IVIM-Morph：基于扩散加权MRI数据的运动补偿定量体素内不相干运动（IVIM）分析用于胎儿肺成熟度功能评估
IVIM-Morph: Motion-compensated quantitative Intra-voxel Incoherent Motion (IVIM) analysis for functional fetal lung maturity assessment from diffusion-weighted MRI data
AI总结：
本文提出自监督深度模型IVIM-Morph，通过配准与IVIM拟合子网络联合估计运动与模型参数，提升胎儿肺DWI定量分析对胎龄的相关性，用于无创评估胎儿肺成熟度。
摘要：
对扩散加权磁共振成像（DWI）数据中的伪扩散进行定量分析，显示出评估胎儿肺成熟度和生成有价值的影像生物标志物的潜力。然而，DWI数据的临床实用性受到采集过程中不可避免的胎儿运动的阻碍。我们提出IVIM-Morph，这是一种自监督深度神经网络模型，利用体素内不相干运动（IVIM）模型对DWI数据进行运动校正定量分析。IVIM-Morph结合了两个子网络：一个配准子网络和一个IVIM模型拟合子网络，能够同时估计IVIM模型参数和运动。为了促进物理上合理的图像配准，我们引入了一种生物物理信息驱动的损失函数，有效平衡配准质量和模型拟合质量。我们使用39名受试者的胎儿DWI数据，通过建立预测的肺部IVIM模型参数与胎龄（GA）之间的相关性，验证了IVIM-Morph的有效性。在对小管期胎儿肺DWI数据进行在体定量分析时，IVIM-Morph与胎龄（GA）的相关性显著改善。IVIM-Morph显示出利用DWI数据开发有价值的生物标志物以进行胎儿肺成熟度无创评估的潜力。此外，其适应性为其他需要运动补偿以进行定量DWI分析的临床情境开辟了潜在应用。IVIM-Morph代码可在以下地址获取：https://github.com/TechnionComputationalMRILab/qDWI-Morph。
英文摘要：
Quantitative analysis of pseudo-diffusion in diffusion-weighted magnetic resonance imaging (DWI) data shows potential for assessing fetal lung maturation and generating valuable imaging biomarkers. Yet, the clinical utility of DWI data is hindered by unavoidable fetal motion during acquisition. We present IVIM-morph, a self-supervised deep neural network model for motion-corrected quantitative analysis of DWI data using the Intra-voxel Incoherent Motion (IVIM) model. IVIM-morph combines two sub-networks, a registration sub-network, and an IVIM model fitting sub-network, enabling simultaneous estimation of IVIM model parameters and motion. To promote physically plausible image registration, we introduce a biophysically informed loss function that effectively balances registration and model-fitting quality. We validated the efficacy of IVIM-morph by establishing a correlation between the predicted IVIM model parameters of the lung and gestational age (GA) using fetal DWI data of 39 subjects. IVIM-morph exhibited a notably improved correlation with gestational age (GA) when performing in-vivo quantitative analysis of fetal lung DWI data during the canalicular phase. IVIM-morph shows potential in developing valuable biomarkers for non-invasive assessment of fetal lung maturity with DWI data. Moreover, its adaptability opens the door to potential applications in other clinical contexts where motion compensation is essential for quantitative DWI analysis. The IVIM-morph code is readily available at: https://github.com/TechnionComputationalMRILab/qDWI-Morph.
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