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    NECT CT에서 DLIR 재구성기법 적용 시 화질분석 = Image Quality Analysis when applying DLIR Reconstruction Techniques in NECT CT

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    https://www.riss.kr/link?id=A108254636

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    120 kVp FBP reconstruction image standard by using raw data after scanning by changing tube voltage among the NECK CT protocols that are broad applied in clinical practice using a human phantom including thyroid gland The usefulness of the DLIR reconstruction technique was investigated. As a result, CTDIvol decreased when the DLIR reconstruction technique was applied, and in particular, the image quality obtained under the same standard scanning conditions at a lower dose for ASIR-V and DLIR reconstruction was reached than when FBP was applied at the same kVp In addition, as a result of SNR and CNR analysis, the DLIR reconstructed image was analyzed with high SNR and CNR values, and SSIM analysis, the SSIM index of the 100 kVp, DLIR reconstructed image was measured to be close to 1, and it was analyzed that the similarity of the reconstructed image to the original image was high (p>0.05). If the results of this study are used to supplement clinical image evaluation and further develop an algorithm applicable to various anatomical structures, it is thought that it will be useful for clinical application as it is possible to maintain the image quality while lowering the examination dose.
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    120 kVp FBP reconstruction image standard by using raw data after scanning by changing tube voltage among the NECK CT protocols that are broad applied in clinical practice using a human phantom including thyroid gland The usefulness of the DLIR recons...

    120 kVp FBP reconstruction image standard by using raw data after scanning by changing tube voltage among the NECK CT protocols that are broad applied in clinical practice using a human phantom including thyroid gland The usefulness of the DLIR reconstruction technique was investigated. As a result, CTDIvol decreased when the DLIR reconstruction technique was applied, and in particular, the image quality obtained under the same standard scanning conditions at a lower dose for ASIR-V and DLIR reconstruction was reached than when FBP was applied at the same kVp In addition, as a result of SNR and CNR analysis, the DLIR reconstructed image was analyzed with high SNR and CNR values, and SSIM analysis, the SSIM index of the 100 kVp, DLIR reconstructed image was measured to be close to 1, and it was analyzed that the similarity of the reconstructed image to the original image was high (p>0.05). If the results of this study are used to supplement clinical image evaluation and further develop an algorithm applicable to various anatomical structures, it is thought that it will be useful for clinical application as it is possible to maintain the image quality while lowering the examination dose.

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    참고문헌 (Reference)

    1 차상영 ; 박재윤 ; 이용기 ; 김정훈 ; 최재호, "전산화단층촬영에서 관전압과 관전류, 통계적 반복재구성법에 따른 화질과 피폭선량" 대한방사선과학회 40 (40): 385-391, 2017

    2 이강윤 ; 김준혁, "인공지능 왓슨 기술과 보건의료의 적용" 연세대학교 의과대학 18 (18): 51-57, 2016

    3 오정민 ; 서현지 ; 김영균 ; 한동균, "복부 CT 프로토콜에서 필터 보정 역투영법과 반복적 재구성기법에 따른 화질 및 선량에 관한 연구" 한국방사선학회 15 (15): 1065-1072, 2021

    4 김현주 ; 정운관 ; 조재환, "경부 CT검사 시 Kernel 값과 검사자세 변화를 통한 화질개선에 관한 연구" 한국방사선학회 5 (5): 59-66, 2011

    5 M. J. Willemink, "The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence" 29 (29): 2185-2195, 2019

    6 International Commission on Radiological Protection, "The 2007 Recommendations of the International Commission on Radiological Protection, ICRP Publication 103" 37 (37): 1-399, 2007

    7 C. S. Ko, "Study About Comparative Analysis and Usefulness By Quantitative Evaluation of Deep Learning Image Reconstruction and Adaptive Statistical Iterative Reconstruction-V in Aortic Vessels CT" 23 (23): 9-19, 2021

    8 L. L. Geyer, "State of the Art : Iterative CT Reconstruction Techniques" 276 (276): 339-396, 2015

    9 International Commission on Radiological Protection, "Managing Patient Dose in Computed tomography, ICRP Publication 87" 30 (30): 1-41, 2000

