https://ir.ymlib.yonsei.ac.kr/handle/22282913/169019 2026-07-18T11:13:51Z 2026-07-18T11:13:51Z Jo, Gyu-Dong Jeon, Kug Jin Choi, Yoon Joo Lee, Chena Han, Sang-Sun https://ir.ymlib.yonsei.ac.kr/handle/22282913/212498 2026-06-10T05:55:40Z 2026-05-01T00:00:00Z

Title: Reply to the Letter to the Editor: Deep learning TMJ MRI-reader-level equivalence is a foundation, not a finish line Authors: Jo, Gyu-Dong; Jeon, Kug Jin; Choi, Yoon Joo; Lee, Chena; Han, Sang-Sun Abstract: [No abstract available]

2026-05-01T00:00:00Z Choi, Yoon Joo Han, Sujeong Lee, Chena Jeon, Kug Jin Oh, Haesung Han, Sang-Sun Lee, Jaesung https://ir.ymlib.yonsei.ac.kr/handle/22282913/212679 2026-06-17T06:56:14Z 2026-04-01T00:00:00Z

Title: A feasibility study of deep learning-based segmentation of the inferior alveolar nerve on magnetic resonance neurography Authors: Choi, Yoon Joo; Han, Sujeong; Lee, Chena; Jeon, Kug Jin; Oh, Haesung; Han, Sang-Sun; Lee, Jaesung Abstract: The inferior alveolar nerve (IAN) is the major sensory nerve innervating the mandibular region, and its automatic segmentation is crucial. It has been indirectly identified on the mandibular canal, which surrounds the IAN, using computed tomography (CT) and cone-beam computed tomography (CBCT). Magnetic resonance neurography (MRN) is an imaging technique designed for nerve visualization, facilitating discrimination of small peripheral nerves from surrounding soft tissues. To our knowledge, this study is the first to perform semi-automatic segmentation of the IAN using MRN images. We developed a deep learning model based on 6,027 coronal MRN images and evaluated its performance using four quantitative metrics, comparing it with six state-of-the-art models that were retrained and tested on the same dataset for small-structure segmentation. Our model achieved a dice similarity coefficient (DSC) of 0.712 +/- 0.254, significantly outperforming the six comparator models. In addition, in an analysis of segmentation failure rates according to DSC thresholds, our model demonstrated the lowest failure rate. In conclusion, unlike many previous studies that focused on bony boundaries using CBCT or CT, this study demonstrates the feasibility and potential clinical utility of MRN-based IAN segmentation.

2026-04-01T00:00:00Z Jo, Gyu-Dong Choi, Yoon Joo Lee, Chena Jeon, Kug Jin Han, Sang-Sun https://ir.ymlib.yonsei.ac.kr/handle/22282913/212756 2026-06-19T07:28:19Z 2026-04-01T00:00:00Z

