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:
this https URL

使用 qDWI-morph，我们成功地开发了一种无监督深度神经网络体系结构来实现运动补偿的定量 DWI 分析，从而提供了一种可以用于无创胎肺成熟度评估的临床可行的生物标志物。