Steve H. Fung VIEW CLINICAL PROFILE

Steve H. Fung, MD

Assistant Professor of Radiology, Academic Institute
Medical Director, MRI Core & Assistant Clinical Member, Research Institute
Houston Methodist
Weill Cornell Medical College


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Biography

Dr. Steve H. Fung is a neuroradiologist at the Houston Methodist Hospital, Assistant Professor of Radiology at Weill Cornell Medical College, and Medical Director of the MRI Core at the Houston Methodist Research Institute. He graduated from the University of Texas at Austin with B.S., Highest Honors in Electrical Engineering and B.S., Highest Honors in Zoology (Neuroscience) with concentrations in mathematics and physics in 1995. He earned his M.D. from Harvard Medical School, Harvard/MIT Division of Health Sciences and Technology in 2000. His research includes work in electrophysiology, MRI, optics, ultrasound, signal and image processing. He was a postdoctoral fellow in molecular imaging at the National Institutes of Health, analyzing tumor microvasculature, modulating blood-brain barrier for enhanced drug delivery, refining and understanding limitations of various kinetic models used for estimating perfusion and permeability with dynamic contrast imaging. He was also a clinical fellow in neuroradiology at the Massachusetts General Hospital, where he worked in stroke-related research, including application of DTI in acute and chronic ischemic stroke and multivariate logistic model using CT perfusion to predict functional deficits and outcome, and clinical application of fMRI and DTI for presurgical planning. As a member of the Houston Methodist Research Institute, his research interests are in advanced MRI applications that can be applied broadly to a wide spectrum of CNS diseases, including cerebrovascular, neoplastic, and neurodegenerative diseases. He is a regular reviewer for several radiology and neuroradiology journals, including AJNR American Journal of Neuroradiology since 2008.

Description of Research

-Applying advanced MRI techniques to evaluate diseases of the central nervous system, including brain tumors, cerebrovascular disease/stroke, dementia/neurodegenerative diseases, and epilepsy, to grade severity of disease and predict outcome.

-Using ASL MRI to determine regional variation of cerebral perfusion and cerebrovascular reactivity in disease states and serial changes in perfusion in response to therapeutic intervention.
-Evaluating functional connectivity using task-based and resting state functional MRI in normal and disease states.
-Analyzing tumor microvasculature and modulating the blood brain barrier for enhanced drug delivery.
Understanding limitations of kinetic models for estimating perfusion and permeability.
-Developing techniques for molecular imaging, controlled drug release, and enhanced drug delivery.

Areas Of Expertise

MRI fMRI DTI DCE-MRI ASL
Education & Training

MD, Harvard University
Research Fellowship, Imaging Sciences Training Program/National Institutes of Health
Internship, Johns Hopkins University
Clinical Fellowship, Harvard University
Residency, University of Texas Health Science Center at Houston
Publications

NS-HGlio: A generalizable and repeatable HGG segmentation and volumetric measurement AI algorithm for the longitudinal MRI assessment to inform RANO in trials and clinics
Abayazeed, AH, Abbassy, A, Müeller, M, Hill, M, Qayati, M, Mohamed, S, Mekhaimar, M, Raymond, C, Dubey, P, Nael, K, Rohatgi, S, Kapare, V, Kulkarni, A, Shiang, T, Kumar, A, Andratschke, N, Willmann, J, Brawanski, A, De Jesus, R, Tuna, I, Fung, SH, Landolfi, JC, Ellingson, BM & Reyes, M 2023, , Neuro-Oncology Advances, vol. 5, no. 1, vdac184, pp. vdac184. https://doi.org/10.1093/noajnl/vdac184

Dual Adversarial Attention Mechanism for Unsupervised Domain Adaptive Medical Image Segmentation
Chen, X, Kuang, T, Deng, H, Fung, SH, Gateno, J, Xia, JJ & Yap, PT 2022, , IEEE Transactions on Medical Imaging, vol. 41, no. 11, pp. 3445-3453. https://doi.org/10.1109/TMI.2022.3186698

Diverse data augmentation for learning image segmentation with cross-modality annotations
Chen, X, Lian, C, Wang, L, Deng, H, Kuang, T, Fung, SH, Gateno, J, Shen, D, Xia, JJ & Yap, PT 2021, , Medical Image Analysis, vol. 71, 102060. https://doi.org/10.1016/j.media.2021.102060

Anatomy-Regularized Representation Learning for Cross-Modality Medical Image Segmentation
Chen, X, Lian, C, Wang, L, Deng, H, Kuang, T, Fung, S, Gateno, J, Yap, PT, Xia, JJ & Shen, DI 2021, , IEEE Transactions on Medical Imaging, vol. 40, no. 1, 9201096, pp. 274-285. https://doi.org/10.1109/TMI.2020.3025133

A case of cerebral vasculitis due to neurobartonellosis
Poursheykhi, M, Mithani, F, Garg, T, Cajavilca, C, Jaijakul, S, Fung, S, Klucznik, R & Gadhia, R 2020, , Neurology(R) neuroimmunology & neuroinflammation, vol. 7, no. 5. https://doi.org/10.1212/NXI.0000000000000791

Obstructive hydrocephalus due to aqueductal stenosis from developmental venous anomaly draining bilateral medial thalami: a case report
Xian, Z, Fung, SH & Nakawah, MO 2020, , Radiology Case Reports, vol. 15, no. 6, pp. 730-732. https://doi.org/10.1016/j.radcr.2020.02.014

