以深度学习为基础,用99m的Tc-diethylenetriamine pentaacetic acid脏扫描测量分裂膜过率
Sejin Ha1,2, Byung Soo Park1, Sangwon Han3
1Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
EJNMMI physics
|July 17, 2024
概括
一个深度学习模型准确地在Tc-DTPA脏扫描上生成感兴趣的区域,以测量膜过率 (GFR). 这种自动化方法与手工测量结果有很好的一致性,有可能改善临床GFR评估.
科学领域:
- 核医学是一种核医学.
- 放射化学 放射化学是指辐射化学.
- 医疗保健中的人工智能
背景情况:
- 淋巴膜过率 (GFR) 是功能的一个关键指标.
- 精确的GFR测量依赖于在部扫描上精确划分感兴趣的区域 (ROI).
- 手动的ROI划分可能会耗时,并且会受到观察者之间的变化.
研究的目的:
- 开发和验证一个深度学习 (DL) 模型,用于在Tc-diethylenetriamine pentaacetic acid (DTPA) 脏扫描上自动生成ROI.
- 与手动方法相比,评估自动化ROI对GFR测量的准确性的影响.
主要方法:
- 一个二维的U-Net卷积神经网络被训练使用手动绘制的ROI作为基础真相.
- 该DL模型在12822名患者的24364个Tc-DTPA脏扫描上产生了自动化ROI.
- 从DL和手动ROI获得的GFR值之间的一致性使用林的一致性相关系数 (CCC),线性回归和布兰德-阿尔特曼分析进行了评估.
主要成果:
- DL模型在GFR测量中与手动ROI有很好的一致性 (CCC>0.969对所有脏比较).
- 布兰德-阿尔特曼分析显示偏差最小,平均差异为-0.2至1.2毫升/分钟/1.73米2.
- 超过81%的扫描显示DL和手动方法之间的绝对GFR差异小于5mL/min/1.73m2.
结论:
- 开发的DL模型有效地在99mTc-DTPA脏扫描上自动生成ROI.
- 这种自动化方法在GFR测量中显示出高精度和最小偏差.
- 在临床实践中,DL模型有可能减少手工工作量并提高GFR评估的精度.
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