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Hyunna Lee

Showing results (11-20 of 41) with videos related to

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Scientific Reports|August 20, 2020
Deep-learning-based image quality enhancement of compressed sensing magnetic resonance imaging of vessel wall: comparison of self-supervised and unsupervised approachesDa-In Eun, Ryoungwoo Jang, Woo Seok Ha, et al.
European Radiology|March 18, 2021
Reproducibility of radiomic features in SENSE and compressed SENSE: impact of acceleration factorsMinjae Kim, Seung Chai Jung, Ji Eun Park, et al.
Diagnostic and Interventional Radiology (Ankara, Turkey)|December 1, 2025
Automated evaluation of pulmonary lesion changes on chest radiograph during follow-up using semantic segmentationYoungjae Kim, Yura Ahn, Sang Min Lee, et al.
Medical Physics|December 14, 2011
Predicting the fidelity of JPEG2000 compressed CT images using DICOM header informationKil Joong Kim, Bohyoung Kim, Hyunna Lee, et al.
Scientific Reports|August 26, 2021
Stability of MRI radiomic features according to various imaging parameters in fast scanned T2-FLAIR for acute ischemic stroke patientsLeehi Joo, Seung Chai Jung, Hyunna Lee, et al.
Korean Journal of Radiology|July 4, 2017
Deep Learning in Medical Imaging: General OverviewJune-Goo Lee, Sanghoon Jun, Young-Won Cho, et al.
Medical Physics|October 1, 2010
Advantage in image fidelity and additional computing time of JPEG2000 3D in comparison to JPEG2000 in compressing abdomen CT image datasets of different section thicknessesHyunna Lee, Kyoung Ho Lee, Kil Joong Kim, et al.
Medical Physics|September 21, 2011
Introduction of heat map to fidelity assessment of compressed CT imagesHyunna Lee, Bohyoung Kim, Kil Joong Kim, et al.
Academic Radiology|July 29, 2023
Deep Learning-Based CT Reconstruction Kernel Conversion in the Quantification of Interstitial Lung Disease: Effect on ReproducibilityYura Ahn, Sang Min Lee, Yujin Nam, et al.
AJR. American Journal of Roentgenology|December 31, 2025
Quantitative CT Measurements of Interstitial Lung Disease: Same-Day Variability Between Two Vendors-A Prospective StudyYura Ahn, Sang Min Lee, Youngjae Kim, et al.
Pageof 5

Showing results (11-20 of 41) with videos related to

Sort By:
Pageof 5
Scientific Reports|August 20, 2020
Deep-learning-based image quality enhancement of compressed sensing magnetic resonance imaging of vessel wall: comparison of self-supervised and unsupervised approachesDa-In Eun, Ryoungwoo Jang, Woo Seok Ha, et al.
European Radiology|March 18, 2021
Reproducibility of radiomic features in SENSE and compressed SENSE: impact of acceleration factorsMinjae Kim, Seung Chai Jung, Ji Eun Park, et al.
Diagnostic and Interventional Radiology (Ankara, Turkey)|December 1, 2025
Automated evaluation of pulmonary lesion changes on chest radiograph during follow-up using semantic segmentationYoungjae Kim, Yura Ahn, Sang Min Lee, et al.
Medical Physics|December 14, 2011
Predicting the fidelity of JPEG2000 compressed CT images using DICOM header informationKil Joong Kim, Bohyoung Kim, Hyunna Lee, et al.
Scientific Reports|August 26, 2021
Stability of MRI radiomic features according to various imaging parameters in fast scanned T2-FLAIR for acute ischemic stroke patientsLeehi Joo, Seung Chai Jung, Hyunna Lee, et al.
Korean Journal of Radiology|July 4, 2017
Deep Learning in Medical Imaging: General OverviewJune-Goo Lee, Sanghoon Jun, Young-Won Cho, et al.
Medical Physics|October 1, 2010
Advantage in image fidelity and additional computing time of JPEG2000 3D in comparison to JPEG2000 in compressing abdomen CT image datasets of different section thicknessesHyunna Lee, Kyoung Ho Lee, Kil Joong Kim, et al.
Medical Physics|September 21, 2011
Introduction of heat map to fidelity assessment of compressed CT imagesHyunna Lee, Bohyoung Kim, Kil Joong Kim, et al.
Academic Radiology|July 29, 2023
Deep Learning-Based CT Reconstruction Kernel Conversion in the Quantification of Interstitial Lung Disease: Effect on ReproducibilityYura Ahn, Sang Min Lee, Yujin Nam, et al.
AJR. American Journal of Roentgenology|December 31, 2025
Quantitative CT Measurements of Interstitial Lung Disease: Same-Day Variability Between Two Vendors-A Prospective StudyYura Ahn, Sang Min Lee, Youngjae Kim, et al.
Pageof 5