用深度学习图像重建技术生成的1.25毫米薄截面图像可以取代腹部CT中的标准护理5毫米图像吗?
Jinjin Cao1, Nayla Mroueh1, Nisanard Pisuchpen1,2
1Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA.
Abdominal radiology (New York)
|June 27, 2023
概括
基于深度学习的图像重建 (DLIR) 可以生成比传统方法高质量的薄截面腹部CT图像. 这一进步可以提高诊断性能,而无需增加辐射剂量.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 图像处理 图像处理
背景情况:
- CT图像重建已经从过后投影到代方法.
- 基于深度学习的图像重建 (DLIR) 是一种新兴的技术,利用深度卷积神经网络来提高图像质量.
研究的目的:
- 用DLIR重建的1.25毫米薄截面腹部CT图像的图像质量与使用自适应统计代重建 (ASIR-V) 重建的5毫米厚图像进行比较.
主要方法:
- 一项回顾性研究分析了52名患者的腹部CT扫描.
- 图像使用40%的ASIR-V (5毫米) 和三个强度的DLIR (1.25毫米) 进行了重建.
- 定性和定量评估评估了图像噪声,对比度,结构可见性,清晰度,工件和对比度与噪声比率 (CNR).
主要成果:
- 与ASIR-V相比,DLIR (中高强度) 的图像质量得分优于ASIR-V.
- 在噪音,对比度,小结构可见性,清晰度和图像偏好 (p<0.05) 方面,DLIR-H获得了最佳分数.
- DLIR重建显示出更好的文物评级,DLIR-H显示出最低的噪音和最高的CNR (p<0.001).
结论:
- 通过DLIR,可以创建薄截面 (1.25毫米) 的腹部CT图像,以提高图像质量和在5毫米ASIR-V图像上达成读者间协议.
- 在没有额外的辐射暴露的情况下,DLIR在临床实践中提供了更好的诊断性能.
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