The University of Queensland

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

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

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

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

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 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

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

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

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

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

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

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

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

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

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

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, 50 (6) PII S031716712200316X, 1-8. doi: 10.1017/cjn.2022.316

Detecting monocyte trafficking in an animal model of glioblastoma using R2* and quantitative susceptibility mapping

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

Brain volume and magnetic susceptibility differences in children and adolescents with prenatal alcohol exposure

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

Instant tissue field and magnetic susceptibility mapping from MRI raw phase using Laplacian enhanced deep neural networks

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

Quantitative susceptibility-weighted imaging in presence of strong susceptibility sources: application to hemorrhage

De, Ashmita, Sun, Hongfu, Emery, Derek J., Butcher, Kenneth S. and Wilman, Alan H. (2022). Quantitative susceptibility-weighted imaging in presence of strong susceptibility sources: application to hemorrhage. Magnetic Resonance Imaging, 92, 224-231. doi: 10.1016/j.mri.2022.06.010

BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources

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, 33 (4), 578-590. doi: 10.1016/j.zemedi.2022.08.001

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

Distinguishing COVID-19 from influenza pneumonia in the early stage through CT imaging and clinical features

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

Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning

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

Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction

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

On the regularization of feature fusion and mapping for fast MR multi-contrast imaging via iterative networks

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

xQSM: quantitative susceptibility mapping with octave convolutional and noise-regularized neural networks

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

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

Age-related differences in cerebral blood flow and cortical thickness with an application to age prediction

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

Amide signal intensities may be reduced in the motor cortex and the corticospinal tract of ALS patients

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

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

A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study

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

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

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

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

Cerebral OEF quantification: a comparison study between quantitative susceptibility mapping and dual‐gas calibrated BOLD imaging

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

Five year iron changes in relapsing-remitting multiple sclerosis deep gray matter compared to healthy controls

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

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

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

Discriminative analysis of regional evolution of iron and myelin/calcium in deep gray matter of multiple sclerosis and healthy subjects

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

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

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

Progressive iron accumulation across multiple sclerosis phenotypes revealed by sparse classification of deep gray matter

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

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

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

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

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

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

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

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

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

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

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

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.

Rapid region-of-interest MRI reconstruction using context-aware rapid region-of-interest MRI reconstruction using context-aware non-local U-net

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.

Simultaneous T1-weighted imaging, R2* mapping, and QSM from a multi-echo MPRAGE sequence using a radial fan-beam sampling scheme at 3 Tesla

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.