使用深度学习在肝脏mpMRI中自动量化T1和T2放松时间:一个序列适应性方法
Lukas Zbinden1,2, Samuel Erb2, Damiano Catucci2,3
1ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
European radiology experimental
|June 14, 2025
概括
深度学习算法准确量化肝脏多参数MRI (mpMRI) T1和T2放松时间,为改善肝脏评估提供不同患者组的可靠结果.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 量化MRI分析 分析
背景情况:
- 肝脏多参数MRI (mpMRI) 提供了有价值的定量数据.
- 准确的细分和量化肝膜对临床评估至关重要.
- 肝脏mpMRI的手动分析可能耗时,并且受观察者之间的变化影响.
研究的目的:
- 评估深度学习 (DL) 算法用于序列适应性肝脏mpMRI评估.
- 验证DL算法在量化不同人群的总和分段T1和T2放松时间方面的性能.
- 评估自动肝脏mpMRI分析的可靠性和通用性.
主要方法:
- 一个神经网络被训练在非对比T1加权MRI上进行肝脏细分.
- DL算法得到了优化,并在120次肝脏mpMRI检查中进行了内部测试.
- 对65名肝纤维化患者和25名健康志愿者进行了外部验证,将DL测量与手动量化进行了比较.
主要成果:
- 在细分 (ICC = 0.95) 和全肝评估 (ICC = 0.97) 的DL和手动测量之间观察到很好的一致性.
- 自动和手动测量之间的差异中位数是最小的:非对比T1的1.8ms,对比增强T1的2.0ms和T2的0.3ms.
- 该算法在各种患者群体和肝脏部分中显示出高可靠性.
结论:
- 使用序列适应DL算法的肝脏mpMRI自动定量是非常有效和可靠的.
- DL算法为总和分段T1和T2放松时间图提供了卓越的准确性.
- 这种可扩展,序列适应的方法可以在没有序列特定培训的情况下提高肝病评估中的临床决策.
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