: deep learning approaches to image enhancementZhu, Xuanyu, Shang, Yun, Yang, Hsin‐Jung, Yang, Lei, Long, Ziyang, Sun, Hongfu, Lin, Fuchun, Zhou, Xin and Han, Hui (2026). Breaking the limits of low‐field MRI: deep learning approaches to image enhancement. NMR in Biomedicine, 39 (6) e70285, 6. doi: 10.1002/nbm.70285 |
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2026 Conference Publication Adaptive gate-aware mamba networks for magnetic resonance fingerprintingDing, Tianyi, Chen, Hongli, Gao, Yang, Liu, Feng, Cloos, Martijn A. and Sun, Hongfu (2026). Adaptive Gate-Aware Mamba Networks for Magnetic Resonance Fingerprinting. IEEE. doi: 10.1109/isbi61048.2026.11515932 |
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2026 Journal Article SynPoC: a high-quality generative diffusion model for transforming ultra-low-field point-of-care MRI using high-field MRI representationsIslam, Kh Tohidul, Dayarathna, Sanuwani, Zhong, Shenjun, Zakavi, Parisa, Kavnoudias, Helen, Farquharson, Shawna, Durbridge, Gail, Sun, Hongfu, Bacchi, Stephen, Egan, Gary F., Barth, Markus, Dwyer, Andrew, McMahon, Katie L., Parizel, Paul M., Law, Meng and Chen, Zhaolin (2026). SynPoC: a high-quality generative diffusion model for transforming ultra-low-field point-of-care MRI using high-field MRI representations. Scientific Reports, 16 (1) 3285, 3285. doi: 10.1038/s41598-025-33162-9 |
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2025 Journal Article Editorial for: “Accelerating <scp>2D</scp> Kidney Magnetic Resonance Fingerprinting Using Deep Learning Based Tissue Quantification”Sun, Hongfu (2025). Editorial for: “Accelerating 2D Kidney Magnetic Resonance Fingerprinting Using Deep Learning Based Tissue Quantification”. Journal of Magnetic Resonance Imaging, 63 (2) jmri.70151, 536-537. doi: 10.1002/jmri.70151 |
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2025 Journal Article DIP-UP: Deep Image Prior for Unwrapping PhaseZhu, Xuanyu, Gao, Yang, Xiong, Zhuang, Jiang, Wei, Liu, Feng and Sun, Hongfu (2025). DIP-UP: Deep Image Prior for Unwrapping Phase. Information, 16 (7) 592, 1-18. doi: 10.3390/info16070592 |
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2025 Journal Article Quantitative susceptibility mapping via deep neural networks with iterative reverse concatenations and recurrent modulesLi, 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 |
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2025 Journal Article MRF-mixer: a simulation-based deep learning framework for accelerated and accurate magnetic resonance fingerprinting reconstructionDing, 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 |
