Kertes Noga, Zaffrani-Reznikov Yael, Afacan Onur, Kurugol Sila, Warfield Simon K, Freiman Moti
Faculty of Biomedical Engineering, Technion, Haifa, Israel.
Boston Children's Hospital, Boston, MA, USA.
Med Image Anal. 2025 Apr;101:103445. doi: 10.1016/j.media.2024.103445. Epub 2024 Dec 31.
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. Our approach was compared against six baseline methods: (1) no motion compensation, (2) affine registration of all DWI images to the initial image, (3) deformable registration of all DWI images to the initial image, (4) deformable registration of each DWI image to its preceding image in the sequence, (5) iterative deformable motion compensation combined with IVIM model parameter estimation, and (6) self-supervised deep-learning-based deformable registration. 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. Specifically, over 2 test groups of cases, it achieved an R of 0.44 and 0.52, outperforming the values of 0.27 and 0.25, 0.25 and 0.00, 0.00 and 0.00, 0.38 and 0.00, and 0.07 and 0.14 obtained by other methods. 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.
扩散加权磁共振成像（DWI）数据中伪扩散的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，采集过程中不可避免的胎儿运动阻碍了DWI数据的临床应用。我们提出了IVIM-morph，这是一种用于使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的自监督深度神经网络模型。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。为了促进符合物理原理的图像配准，我们引入了一个基于生物物理学的损失函数，该函数有效地平衡了配准和模型拟合质量。我们使用39名受试者的胎儿DWI数据，通过建立肺部预测的IVIM模型参数与胎龄（GA）之间的相关性，验证了IVIM-morph的有效性。我们的方法与六种基线方法进行了比较：（1）无运动补偿，（2）将所有DWI图像仿射配准到初始图像，（3）将所有DWI图像变形配准到初始图像，（4）将每个DWI图像变形配准到序列中其前一图像，（5）迭代变形运动补偿结合IVIM模型参数估计，以及（6）基于自监督深度学习的变形配准。在细支气管期对胎儿肺DWI数据进行体内定量分析时，IVIM-morph与胎龄（GA）的相关性显著提高。具体而言，在2个测试病例组中，它的R值分别为0.44和0.52，优于其他方法获得的0.27和0.25、0.25和0.00、0.00和0.00、0.38和0.00以及0.07和0.14。IVIM-morph在利用DWI数据开发用于无创评估胎儿肺成熟度的有价值生物标志物方面显示出潜力。此外，它的适应性为在其他临床环境中的潜在应用打开了大门，在这些环境中，运动补偿对于定量DWI分析至关重要。IVIM-morph代码可在以下网址获取：https://github.com/TechnionComputationalMRILab/qDWI-Morph。
