Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Chulkyun Ahn"'
Autor:
Gyeong Deok Jo, Chulkyun Ahn, Jung Hee Hong, Da Som Kim, Jongsoo Park, Hyungjin Kim, Jong Hyo Kim, Jin Mo Goo, Ju Gang Nam
Publikováno v:
BMC Medical Imaging, Vol 23, Iss 1, Pp 1-10 (2023)
Abstract Objective Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of th
Externí odkaz:
https://doaj.org/article/13ee5b315d8c412d93d7fd4bb3fe742c
Autor:
Chulkyun Ahn, Jong Hyo Kim
Publikováno v:
Diagnostics, Vol 14, Iss 1, p 96 (2023)
Gaining the ability to audit the behavior of deep learning (DL) denoising models is of crucial importance to prevent potential hallucinations and adversarial clinical consequences. We present a preliminary version of AntiHalluciNet, which is designed
Externí odkaz:
https://doaj.org/article/f9d23913ca364445b732eab42f5e08ad
Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study.
Publikováno v:
PLoS ONE, Vol 17, Iss 7, p e0271724 (2022)
While the recent advancements of computed tomography (CT) technology have contributed in reducing radiation dose and image noise, an objective evaluation of image quality in patient scans has not yet been established. In this study, we present a pati
Externí odkaz:
https://doaj.org/article/fb5cba01aaae4be0af7130bacc320e36
Autor:
Hyo-Jin Kang, Jeong Min Lee, Chulkyun Ahn, Jae Seok Bae, Seungchul Han, Se Woo Kim, Jeong Hee Yoon, Joon Koo Han
Publikováno v:
European Radiology. 33:3660-3670
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a2155ba26872815e11dd67c77333c53d
https://doi.org/10.1007/s00330-023-09520-4
https://doi.org/10.1007/s00330-023-09520-4
Publikováno v:
Medical Imaging 2023: Image Processing.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9cd9a6f0084edece8c65aa87f35a20fa
https://doi.org/10.1117/12.2654171
https://doi.org/10.1117/12.2654171
Publikováno v:
Progress in Medical Physics. 32:92-98
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::496d0acf87fbab9b5e5e11905fa7abb6
https://doi.org/10.14316/pmp.2021.32.4.92
https://doi.org/10.14316/pmp.2021.32.4.92
Autor:
Hyewon Choi, Hyungjin Kim, Kwang Nam Jin, Yeon Joo Jeong, Kum Ju Chae, Kyung Hee Lee, Hwan Seok Yong, Bomi Gil, Hye-Jeong Lee, Ki Yeol Lee, Kyung Nyeo Jeon, Jaeyoun Yi, Sola Seo, Chulkyun Ahn, Joonhyung Lee, Kyuhyup Oh, Jin Mo Goo
Publikováno v:
Journal of thoracic imaging. 37(4)
We aimed to identify clinically relevant deep learning algorithms for emphysema quantification using low-dose chest computed tomography (LDCT) through an invitation-based competition.The Korean Society of Imaging Informatics in Medicine (KSIIM) organ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2bbc00a2f2ef29d637c3318b6ea1cb9e
https://pubmed.ncbi.nlm.nih.gov/35749623
https://pubmed.ncbi.nlm.nih.gov/35749623
Publikováno v:
European radiology.
To investigate performance of 1-mm, sharp kernel, low-dose chest computed tomography (LDCT) for coronary artery calcium scoring (CACS) using deep learning (DL)-based denoising technique.This retrospective, intra-individual comparative study consisted
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::61f634cfaa97cfc73186bb9dcb8a053f
https://pubmed.ncbi.nlm.nih.gov/36520181
https://pubmed.ncbi.nlm.nih.gov/36520181
Autor:
Taehee Lee, Jeong Min Lee, Jeong Hee Yoon, Ijin Joo, Jae Seok Bae, Jeongin Yoo, Jae Hyun Kim, Chulkyun Ahn, Jong Hyo Kim
Publikováno v:
European radiology. 32(9)
To evaluate the diagnostic value of deep learning model (DLM) reconstructed dual-energy CT (DECT) low-keV virtual monoenergetic imaging (VMI) for assessing hypoenhancing hepatic metastases.This retrospective study included 131 patients who underwent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0627fe56deee2510ce45528de66471f1
https://pubmed.ncbi.nlm.nih.gov/35380228
https://pubmed.ncbi.nlm.nih.gov/35380228
Autor:
June Young, Seo, Ijin, Joo, Jeong Hee, Yoon, Hyo Jin, Kang, Sewoo, Kim, Jong Hyo, Kim, Chulkyun, Ahn, Jeong Min, Lee
Publikováno v:
European Journal of Radiology. 154:110390
To investigate clinical applicability of deep learning(DL)-based reconstruction of virtual monoenergetic images(VMIs) of arterial phase liver CT obtained by rapid kVp-switching dual-energy CT for evaluation of hypervascular liver lesions.We retrospec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7271228da8eac1bf5414b07e1f637df
https://doi.org/10.1016/j.ejrad.2022.110390
https://doi.org/10.1016/j.ejrad.2022.110390