Title: Deep learning for synthetic contrast-enhanced CT and MRI: a scoping review Authors: Jo, Gyu-Dong; Choi, Yoon Joo; Lee, Chena; Jeon, Kug Jin; Han, Sang-Sun Abstract: ObjectivesDeep learning-based synthetic contrast imaging has been proposed as an alternative to iodinated and gadolinium-based contrast agents in CT and MRI. This scoping review aimed to provide a cross-modality overview of current evidence, including study characteristics and validation strategies.Materials and methodsFollowing PRISMA-ScR guidelines, PubMed, Embase, Scopus, and Web of Science were searched from inception to September 2025. Eligible studies applied deep learning to synthesize contrast-enhanced CT/MRI from non-contrast or modified-contrast inputs and reported reference-based validation. Extracted variables included modality, anatomy, input type, model type, dataset scale, validation category, and four predefined evaluation metrics.ResultsFifty-six studies met the inclusion criteria (25 CT, 31 MRI). The brain was the most frequent target, followed by head and neck, breast, and liver applications. Non-contrast inputs were used in 71% of studies, with the remainder using modified-contrast strategies. Generative adversarial networks were the predominant model class, while diffusion and transformer models appeared after 2023. Dataset sizes ranged from 10 to 7306 (median, 218), and 57% of studies were single-center. Quantitative fidelity was evaluated in 88% of studies, reporting structural similarity index values of 0.73-0.99 and peak signal-to-noise ratios of 22-51 dB. Task-based performance was assessed in 39% of studies, radiologist-rated image quality in 54%, and diagnostic performance in 30%, with sensitivities of 72-92% and specificities of 59-95%.ConclusionDeep learning-based synthetic contrast imaging shows high quantitative and perceptual fidelity, but evidence supporting diagnostic interchangeability and routine clinical use remains limited.Key PointsQuestion Reducing contrast media use in CT and MRI is a clinical priority, and deep learning-based synthetic imaging is emerging as a potential alternative.Findings Deep learning generated synthetic contrast-enhanced images with high quantitative fidelity and acceptable image quality, but diagnostic performance was evaluated in only limited studies.Clinical relevance This review outlines the capabilities and limitations of deep learning-based synthetic contrast imaging, informing the development of contrast-minimizing strategies for safer CT and MRI practice.Key PointsQuestion Reducing contrast media use in CT and MRI is a clinical priority, and deep learning-based synthetic imaging is emerging as a potential alternative.Findings Deep learning generated synthetic contrast-enhanced images with high quantitative fidelity and acceptable image quality, but diagnostic performance was evaluated in only limited studies.Clinical relevance This review outlines the capabilities and limitations of deep learning-based synthetic contrast imaging, informing the development of contrast-minimizing strategies for safer CT and MRI practice.Key PointsQuestion Reducing contrast media use in CT and MRI is a clinical priority, and deep learning-based synthetic imaging is emerging as a potential alternative.Findings Deep learning generated synthetic contrast-enhanced images with high quantitative fidelity and acceptable image quality, but diagnostic performance was evaluated in only limited studies.Clinical relevance This review outlines the capabilities and limitations of deep learning-based synthetic contrast imaging, informing the development of contrast-minimizing strategies for safer CT and MRI practice.

2026-04-01T00:00:00Z Lee, Chena Lee, Ji Yun MacDonald, David https://ir.ymlib.yonsei.ac.kr/handle/22282913/211181 2026-03-16T00:49:15Z 2026-01-01T00:00:00Z

Title: Morphological analysis of anatomical structure of nasopharynx in cone-beam computed tomography in Korean population Authors: Lee, Chena; Lee, Ji Yun; MacDonald, David Abstract: Purpose: This study evaluated the nasopharyngeal anatomy, particularly the fossa of Rosenm & uuml;ller (FoR), on cone-beam computed tomography (CBCT) in a Korean population to establish normative reference data by sex and age. Materials and Methods: In CBCT images, FoR was classified into three types (A-C) for image analysis. Measurements of nasopharyngeal dimensions were performed in Types B and C. Sex-and age-related differences were evaluated using chi-square and independent t-tests, and reliability was assessed using the intraclass correlation coefficient. Results: In total, 492 CBCTs (244 males, 248 females; 20-69 years) were included. Type C was the most frequent morphology and increased with age. Types A and B were more prevalent among males than among females, whereas Type C was predominant among females (57.3%) compared with males (34.4%). Asymmetry was more frequent in males (13.9%) than in females (10.1%). Significant sex differences due to the larger males were found in the distance of the torus levatorius, the distance between the sphenopalatine notch and the right torus levatorius, and the horizontal and vertical dimensions of the FoR. No significant side-to-side differences were observed. Reliability was excellent (ICC = 0.97). Conclusion: Type C was the most frequent morphology in both sexes, whereas Types A and B were more frequently observed in males than in females. These differences may indirectly contribute to sex-related disparities in nasopharyngeal carcinoma (NPC) incidence. The normative reference values may aid early detection in dental imaging, and further prospective studies including NPC patients are needed to clarify the role of nasopharyngeal morphology.

2026-01-01T00:00:00Z