论文标题
QDWI-MORPH：运动补偿的定量扩散加权MRI分析用于胎儿肺成熟度评估
qDWI-Morph: Motion-compensated quantitative Diffusion-Weighted MRI analysis for fetal lung maturity assessment
论文作者
论文摘要
胎儿肺扩散加权MRI（DWI）数据的定量分析显示，提供了提供的定量成像生物标志物，从而间接反映了胎儿肺的成熟。然而，收购过程中的胎儿运动妨碍了对获得的DWI数据的定量分析，从而阻碍了可靠的临床利用。我们介绍了QDWI-Morph，这是一种无监督的深神经网络结构，用于运动补偿定量DWI（QDWI）分析。我们的方法将注册子网络与定量DWI模型拟合子网络融合。我们同时估计QDWI参数和运动模型，通过最大程度地降低了整合注册损失和模型拟合质量损失的生物形态信息损耗函数。我们证明了QDWI-MORPH的附加值：1）基线QDWI分析而没有运动补偿，2）仅包含注册损失的基线深度学习模型。 QDWI-morph通过对胎儿肺DWI数据的体内QDWI分析（R平方= 0.32 vs. 0.13，0.28）实现了与胎龄的相关性。我们的QDWI-MORPH有可能对DWI数据进行运动补偿的定量分析，并为非侵入性胎儿肺成熟度评估提供临床上可行的生物标志物。我们的代码可在以下网址提供：https：//github.com/technioncomputationalmrilab/qdwi-morph。
Quantitative analysis of fetal lung Diffusion-Weighted MRI (DWI) data shows potential in providing quantitative imaging biomarkers that indirectly reflect fetal lung maturation. However, fetal motion during the acquisition hampered quantitative analysis of the acquired DWI data and, consequently, reliable clinical utilization. We introduce qDWI-morph, an unsupervised deep-neural-network architecture for motion compensated quantitative DWI (qDWI) analysis. Our approach couples a registration sub-network with a quantitative DWI model fitting sub-network. We simultaneously estimate the qDWI parameters and the motion model by minimizing a bio-physically-informed loss function integrating a registration loss and a model fitting quality loss. We demonstrated the added-value of qDWI-morph over: 1) a baseline qDWI analysis without motion compensation and 2) a baseline deep-learning model incorporating registration loss solely. The qDWI-morph achieved a substantially improved correlation with the gestational age through in-vivo qDWI analysis of fetal lung DWI data (R-squared=0.32 vs. 0.13, 0.28). Our qDWI-morph has the potential to enable motion-compensated quantitative analysis of DWI data and to provide clinically feasible bio-markers for non-invasive fetal lung maturity assessment. Our code is available at: https://github.com/TechnionComputationalMRILab/qDWI-Morph.
