基于深度学习的扫描范围优化可以减少冠状动脉CT血管学中的辐射暴露.
Aydin Demircioğlu1, Denise Bos2, Ender Demircioğlu3
1Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany. aydin.demircioglu@uk-essen.de.
一个深度神经网络准确地界定了心脏CT中的扫描范围,显著减少了患者的辐射暴露. 这种自动化方法优化了成像协议,并降低了冠状动脉血管学中的辐射剂量.
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:
- 心脏计算机断层扫描 (CT) 对于诊断冠心病至关重要.
- 患者的辐射暴露是CT的一个关键问题,受扫描范围的影响.
- 优化CT定位器扫描范围可以降低辐射剂量.
研究的目的:
- 开发一个深度神经网络 (DNN) 来自动化CT定位器中的扫描范围划分.
- 对于接受心脏CT检查的患者,减少辐射剂量.
- 提高CT检查计划的效率.
主要方法:
- 在1507个追溯CT定位器上训练DNN,以预测优化的扫描范围.
- 在内部 (233) 和外部 (298) 独立队列上验证了DNN.
- 模拟有效辐射剂量使用蒙特卡洛模拟,并使用等效性和优越性测试比较扫描范围.
主要成果:
- 在DNN中,与放射科医生定义的扫描范围达成96.5%的一致性 (p < 0.001).
- 自动扫描范围在内部队列中的有效辐射剂量减少了10.0% (p = 0.002).
- 自动扫描范围在外部队列中减少了12.6%的有效辐射剂量 (p < 0.001).
结论:
- 使用DNN的全自动扫描范围划分显著减少CT冠状动脉血管学中的辐射暴露.
- 这种由人工智能驱动的方法提供了高准确性和减少放射科医生的工作负载的潜力.
- 扫描范围的自动优化是减少心脏CT剂量的有希望的策略.
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