剂量优化使用深度学习工具在Urolithiasis的各种CT协议中:一个物理人类幻影研究
Jae Hun Shim1, Se Young Choi1, In Ho Chang1
1Department of Urology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06973, Republic of Korea.
Medicina (Kaunas, Lithuania)
|September 28, 2023
概括
深度学习可以将CT扫描的辐射剂量减少三分之一,同时保持图像质量. 这种人工智能工具提高了诊断准确度,特别是在较低的辐射设置下,为医学成像提供了一个有前途的方法.
科学领域:
- 医疗成像医学成像
- 放射学中的人工智能
- 辐射剂量优化 辐射剂量优化
背景情况:
- 确定CT扫描的最佳辐射剂量对于保持图像质量至关重要.
- 深度学习 (DL) 应用程序显示出增强医疗图像的潜力.
- 评估DL对图像质量和辐射剂量的影响至关重要.
研究的目的:
- 评估深度学习应用程序在优化CT成像辐射剂量的有效性.
- 通过减少辐射暴露来保持诊断图像质量.
- 将深度学习增强的图像与传统的重建方法进行比较.
主要方法:
- 尿酸石头被放置在一个物理的人类幻影中.
- 用不同的管电压 (120, 100, 80 kV) 和电流时间产物 (100, 70, 30, 15 mAs) 进行CT扫描.
- 图像使用过后投影 (FBP),代重建 (IR,iDose) 和基于知识的代模型重建 (IMR) 进行了重建,使用和不使用深度学习应用程序.
- 放射科医生和泌尿科医生的客观 (霍恩斯菲尔德单位标准偏差) 和主观评估被用来评估图像质量和诊断准确性.
主要成果:
- 深度学习应用程序在所有重建方法中减少了客观图像噪声.
- 应用深度学习的FBP在约三分之一的辐射剂量 (100kV-30mAs) 达到与IR相似的噪声水平.
- 主观图像质量得分在辐射剂量低于100kV-30mAs时恶化.
- 通过深度学习,诊断的准确性在100kV-30mAs以下的设置中得到改善,除了80kV-15mAs.
结论:
- 应用深度学习的FBP在100kV-30 mAs或更高的设置下显示了与IR相似的图像质量.
- 通过使用深度学习,在100kV-30mAs下,可以减少约三分之一的辐射剂量,同时保持客观噪声水平.
- 深度学习有望减少CT成像中的辐射暴露,而不会影响诊断性能.
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