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: this https URL

IVIM-morph 是一种自监督深度神经网络模型，用于运动校正的扩散加权磁共振成像数据的定量分析，其将 IVIM 模型参数估计和运动同时进行，展示了在胎儿肺部定量分析中改善胎龄相关性，同时为非侵入性胎儿肺成熟性评估和动态扩散加权成像分析提供了潜在应用。

IVIM-Morph: 动态补偿的定量矢量内相不相干运动（IVIM）分析用于从扩散加权磁共振成像数据评估功能性胎儿肺成熟度

IVIM-Morph: Motion-compensated quantitative Intra-voxel Incoherent  Motion (IVIM) analysis for functional fetal lung maturity assessment from  diffusion-weighted MRI data

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。