Quantitative gadolinium chelate-enhanced magnetic resonance imaging of normal endothelial barrier disruption from nanoparticle biophilicity interactions
Sarin, H, Fung, SH, Kanevsky, AS, Wu, H, Wilson, CM, Vo, H, Auh, S, Glen, D & Reynolds, R 2020, , Materials Today: Proceedings, vol. 45, pp. 3795-3799. https://doi.org/10.1016/j.matpr.2021.01.537

Co-registration of MR and FDG-PET imaging for stereotactic radiotherapy planning; case report in a previously irradiated brain metastasis with recurrent tumor and radiation necrosis
Scranton, RA, Sadrameli, S, Butler, EB, Farach, A, Wang, H-C, Teh, BS, Tremont-Lukats, IW, Fung, SH, Zanotti-Fregonara, P & Rostomily, RC 2019, , Practical Radiation Oncology. https://doi.org/10.1016/j.prro.2019.11.008

One-Shot Generative Adversarial Learning for MRI Segmentation of Craniomaxillofacial Bony Structures
Chen, X, Lian, C, Wang, L, Deng, H, Fung, SH, Nie, D, Thung, K-H, Yap, P-T, Gateno, J, Xia, JJ & Shen, D 2019, , IEEE Transactions on Medical Imaging. https://doi.org/10.1109/TMI.2019.2935409

Sleep-Disordered Breathing and Idiopathic Normal-Pressure Hydrocephalus: Recent Pathophysiological Advances
Roman, GC, Jackson, RE, Fung, SH, Zhang, YJ & Verma, A 2019, , Current Neurology and Neuroscience Reports, vol. 19, no. 7, 39. https://doi.org/10.1007/s11910-019-0952-9

Reply: Letter to The Editor on “Idiopathic normal-pressure hydrocephalus and obstructive sleep apnea are frequently associated: A prospective cohort study” Journal of the Neurological Sciences 395 (2018) 164–168
Roman, GC, Verma, A, Zhang, YJ & Fung, SH 2019, , Journal of the Neurological Sciences, vol. 397, pp. 173. https://doi.org/10.1016/j.jns.2019.01.004

“Ears of the lynx” MRI sign is associated with SPG11 and SPG15 hereditary spastic paraplegia
Pascual, MB, De Bot, ST, Daniels, MR, França, MC, Toro, C, Riverol, M, Hedera, P, Bassi, MT, Bresolin, N, Van De Warrenburg, BP, Kremer, B, Nicolai, J, Charles, P, Xu, J, Singh, S, Patronas, NJ, Fung, SH, Gregory, MD & Masdeu, JC 2019, , American Journal of Neuroradiology, vol. 40, no. 1, pp. 199-203. https://doi.org/10.3174/ajnr.A5935

Idiopathic normal-pressure hydrocephalus and obstructive sleep apnea are frequently associated: A prospective cohort study
Román, GC, Verma, AK, Zhang, YJ & Fung, SH 2018, , Journal of the Neurological Sciences, vol. 395, pp. 164-168. https://doi.org/10.1016/j.jns.2018.10.005

Advanced neuroimaging in Balo's concentric sclerosis: MRI, MRS, DTI, and ASL perfusion imaging over 1 year
Yeo, CJJ, Hutton, GJ & Fung, SH 2018, , Radiology Case Reports, vol. 13, no. 5, pp. 1030-1035. https://doi.org/10.1016/j.radcr.2018.04.010

Craniomaxillofacial Bony Structures Segmentation from MRI with Deep-Supervision Adversarial Learning
Zhao, M, Wang, L, Chen, J, Nie, D, Cong, Y, Ahmad, S, Ho, A, Yuan, P, Fung, SH, Deng, HH, Xia, J & Shen, D 2018, . in AF Frangi, G Fichtinger, JA Schnabel, C Alberola-López & C Davatzikos (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. vol. 11073, Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer-Verlag, pp. 720-727, 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, Spain, 9/16/18. https://doi.org/10.1007/978-3-030-00937-3_82

The long-term sequelae of palliative radiation therapy to lumbosacral spine using conventional PA-single portal technique
Hsiao, KY, Wang, HC, Fung, SH, Haque, W, Butler, EB & Teh, BS 2018, , Practical Radiation Oncology, vol. 8, no. 6, pp. 376-381. https://doi.org/10.1016/j.prro.2018.04.003

Transulcal parafascicular minimally invasive approach to deep and subcortical cavernomas: technical note
Scranton, RA, Fung, SH & Britz, GW 2016, , Journal of Neurosurgery, vol. 125, no. 6, pp. 1360–1366. https://doi.org/10.3171/2015.12.JNS152185

Technical pitfalls of signal truncation in perfusion MRI of glioblastoma
Wong, KK, Fung, SH, New, PZ & Wong, STC 2016, , Frontiers in Neurology, vol. 7, no. AUG, 121, pp. 121. https://doi.org/10.3389/fneur.2016.00121

Paramagnetic Gd3+ labeled red blood cells for magnetic resonance angiography
Aryal, S, Stigliano, C, Key, J, Ramirez, M, Anderson, J, Karmonik, C, Fung, S & Decuzzi, P 2016, , Biomaterials, vol. 98, pp. 163-170. https://doi.org/10.1016/j.biomaterials.2016.05.002

Epithelioid glioblastoma presenting as massive intracerebral hemorrhage: Case report and review of the literature
Liebelt, B, Boghani, Z, Takei, H, Fung, S & Britz, G 2015, , Surgical Neurology International, vol. 6, no. 3, pp. S97-S100. https://doi.org/10.4103/2152-7806.153643

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