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Scientific Reports
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August 20, 2020
Deep-learning-based image quality enhancement of compressed sensing magnetic resonance imaging of vessel wall: comparison of self-supervised and unsupervised approaches
Da-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 factors
Minjae 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 segmentation
Youngjae Kim, Yura Ahn, Sang Min Lee, et al.
Medical Physics
|
December 14, 2011
Predicting the fidelity of JPEG2000 compressed CT images using DICOM header information
Kil 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 patients
Leehi Joo, Seung Chai Jung, Hyunna Lee, et al.
Korean Journal of Radiology
|
July 4, 2017
Deep Learning in Medical Imaging: General Overview
June-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 thicknesses
Hyunna Lee, Kyoung Ho Lee, Kil Joong Kim, et al.
Medical Physics
|
September 21, 2011
Introduction of heat map to fidelity assessment of compressed CT images
Hyunna 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 Reproducibility
Yura 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 Study
Yura Ahn, Sang Min Lee, Youngjae Kim, et al.
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of 5
Search research articles
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Showing results (11-20 of 41) with videos related to
Sort By:
Page
of 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 approaches
Da-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 factors
Minjae 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 segmentation
Youngjae Kim, Yura Ahn, Sang Min Lee, et al.
Medical Physics
|
December 14, 2011
Predicting the fidelity of JPEG2000 compressed CT images using DICOM header information
Kil 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 patients
Leehi Joo, Seung Chai Jung, Hyunna Lee, et al.
Korean Journal of Radiology
|
July 4, 2017
Deep Learning in Medical Imaging: General Overview
June-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 thicknesses
Hyunna Lee, Kyoung Ho Lee, Kil Joong Kim, et al.
Medical Physics
|
September 21, 2011
Introduction of heat map to fidelity assessment of compressed CT images
Hyunna 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 Reproducibility
Yura 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 Study
Yura Ahn, Sang Min Lee, Youngjae Kim, et al.
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of 5