通过基于深度学习的降噪处理来减少胸部X-ray中的辐射暴露:一个幻影和回顾性临床研究
K Mori1, T Negishi2, R Sekiguchi3
1Department of Radiological Technology, Saiseikai Kawaguchi General Hospital, 5-11-5 Nishikawaguchi, Kawaguchi, Saitama, 332-8558, Japan; Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, 7-2-10 Higashi-Ogu, Arakawa, Tokyo, 116-8551, Japan.
Radiography (London, England : 1995)
|April 25, 2025
概括
智能降噪 (INR) 在胸部X射线中显著降低了35%的辐射剂量,而不会影响图像质量. 这种深度学习技术为诊断成像提供了更安全的替代方案.
科学领域:
- 医疗成像医学成像
- 放射学 放射学 放射学 放射学
- 医疗保健中的人工智能
背景情况:
- 智能降噪 (INR) 是佳能为平面放射学开发的深度学习算法.
- INR的目的是提高图像质量,减少患者的辐射暴露剂量.
- 这项研究专门研究INR对平面胸部X射线图中患者剂量减少的影响.
研究的目的:
- 评估智能降噪 (INR) 在平面胸部X射线图期间降低患者辐射剂量的有效性.
- 在使用INR时,评估剂量降低是否影响图像质量.
- 为了确定胸部X射线中INR的最佳成像条件.
主要方法:
- 使用Lungman幻影的平均意见得分 (MOS) 评估模拟瘤可见性.
- 在100名患者中使用盲人/无参考图像空间质量评估器 (BRISQUE) 评估临床图像质量.
- 标准放射 (对照组) 和INR辅助放射 (评估组) 之间的入口表面剂量 (Ka,e) 的比较.
主要成果:
- 在模拟质量可见性 (MOS) 中没有发现显著差异,剂量减少为33.33%.
- 临床图像质量 (BRISQUE) 在对照组和评估组之间仍然可比 (p=0.35).
- 在INR组 (p<0.001) 中,患者入口表面剂量 (Ka,e) 显著减少,平均为35%.
结论:
- 智能降噪 (INR) 有效地在胸部X光学中将患者的辐射剂量降低约35%,而不会影响图像质量.
- 广泛采用INR可能会导致更低的诊断参考水平和更好的患者安全.
- 在临床实践中,INR显示了剂量降低的巨大潜力.
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