Intra-voxel incoherent motion (IVIM) analysis of fetal lungs
Diffusion-Weighted MRI (DWI) data shows potential in providing quantitative
imaging bio-markers that reflect, indirectly, diffusion and pseudo-diffusion
for non-invasive fetal lung maturation assessment. However, long acquisition
times, due to the large number of different ``b-value'' images required for
IVIM analysis, precluded clinical feasibility. We introduce SUPER-IVIM-DC a
deep-neural-networks (DNN) approach which couples supervised loss with a
data-consistency term to enable IVIM analysis of DWI data acquired with a
limited number of b-values. We demonstrated the added-value of SUPER-IVIM-DC
over both classical and recent DNN approaches for IVIM analysis through
numerical simulations, healthy volunteer study, and IVIM analysis of fetal lung
maturation from fetal DWI data. Our numerical simulations and healthy volunteer
study show that SUPER-IVIM-DC estimates of the IVIM model parameters from
limited DWI data had lower normalized root mean-squared error compared to
previous DNN-based approaches. Further, SUPER-IVIM-DC estimates of the
pseudo-diffusion fraction parameter from limited DWI data of fetal lungs
correlate better with gestational age compared to both to classical and
DNN-based approaches (0.555 vs. 0.463 and 0.310). SUPER-IVIM-DC has the
potential to reduce the long acquisition times associated with IVIM analysis of
DWI data and to provide clinically feasible bio-markers for non-invasive fetal
lung maturity assessment.

本文介绍了一种使用 SUPER-IVIM-DC 深度神经网络技术来提高 DWI 数据捕获 b 值数量限制的 IVIM 分析方法，透过模拟、慢性志愿者和胎儿数据的实验，发现该方法相较于以往的 DNN 方法和经典方法具备更低的正常化均方根误差及更高的胎龄相关性，可为非侵入性胎儿肺成熟性评估提供临床可行性生物标记，并可减少 DWI 数据分析的长时间获取。

基于 IVIM 模型的胎肺成熟度有监督学习评估方法：数据一致性约束下的有限 DWI 数据

SUPER-IVIM-DC: Intra-voxel incoherent motion based Fetal lung maturity assessment from limited DWI data using supervised learning coupled with data-consistency