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ENGLISH ABSTRACT
CT深度学习图像重建算法临床应用中国专家共识
中国医师协会放射医师分会
中华医学会影像技术分会
中国医师协会医学技师专业委员会
中国医学装备协会放射影像装备分会
作者及单位信息
·
DOI： 10.3760/cma.j.cn112149-20240111-00021
Chinese expert consensus on the clinical application of deep learning image reconstruction algorithms for CT
Chinese Association of Radiologists
Chinese Society of Imaging Technology of Chinese Medical Association
Chinese Professional Committee of Medical Technologists
Chinese Association of Radiological Equipment
Jin Zhengyu
Li Zhenlin
Gao Jianbo
Authors Info & Affiliations
Chinese Association of Radiologists
Chinese Society of Imaging Technology of Chinese Medical Association
Chinese Professional Committee of Medical Technologists
Chinese Association of Radiological Equipment
Jin Zhengyu
Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
Li Zhenlin
Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
Gao Jianbo
Department of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
·
DOI： 10.3760/cma.j.cn112149-20240111-00021
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摘要

图像重建算法是CT算法中的核心。近年来深度学习图像重建（DLIR）算法逐渐应用于临床，显著改善CT的图像质量和辐射剂量，在疾病的诊断中发挥重要作用。为推动 DLIR在我国的规范化应用，中国医师协会放射医师分会和医学技师专业委员会、中华医学会影像技术分会和中国医学装备协会放射影像装备分会组织专家参考大量文献并结合我国的临床实践，经过多次讨论达成以下共识，包括DLIR的技术特点和临床前实验研究，以及在头颈、心脏大血管、胸部、腹部、骨肌、儿童、急诊和能谱方面的临床应用。

体层摄影术，X线计算机;图像重建算法;深度学习
引用本文

中国医师协会放射医师分会,中华医学会影像技术分会,中国医师协会医学技师专业委员会,等. CT深度学习图像重建算法临床应用中国专家共识[J]. 中华放射学杂志,2024,58(07)：697-706.

DOI:10.3760/cma.j.cn112149-20240111-00021

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图像重建算法是CT成像质量和诊断结果可靠的重要保障。常用的图像重建算法有滤波反投影（filtered back projection，FBP）算法和迭代重建算法，不同算法在计算效率、准确度和数值稳定性等方面各有特点。深度学习图像重建（deep learning image reconstruction，DLIR）是一种新型的CT图像重建算法，突破了非线性迭代重建算法在图像质量方面的限制，有效降低图像噪声并提高图像质量，同时减少计算负载，能提供常规快速重建，已经应用于各种疾病的诊疗。随着DLIR的逐渐普及，其临床应用和解读成为迫切需求。中国医师协会放射医师分会和医学技师专业委员会、中华医学会影像技术分会和中国医学装备协会放射影像装备分会组织全国多家单位的多位专家在参考大量文献的基础上，结合DLIR临床应用方面的丰富经验，形成此共识。该共识从基本原理和技术应用出发，旨在论述DLIR算法在临床疾病诊疗中的应用价值和意义，提高诊断效能并给出推荐意见，为临床规范检查提供指导，更好地为患者健康服务。
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备注信息
A
金征宇，中国医学科学院北京协和医学院北京协和医院放射科，北京100730，Email： mocdef.3ab61.pivuygnehznij.rjc
B
李真林，四川大学华西医院放射科，成都610041，Email： mocdef.6ab2110dclzl
C
高剑波，郑州大学第一附属医院放射科，郑州450052，Email： mocdef.3ab61.pivobnaijoag.rjc
D
中国医师协会放射医师分会, 中华医学会影像技术分会, 中国医师协会医学技师专业委员会, 等. CT深度学习图像重建算法临床应用中国专家共识[J]. 中华放射学杂志, 2024, 58(7): 697-706. DOI: 10.3760/cma.j.cn112149-20240111-00021.
E
郑州大学第一附属医院放射科梁盼、王明月、侯平和王怡然在本共识编写中的贡献
F
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