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.

翻译：弥散加权磁共振成像（DWI）数据中伪扩散的定量分析在评估胎儿肺成熟度及生成有价值的影像生物标志物方面展现出潜力。然而，DWI数据的临床实用性受限于采集过程中不可避免的胎儿运动。我们提出IVIM-morph——一种基于自监督深度神经网络模型的运动校正DWI数据定量分析方法，该模型采用体素内不相干运动（IVIM）模型。IVIM-morph融合了两个子网络：配准子网络与IVIM模型拟合子网络，可同时估计IVIM模型参数与运动信息。为促进符合物理规律的图像配准，我们引入一种生物物理信息驱动的损失函数，有效平衡配准质量与模型拟合质量。我们通过建立39例胎儿DWI数据中预测的肺部IVIM模型参数与孕周（GA）之间的相关性，验证了IVIM-morph的有效性。在小管期胎儿肺DWI数据的活体定量分析中，IVIM-morph显示出与孕周（GA）显著提升的相关性。IVIM-morph为利用DWI数据无创评估胎儿肺成熟度开发有价值的生物标志物展现出潜力。此外，其可扩展性为运动补偿对定量DWI分析至关重要的其他临床应用敞开了大门。IVIM-morph代码已开源，获取地址：https://github.com/TechnionComputationalMRILab/qDWI-Morph。