Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Hyeong-Geol Shin

Showing results (1-10 of 30) with videos related to

Pageof 3
Sort By:
Neuroimage|November 22, 2025
Resolution generalization of deep learning-based dipole inversion networks for QSMSooyeon Ji, Minjun Kim, Jongho Lee, et al.
Neuroimage|March 30, 2023
Depth-wise profiles of iron and myelin in the cortex and white matter using χ-separation: A preliminary studySubin Lee, Hyeong-Geol Shin, Minjun Kim, et al.
Magnetic Resonance in Medicine|July 10, 2020
A geometric approach to separate the effects of magnetic susceptibility and chemical shift/exchange in a phantom with isotropic magnetic susceptibilityHyunsung Eun, Hwihun Jeong, Jingu Lee, et al.
Magnetic Resonance in Medicine|December 16, 2023
Exploiting gradient-echo frequency evolution: Probing white matter microstructure and extracting bulk susceptibility-induced frequency for quantitative susceptibility mappingLin Chen, Hyeong-Geol Shin, Peter C M van Zijl, et al.
Machine Learning in Clinical Neuroimaging : 6Th International Workshop, MLCN 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. MLCN (Workshop) (6Th : 2023 : Vancouver, B.C.)|March 12, 2026
WaveSep: A Flexible Wavelet-Based Approach for Source Separation in Susceptibility ImagingZhenghan Fang, Hyeong-Geol Shin, Peter van Zijl, et al.
Machine Learning in Clinical Neuroimaging : 7Th International Workshop, MLCN 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings. MLCN (Workshop) (7Th : 2024 : Marrakesh, Morocco)|January 8, 2025
ProxiMO: Proximal Multi-operator Networks for Quantitative Susceptibility MappingShmuel Orenstein, Zhenghan Fang, Hyeong-Geol Shin, et al.
Neuroimage|August 22, 2017
An R<sub>2</sub>* model of white matter for fiber orientation and myelin concentrationJingu Lee, Hyeong-Geol Shin, Woojin Jung, et al.
Magnetic Resonance in Medicine|November 1, 2019
Artificial neural network for myelin water imagingJieun Lee, Doohee Lee, Joon Yul Choi, et al.
NMR in Biomedicine|May 2, 2024
Comparison between R2'-based and R2*-based χ-separation methods: A clinical evaluation in individuals with multiple sclerosisSooyeon Ji, Jinhee Jang, Minjun Kim, et al.
Journal of Clinical Neurology (Seoul, Korea)|April 10, 2018
Diagnosis of Early-Stage Idiopathic Parkinson's Disease Using High-Resolution Quantitative Susceptibility Mapping Combined with Histogram Analysis in the Substantia Nigra at 3 TEung Yeop Kim, Young Hee Sung, Hyeong Geol Shin, et al.
Pageof 3

Showing results (1-10 of 30) with videos related to

Sort By:
Pageof 3
Neuroimage|November 22, 2025
Resolution generalization of deep learning-based dipole inversion networks for QSMSooyeon Ji, Minjun Kim, Jongho Lee, et al.
Neuroimage|March 30, 2023
Depth-wise profiles of iron and myelin in the cortex and white matter using χ-separation: A preliminary studySubin Lee, Hyeong-Geol Shin, Minjun Kim, et al.
Magnetic Resonance in Medicine|July 10, 2020
A geometric approach to separate the effects of magnetic susceptibility and chemical shift/exchange in a phantom with isotropic magnetic susceptibilityHyunsung Eun, Hwihun Jeong, Jingu Lee, et al.
Magnetic Resonance in Medicine|December 16, 2023
Exploiting gradient-echo frequency evolution: Probing white matter microstructure and extracting bulk susceptibility-induced frequency for quantitative susceptibility mappingLin Chen, Hyeong-Geol Shin, Peter C M van Zijl, et al.
Machine Learning in Clinical Neuroimaging : 6Th International Workshop, MLCN 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. MLCN (Workshop) (6Th : 2023 : Vancouver, B.C.)|March 12, 2026
WaveSep: A Flexible Wavelet-Based Approach for Source Separation in Susceptibility ImagingZhenghan Fang, Hyeong-Geol Shin, Peter van Zijl, et al.
Machine Learning in Clinical Neuroimaging : 7Th International Workshop, MLCN 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings. MLCN (Workshop) (7Th : 2024 : Marrakesh, Morocco)|January 8, 2025
ProxiMO: Proximal Multi-operator Networks for Quantitative Susceptibility MappingShmuel Orenstein, Zhenghan Fang, Hyeong-Geol Shin, et al.
Neuroimage|August 22, 2017
An R<sub>2</sub>* model of white matter for fiber orientation and myelin concentrationJingu Lee, Hyeong-Geol Shin, Woojin Jung, et al.
Magnetic Resonance in Medicine|November 1, 2019
Artificial neural network for myelin water imagingJieun Lee, Doohee Lee, Joon Yul Choi, et al.
NMR in Biomedicine|May 2, 2024
Comparison between R2'-based and R2*-based χ-separation methods: A clinical evaluation in individuals with multiple sclerosisSooyeon Ji, Jinhee Jang, Minjun Kim, et al.
Journal of Clinical Neurology (Seoul, Korea)|April 10, 2018
Diagnosis of Early-Stage Idiopathic Parkinson's Disease Using High-Resolution Quantitative Susceptibility Mapping Combined with Histogram Analysis in the Substantia Nigra at 3 TEung Yeop Kim, Young Hee Sung, Hyeong Geol Shin, et al.
Pageof 3