SUPER-IVIM-DC:基于胎儿肺成熟度的基于胚胎肺成熟度的静脉不协调运动,利用有限的DWI数据,利用有监督的学习和数据一致性,对有限的DWI数据进行评估 (SUPER-IVIM-DC: Intra-voxel incoherent motion based Fetal lung maturity assessment from limited DWI data using supervised learning coupled with data-consistency)

Analysis · Learning · 估计/估计量 · 可约的 · DNN ·

2022 年 6 月 8 日

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

翻译：SUPER-IVIM-DC:基于胎儿肺成熟度的基于胚胎肺成熟度的静脉不协调运动,利用有限的DWI数据,利用有监督的学习和数据一致性,对有限的DWI数据进行评估

Noam Korngut,Elad Rotman,Onur Afacan,Sila Kurugol,Yael Zaffrani-Reznikov,Shira Nemirovsky-Rotman,Simon Warfield,Moti Freiman

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. % add results 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 (0.242 vs. -0.079 and 0.239). 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.

翻译：然而,由于IVIM分析需要大量不同的“b-价值”图像分析,长期购买时间很长,因此无法进行临床可行性。我们引入了SUPER-IVIM-DC的深神经内核网络(DNN)方法,即夫妇用数据一致性术语对损失进行监督,以便能够对以有限b值数量获得的DWI数据进行IVIM分析,以便通过间接、传播和假扩散为非侵入性胚胎肺部成熟评估提供定量成象生物标志,间接、传播和假扩散,用于非侵入性胚胎肺突变评估;然而,由于IVIM分析需要大量不同的“b-价值”图像,因此获取时间很长,无法进行临床可行性。我们引入了SUPER-IVIM-C的深神经内核网络(DNN),夫妇以数据一致性术语对DWIIM模型参数的SUPER-IVIM估计数进行了IV-从有限的DWIWI、FIM-NFIM的更常值分析,以及FIM-M的S-MLA、FIM-DFM的更替、比值、更低的SIMFIM-DIM-DIM-DFL的更低、比、比、比、比、比、比、更低、更低的FIM-DFIM-DFIM-DFL的比、比、更、更、更、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比、比