| Super‐resolution musculoskeletal MRI using deep learning AS Chaudhari, Z Fang, F Kogan, J Wood, KJ Stevens, EK Gibbons, ... Magnetic resonance in medicine 80 (5), 2139-2154, 2018 | 363 | 2018 |
| Frequency-selective control of cortical and subcortical networks by central thalamus J Liu, HJ Lee, AJ Weitz, Z Fang, P Lin, MK Choy, R Fisher, V Pinskiy, ... Elife 4, e09215, 2015 | 148 | 2015 |
| Optogenetic fMRI reveals distinct, frequency-dependent networks recruited by dorsal and intermediate hippocampus stimulations AJ Weitz, Z Fang, HJ Lee, RS Fisher, WC Smith, MK Choy, J Liu, P Lin, ... NeuroImage 107, 229-241, 2015 | 109 | 2015 |
| Combining optogenetic stimulation and fMRI to validate a multivariate dynamical systems model for estimating causal brain interactions S Ryali, YYI Shih, T Chen, J Kochalka, D Albaugh, Z Fang, K Supekar, ... NeuroImage 132, 398-405, 2016 | 68 | 2016 |
| Utility of deep learning super‐resolution in the context of osteoarthritis MRI biomarkers AS Chaudhari, KJ Stevens, JP Wood, AK Chakraborty, EK Gibbons, ... Journal of Magnetic Resonance Imaging 51 (3), 768-779, 2020 | 55 | 2020 |
| High spatial resolution compressed sensing (HSPARSE) functional MRI Z Fang, N Van Le, MK Choy, JH Lee Magnetic resonance in medicine 76 (2), 440-455, 2016 | 46 | 2016 |
| Diagnostic accuracy of quantitative multicontrast 5-minute knee MRI using prospective artificial intelligence image quality enhancement AS Chaudhari, MJ Grissom, Z Fang, B Sveinsson, JH Lee, GE Gold, ... American Journal of Roentgenology 216 (6), 1614-1625, 2021 | 39 | 2021 |
| In vivo visualization and control of patholigical changes in neural circuits JH Lee, Z Fang US Patent 10,478,639, 2019 | 37 | 2019 |
| High-throughput optogenetic functional magnetic resonance imaging with parallel computations Z Fang, JH Lee Journal of neuroscience methods 218 (2), 184-195, 2013 | 28 | 2013 |
| Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging A Chaudhari, Z Fang, JH Lee, G Gold, B Hargreaves International Workshop on Machine Learning for Medical Image Reconstruction …, 2018 | 23 | 2018 |
| Optogenetic functional MRI P Lin, Z Fang, J Liu, JH Lee Journal of visualized experiments: JoVE, 2016 | 19 | 2016 |
| Comparison of fMRI analysis methods for heterogeneous BOLD responses in block design studies J Liu, BA Duffy, D Bernal-Casas, Z Fang, JH Lee NeuroImage 147, 390-408, 2017 | 17 | 2017 |
| Compressed sensing high resolution functional magnetic resonance imaging JH Lee, Z Fang US Patent 10,667,691, 2020 | 11 | 2020 |
| Systems and methods for generating thin image slices from thick image slices Z Fang, AS Chaudhari, JH Lee, BA Hargreaves US Patent App. 16/979,104, 2020 | 5* | 2020 |
| Compressed sensing enabled ultra-high resolution optogenetic functional magnetic resonance imaging (ofMRI) N Van Le, TH Nguyen, X Yu, Z Fang, JH Lee Proc. Intl. Soc. Mag. Reson. Med, 2051, 2012 | 2 | 2012 |
| Convolutional neural network for real-time high spatial resolution functional magnetic resonance imaging C Alkan, Z Fang, JH Lee Proceedings of the 27th Annual Meeting of ISMRM, Montréal, QC, Canada, 4792, 2019 | 1 | 2019 |
| Efficacy and/or therapeutic parameter recommendation using individual patient data and therapeutic brain network maps Z Fang, JH Lee US Patent 11,612,353, 2023 | | 2023 |
| Compressed sensing high resolution functional magnetic resonance imaging JH Lee, Z Fang US Patent 11,357,402, 2022 | | 2022 |
| Synchronization devices and methods for synchronizing imaging systems and stimulation systems M Madsen, Z Fang, JH Lee US Patent 10,942,238, 2021 | | 2021 |
| Efficacy and/or therapeutic parameter recommendation using individual patient data and therapeutic brain network maps Z Fang, JH Lee US Patent App. 16/186,374, 2019 | | 2019 |
Brian HargreavesProfessor of Radiology, Stanford University在 stanford.edu 的电子邮件经过验证
