Full metadata record
DC Field | Value | Language |
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dc.contributor.author | 이정경 | * |
dc.contributor.author | 김진실 | * |
dc.contributor.author | 이혜아 | * |
dc.date.accessioned | 2023-01-18T16:32:51Z | - |
dc.date.available | 2023-01-18T16:32:51Z | - |
dc.date.issued | 2023 | * |
dc.identifier.issn | 0720-048X | * |
dc.identifier.other | OAK-32865 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/263841 | - |
dc.description.abstract | Purpose: This study determined whether image quality and detectability of ultralow-dose hepatic multiphase CT (ULDCT, 33.3% dose) using a vendor-agnostic deep learning model(DLM) are noninferior to those of standard-dose CT (SDCT, 100% dose) using model-based iterative reconstruction(MBIR) in patients with chronic liver disease focusing on arterial phase. Methods: Sixty-seven patients underwent hepatic multiphase CT using a dual-source scanner to obtain two different radiation dose CT scans (100%, SDCT and 33.3%, ULDCT). ULDCT using DLM and SDCT using MBIR were compared. A margin of −0.5 for the difference between the two protocols was pre-defined as noninferiority of the overall image quality of the arterial phase image. Quantitative image analysis (signal to noise ratio[SNR] and contrast to noise ratio[CNR]) was also conducted. The detectability of hepatic arterial focal lesions was compared using the Jackknife free-response receiver operating characteristic analysis. Non-inferiority was satisfied if the margin of the lower limit of 95%CI of the difference in figure-of-merit was less than –0.1. Results: Mean overall arterial phase image quality scores with ULDCT using DLM and SDCT using MBIR were 4.35 ± 0.57 and 4.08 ± 0.58, showing noninferiority (difference: −0.269; 95 %CI, −0.374 to −0.164). ULDCT using DLM showed a significantly superior contrast-to-noise ratio of arterial enhancing lesion (p < 0.05). Figure-of-merit for detectability of arterial hepatic focal lesion was 0.986 for ULDCT using DLM and 0.963 for SDCT using MBIR, showing noninferiority (difference: −0.023, 95 %CI: –0.016 to 0.063). Conclusion: ULDCT using DLM with 66.7% dose reduction showed non-inferior overall image quality and detectability of arterial focal hepatic lesion compared to SDCT using MBIR. © 2022 Elsevier B.V. | * |
dc.language | English | * |
dc.publisher | Elsevier Ireland Ltd | * |
dc.subject | Deep learning | * |
dc.subject | Hepatocellular carcinoma | * |
dc.subject | Image reconstruction | * |
dc.subject | Multidetector computed tomography | * |
dc.subject | Radiation | * |
dc.title | Ultra-low-dose hepatic multiphase CT using deep learning-based image reconstruction algorithm focused on arterial phase in chronic liver disease: A non-inferiority study | * |
dc.type | Article | * |
dc.relation.volume | 159 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.journaltitle | European Journal of Radiology | * |
dc.identifier.doi | 10.1016/j.ejrad.2022.110659 | * |
dc.identifier.wosid | WOS:000917421800001 | * |
dc.identifier.scopusid | 2-s2.0-85145290245 | * |
dc.author.google | Lee H.J. | * |
dc.author.google | Kim J.S. | * |
dc.author.google | Lee J.K. | * |
dc.author.google | Lee H.A. | * |
dc.author.google | Pak S. | * |
dc.contributor.scopusid | 이정경(35316081600) | * |
dc.contributor.scopusid | 김진실(57189764787) | * |
dc.contributor.scopusid | 이혜아(57188947704) | * |
dc.date.modifydate | 20240318141604 | * |