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.
ArXiv
|February 5, 2024
概括
一个新的深度学习模型,IVIM-morph,在扩散加权成像 (DWI) 中纠正胎儿运动,以准确评估肺成熟. 这种方法增强了成像生物标志物与妊娠年龄之间的相关性,为非侵入性胎儿发育监测铺平了道路.
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往往无法保持定量DWI分析的完整性.
研究的目的:
- 引入IVIM-morph,一个自我监督的深度神经网络,用于对胎儿肺部DWI数据进行运动校正的定量分析.
- 为了能够同时估计声内不连贯运动 (IVIM) 模型参数和胎儿运动.
- 开发一种可靠的方法,用于生成胎儿肺成熟度的可靠成像生物标志物.
主要方法:
- 开发了IVIM-morph,这是一个具有集成注册和IVIM模型合适子网络的深度神经网络.
- 引入了生物物理知情损失函数,以平衡图像注册和IVIM模型拟合.
- 使用39名受试者的胎儿DWI数据验证了该模型,与六种基线方法进行了比较.
主要成果:
- 与所有基线方法相比,IVIM-morph证明了预测的IVIM参数和妊娠年龄 (GA) 之间的相关性明显改善.
- 在胎儿肺状阶段,在试验组中获得更高的相关系数 (例如0.44和0.52).
- 超越性能的方法包括无补偿,亲缘/可变形注册和其他深度学习方法.
结论:
- 对于定量DWI分析,IVIM-morph有效地纠正胎儿运动,从而能够准确评估胎儿肺部成熟度.
- 该模型显示了开发用于胎儿发育的非侵入性生物标志物的潜力.
- IVIM-morph的适应性表明在各种临床环境中对运动敏感的定量DWI分析中具有更广泛的应用.
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