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深度学习图像重建算法的影响,以减少辐射剂量和图像噪声,与代重建和过后投影相比,用于头部和胸部计算机断层扫描检查:系统性审查

Obhuli Chandran M¹, Saikiran Pendem¹, Priya P S²

¹Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.

|May 10, 2024

概括

深度学习图像重建 (DLIR) 提高图像质量,同时减少头部和胸部CT扫描中的辐射剂量和图像噪声. 这种先进的技术有望改善患者护理和诊断准确度.

科学领域:

放射学放射学是一门学科.
医疗成像医学成像
人工智能在医学中的应用

背景情况:

深度学习图像重建 (DLIR) 是一种先进的计算机断层扫描 (CT) 技术.
与传统的代重建 (IR) 和过后投影 (FBP) 方法相比,DLIR具有潜在的优势.
进一步调查DLIR在头部和胸部CT中的有效性是有必要的.

研究的目的:

审查DLIR对辐射剂量 (RD) 和图像噪声 (IN) 的影响.
评估DLIR在头部和胸部CT中的性能与IR和FBP相比.
评估在CT检查中使用DLIR的研究的总体结果.

主要方法:

从2017年到2023年,在主要数据库 (PubMed,Scopus,科学网,Cochrane图书馆,Embase) 进行了系统的文献搜索.
确定并选择了报告DLIR在头部和胸部CT使用的研究.
数据提取遵循系统审查和元分析 (PRISMA) 准则的首选报告项目.

主要成果:

包括15项高到中等质量的研究,涉及1292名参与者.
DLIR显示图像质量 (IQ) 的改善.
与IR和FBP相比,DLIR显著降低了辐射剂量 (RD) 和图像噪声 (IN).

结论:

DLIR算法提高图像质量,减少头部和胸部CT的图像噪声.
在不影响诊断准确性的情况下,DLIR可以降低辐射剂量.
通过尽量减少辐射风险和提高诊断能力,DLIR具有改善患者护理的巨大潜力.

关键词:

计算机断层扫描 (CT) 是一种计算机断层扫描.深度学习图像重建图像重建图像质量图像质量代的重建技术代的重建技术低剂量的低剂量

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