    10 Y. Y. Lee, "Lymphomas of the head and neck : CT findings at initial presentation" 149 (149): 575-581, 1987

    1 차상영 ; 박재윤 ; 이용기 ; 김정훈 ; 최재호, "전산화단층촬영에서 관전압과 관전류, 통계적 반복재구성법에 따른 화질과 피폭선량" 대한방사선과학회 40 (40): 385-391, 2017

    2 이강윤 ; 김준혁, "인공지능 왓슨 기술과 보건의료의 적용" 연세대학교 의과대학 18 (18): 51-57, 2016

    3 오정민 ; 서현지 ; 김영균 ; 한동균, "복부 CT 프로토콜에서 필터 보정 역투영법과 반복적 재구성기법에 따른 화질 및 선량에 관한 연구" 한국방사선학회 15 (15): 1065-1072, 2021

    4 김현주 ; 정운관 ; 조재환, "경부 CT검사 시 Kernel 값과 검사자세 변화를 통한 화질개선에 관한 연구" 한국방사선학회 5 (5): 59-66, 2011

    5 M. J. Willemink, "The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence" 29 (29): 2185-2195, 2019

    6 International Commission on Radiological Protection, "The 2007 Recommendations of the International Commission on Radiological Protection, ICRP Publication 103" 37 (37): 1-399, 2007

    7 C. S. Ko, "Study About Comparative Analysis and Usefulness By Quantitative Evaluation of Deep Learning Image Reconstruction and Adaptive Statistical Iterative Reconstruction-V in Aortic Vessels CT" 23 (23): 9-19, 2021

    8 L. L. Geyer, "State of the Art : Iterative CT Reconstruction Techniques" 276 (276): 339-396, 2015

    9 International Commission on Radiological Protection, "Managing Patient Dose in Computed tomography, ICRP Publication 87" 30 (30): 1-41, 2000

    10 Y. Y. Lee, "Lymphomas of the head and neck : CT findings at initial presentation" 149 (149): 575-581, 1987

    11 Chankue Park ; Ki Seok Choo ; 김진혁 ; Kyung Jin Nam ; 이지원 ; 김진유, "Image Quality and Radiation Dose in CT Venography Using Model-Based Iterative Reconstruction at 80 kVp versus Adaptive Statistical Iterative Reconstruction-V at 70 kVp" 대한영상의학회 20 (20): 1167-1175, 2019

    12 A. Qayyum, "Head and Neck Tumor Segmentation and Outcome Prediction" 58-67, 2022

    13 R. Miyajima, "Effective Techniques to Reduce Radiation Exposure to Medical Staffduring Assist of X-ray Computed Tomography Examination" 74 (74): 326-334, 2018

    14 박용구 ; 정승은, "CT검사의 방사선 피폭과 저감화 방법" 대한의사협회 54 (54): 1262-1268, 2011

    15 홍준용 ; 박상현 ; 정영진, "AI 의료영상 분석의 개요 및 연구 현황에 대한 고찰" 대한방사선과학회 43 (43): 195-208, 2020

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    학술지 이력

    학술지 이력
    연월일 이력구분 이력상세 등재구분
    2026 평가 재인증평가 신청대상 (재인증)
    2020-01-01 등재 등재학술지 유지 (재인증) KCI등재
    2017-01-01 등재 등재학술지 선정 (계속평가) KCI등재
    2016-01-01 등재 등재후보학술지 유지 (계속평가) KCI등재후보
    2015-01-01 등재 등재후보학술지 유지 (계속평가) KCI등재후보
    2013-01-01 등재 등재후보학술지 유지 (기타) KCI등재후보
    2012-01-01 등재 등재후보학술지 유지 (기타) KCI등재후보
    2011-02-28 학술지명변경 한글명 : 한국방사선학회 논문지 -> 한국방사선학회논문지 KCI등재후보
    2010-01-01 등재 등재후보학술지 선정 (신규평가) KCI등재후보
    2008-01-24 학회명변경 한글명 : 방사선학회 -> 한국방사선학회
    영문명 : The Society of Radiology -> The Korea Society of Radiology
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    기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
    2016 0.28 0.28 0.36
    KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
    0.37 0.37 0.452 0.05
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