IVIM-Morph:以运动补偿的定量内声素不一致运动 (IVIM) 分析,用于从扩散权重的MRI数据中评估功能性胎儿肺成熟度
Noga Kertes1, Yael Zaffrani-Reznikov1, Onur Afacan2
1Faculty of Biomedical Engineering, Technion, Haifa, Israel.
Medical image analysis
|January 5, 2025
概括
胎儿运动使扩散权重成像 (DWI) 分析复杂化肺成熟. 一个新的深度学习模型IVIM-morph纠正了运动以获得准确的定量分析,改善了与妊娠年龄的相关性.
科学领域:
- 医疗成像医学成像
- 生物物理学的生物物理.
- 机器学习 机器学习
背景情况:
- 扩散权重成像 (DWI) 显示出评估胎儿肺成熟的前景.
- 在MRI采集过程中胎儿运动显著阻碍了DWI数据的定量分析.
- 现有的运动校正方法在准确性和适用性方面存在局限性.
研究的目的:
- 开发和验证一个自我监督的深度神经网络,IVIM-morph,用于对胎儿肺部DWI数据的运动校正定量分析.
- 为了能够准确地估计内声体不连贯运动 (IVIM) 模型参数,尽管胎儿运动.
- 建立IVIM参数和胎儿肺成熟度指标,如妊娠年龄之间的相关性.
主要方法:
- 开发了IVIM-morph,这是一个结合注册和IVIM模型拟合子网络的深度神经网络.
- 引入了生物物理知情损失函数,以平衡注册和模型拟合的准确性.
- 验证了39名受试者的胎儿DWI数据模型,并将其与六种基线方法进行比较.
主要成果:
- 与基线方法相比,IVIM-morph与妊娠年龄的相关性明显改善 (Rf2为0.44和0.52).
- 该模型成功地对胎儿肺部DWI数据进行了运动校正的定量分析.
- 在预测怀孕年龄的过程中取得了卓越的表现 肺部发育的状阶段.
结论:
- 在胎儿肺部DWI分析中,IVIM-morph提供了一个强大的运动补偿解决方案.
- 该模型有可能产生有价值的非侵入性生物标志物,用于评估胎儿肺部成熟度.
- 它的适应性表明在各种临床场景的定量DWI分析中具有更广泛的应用.
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