评估基于深度学习的软件在无对比头部CT扫描中的图像质量和影响
Denise Bos1, Aydin Demircioğlu2, Julia Neuhoff3
1Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany. denise.bos@uk-essen.de.
Scientific reports
|May 23, 2024
概括
深度学习软件通过降低噪音而提高了头部CT扫描中的客观图像质量,而不是过后投影. 然而,主观评估显示,对图像质量没有显著的临床影响.
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:
- 在无对比头部CT中评估图像质量对于诊断至关重要.
- 传统的重建技术,如过后投影 (FBP) 和代重建 (IR) 有局限性.
- 基于深度学习 (DL) 的软件为图像质量增强提供了潜力.
研究的目的:
- 评估不同CT重建技术和基于DL的软件的客观和主观图像质量.
- 在头部CT成像中比较DL后处理与FBP和IR.
- 确定DL软件对图像质量的临床影响.
主要方法:
- 从三种不同的扫描仪对152个成年人头部CT扫描进行了回顾性分析.
- 使用基于FBP,IR和DL的软件 (PS) 进行重建.
- 客观 (降噪,SNR改进) 和主观 (评分者偏好) 的图像质量评估.
主要成果:
- 在FBP图像中,DL后处理显著降低了噪声 (高达15.4%) 和改善了SNR (高达19.7%).
- DL与IR之间的差异是特定于协议/地点的,并且往往不显著.
- 主观评估显示,图像质量没有显著的整体改善,评级者之间的可靠性很低.
结论:
- 与头部CT中的FBP相比,基于DL的无噪声软件可以提高客观图像质量.
- 对主观图像质量的临床影响并不显著,可能是由于全剂量图像中的噪声水平已经很低.
- 可能需要进一步的研究来优化DL在不同协议和站点上的应用.
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