Search Results - "Ahn, Chulkyun"

Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques by Nam, Ju Gang, Ahn, Chulkyun, Choi, Hyewon, Hong, Wonju, Park, Jongsoo, Kim, Jong Hyo, Goo, Jin Mo

“…Objective To compare the image quality between the vendor-agnostic and vendor-specific algorithms on ultralow-dose chest CT. Methods Vendor-agnostic deep…”
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Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study by Chun, Minsoo, Choi, Jin Hwa, Kim, Sihwan, Ahn, Chulkyun, Kim, Jong Hyo

“…While the recent advancements of computed tomography (CT) technology have contributed in reducing radiation dose and image noise, an objective evaluation of…”
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AntiHalluciNet: A Potential Auditing Tool of the Behavior of Deep Learning Denoising Models in Low-Dose Computed Tomography by Ahn, Chulkyun, Kim, Jong Hyo

“…Gaining the ability to audit the behavior of deep learning (DL) denoising models is of crucial importance to prevent potential hallucinations and adversarial…”
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Image quality in liver CT: low-dose deep learning vs standard-dose model-based iterative reconstructions by Park, Sungeun, Yoon, Jeong Hee, Joo, Ijin, Yu, Mi Hye, Kim, Jae Hyun, Park, Junghoan, Kim, Se Woo, Han, Seungchul, Ahn, Chulkyun, Kim, Jong Hyo, Lee, Jeong Min

“…Objectives To compare the overall image quality and detectability of significant (malignant and pre-malignant) liver lesions of low-dose liver CT (LDCT, 33.3%…”
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Dose reduction potential of vendor-agnostic deep learning model in comparison with deep learning–based image reconstruction algorithm on CT: a phantom study by Choi, Hyunsu, Chang, Won, Kim, Jong Hyo, Ahn, Chulkyun, Lee, Heejin, Kim, Hae Young, Cho, Jungheum, Lee, Yoon Jin, Kim, Young Hoon

“…Objectives To compare the dose reduction potential (DRP) of a vendor-agnostic deep learning model (DLM, ClariCT.AI) with that of a vendor-specific deep…”
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Deep learning–based image reconstruction of 40-keV virtual monoenergetic images of dual-energy CT for the assessment of hypoenhancing hepatic metastasis by Lee, Taehee, Lee, Jeong Min, Yoon, Jeong Hee, Joo, Ijin, Bae, Jae Seok, Yoo, Jeongin, Kim, Jae Hyun, Ahn, Chulkyun, Kim, Jong Hyo

“…Objectives To evaluate the diagnostic value of deep learning model (DLM) reconstructed dual-energy CT (DECT) low-keV virtual monoenergetic imaging (VMI) for…”
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Correction to: Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques by Nam, Ju Gang, Ahn, Chulkyun, Choi, Hyewon, Hong, Wonju, Park, Jongsoo, Kim, Jong Hyo, Goo, Jin Mo

“…A Correction to this paper has been published: https://doi.org/10.1007/s00330-021-07733-z…”
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Deep Learning Algorithm for Simultaneous Noise Reduction and Edge Sharpening in Low-Dose CT Images: A Pilot Study Using Lumbar Spine CT by Yeoh, Hyunjung, Hong, Sung Hwan, Ahn, Chulkyun, Choi, Ja-Young, Chae, Hee-Dong, Yoo, Hye Jin, Kim, Jong Hyo

“…Objective The purpose of this study was to assess whether a deep learning (DL) algorithm could enable simultaneous noise reduction and edge sharpening in…”
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Incremental Image Noise Reduction in Coronary CT Angiography Using a Deep Learning-Based Technique with Iterative Reconstruction by Hong, Jung Hee, Park, Eun Ah, Lee, Whal, Ahn, Chulkyun, Kim, Jong Hyo

“…To assess the feasibility of applying a deep learning-based denoising technique to coronary CT angiography (CCTA) along with iterative reconstruction for…”
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75% radiation dose reduction using deep learning reconstruction on low-dose chest CT by Jo, Gyeong Deok, Ahn, Chulkyun, Hong, Jung Hee, Kim, Da Som, Park, Jongsoo, Kim, Hyungjin, Kim, Jong Hyo, Goo, Jin Mo, Nam, Ju Gang

“…Abstract Objective Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to…”
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Application of a Deep Learning-Based Contrast-Boosting Algorithm to Low-Dose Computed Tomography Pulmonary Angiography With Reduced Iodine Load by Park, Minsu, Hwang, Minhee, Lee, Ji Won, Kim, Kun-Il, Ahn, Chulkyun, Suh, Young Ju, Jeong, Yeon Joo

“…The aim of this study was to assess the effectiveness of a deep learning-based image contrast-boosting algorithm by enhancing the image quality of low-dose…”
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Performance of 1-mm non-gated low-dose chest computed tomography using deep learning-based noise reduction for coronary artery calcium scoring by Choi, Hyewon, Park, Eun-Ah, Ahn, Chulkyun, Kim, Jong-Hyo, Lee, Whal, Jeong, Baren

“…Objective To investigate performance of 1-mm, sharp kernel, low-dose chest computed tomography (LDCT) for coronary artery calcium scoring (CACS) using deep…”
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Low dose of contrast agent and low radiation liver computed tomography with deep-learning-based contrast boosting model in participants at high-risk for hepatocellular carcinoma: prospective, randomized, double-blind study by Kang, Hyo-Jin, Lee, Jeong Min, Ahn, Chulkyun, Bae, Jae Seok, Han, Seungchul, Kim, Se Woo, Yoon, Jeong Hee, Han, Joon Koo

“…Objective To investigate the image quality and lesion conspicuity of a deep-learning-based contrast-boosting (DL-CB) algorithm on double-low-dose (DLD) CT of…”
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Deep learning-based reconstruction of virtual monoenergetic images of kVp-switching dual energy CT for evaluation of hypervascular liver lesions: Comparison with standard reconstruction technique by Seo, June Young, Joo, Ijin, Yoon, Jeong Hee, Kang, Hyo Jin, Kim, Sewoo, Kim, Jong Hyo, Ahn, Chulkyun, Lee, Jeong Min

“…To investigate clinical applicability of deep learning(DL)-based reconstruction of virtual monoenergetic images(VMIs) of arterial phase liver CT obtained by…”
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Deep learning-based iodine contrast-augmenting algorithm for low-contrast-dose liver CT to assess hypovascular hepatic metastasis by Lee, Taehee, Yoon, Jeong Hee, Park, Jin Young, Lee, Jihyuk, Choi, Jae Won, Ahn, Chulkyun, Lee, Jeong Min

“…Purpose To investigate the image quality and diagnostic performance of low-contrast-dose liver CT using a deep learning-based iodine contrast-augmenting…”
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A Challenge for Emphysema Quantification Using a Deep Learning Algorithm With Low-dose Chest Computed Tomography by Choi, Hyewon, Kim, Hyungjin, Jin, Kwang Nam, Jeong, Yeon Joo, Chae, Kum Ju, Lee, Kyung Hee, Yong, Hwan Seok, Gil, Bomi, Lee, Hye-Jeong, Lee, Ki Yeol, Jeon, Kyung Nyeo, Yi, Jaeyoun, Seo, Sola, Ahn, Chulkyun, Lee, Joonhyung, Oh, Kyuhyup, Goo, Jin Mo

“…Purpose: We aimed to identify clinically relevant deep learning algorithms for emphysema quantification using low-dose chest computed tomography (LDCT) through…”
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