Fetal whole-heart 4D imaging using motion-corrected multi-planar real-time MRI
Joshua FP van Amerom, David FA Lloyd, Maria Deprez, Anthony N Price, Shaihan J Malik, Kuberan Pushparajah, Milou PM van Poppel, Mary A Rutherford, Reza Razavi, Joseph V Hajnal
Purpose: To develop a MRI acquisition and reconstruction framework for volumetric cine visualisation of the fetal heart and great vessels in the presence of maternal and fetal motion.
Methods: Four-dimensional depiction was achieved using a highly-accelerated multi-planar real-time balanced steady state free precession acquisition combined with retrospective image-domain techniques for motion correction, cardiac synchronisation and outlier rejection. The framework was evaluated and optimised using a numerical phantom, and evaluated in a study of 20 mid- to late-gestational age human fetal subjects. Reconstructed cine volumes were evaluated by experienced cardiologists and compared with matched ultrasound. A preliminary assessment of flow-sensitive reconstruction using the velocity information encoded in the phase of dynamic images is included.
Results: Reconstructed cine volumes could be visualised in any 2D plane without the need for highly-specific scan plane prescription prior to acquisition or for maternal breath hold to minimise motion. Reconstruction was fully automated aside from user-specified masks of the fetal heart and chest. The framework proved robust when applied to fetal data and simulations confirmed that spatial and temporal features could be reliably recovered. Expert evaluation suggested the reconstructed volumes can be used for comprehensive assessment of the fetal heart, either as an adjunct to ultrasound or in combination with other MRI techniques.
Conclusion: The proposed methods show promise as a framework for motion-compensated 4D assessment of the fetal heart and great vessels.
Measuring the time-scale-dependent information flow between maternal and fetal heartbeats during the third trimester
CNRS, ENS de Lyon, LPENSL（法国国家科学研究中心，里昂高等师范学院）；Department of Obstetrics and Gynecology, TUM University Hospital of Technical University of MunichTUM School of Medicine, Technical University of Munich（慕尼黑工业大学附属医院妇产科，慕尼黑工业大学医学院）；Technical University of Munich（慕尼黑工业大学；高加综合研究院）；Institute for Advanced Study, Garching（布宜诺斯艾利斯大学医学院细胞生物学与神经科学研究所）；Instituto de Biología Celular y Neurociencia ”Prof. Eduardo De Robertis”, Facultad de MedicinaUniversidad de Buenos Aires（华盛顿大学妇产科）；Dept. of Obstetrics and Gynecology, University of Washington（华盛顿大学人类发展与残疾研究所）；Institute on Human Development and Disability, University of Washington
Distance to healthy cardiovascular dynamics from fetal heart rate scale-dependent features in pregnant sheep model of human labor predicts cardiovascular decompensation
Rapid head-pose detection for automated slice prescription of fetal-brain MRI
Department of Radiology, Massachusetts General Hospital（麻省总医院放射科）；Radiology, Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院放射科）
Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration: application to gestational age prediction
Universidad Politécnica de Madrid（马德里理工大学）；Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)（生物医学研究网络中心（生物工程、生物材料与纳米医学））；King’s College London（伦敦国王学院）
SUPER-IVIM-DC: Intra-voxel incoherent motion based Fetal lung maturity assessment from limited DWI data using supervised learning coupled with data-consistency
胎儿肺的体素内非相干运动（IVIM）分析弥散加权MRI（DWI）数据显示出提供间接反映弥散和伪弥散的定量成像生物标记物用于非侵入性胎儿肺成熟评估的潜力。然而，由于IVIM分析所需的大量不同的"b值“图像，长的采集时间排除了临床可行性。我们引入了SUPER-IVIM-DC，这是一种深度神经网络（DNN）方法，它将监督损失与数据一致性项相结合，以实现对使用有限数量的b值获取的DWI数据的IVIM分析。我们通过数值模拟、健康志愿者研究和来自胎儿DWI数据的胎儿肺成熟的IVIM分析，证明了SUPER-IVIM-DC相对于经典和最近DNN方法用于IVIM分析的附加值。我们的数值模拟和健康志愿者研究表明，与以前基于DNN的方法相比，来自有限DWI数据的IVIM模型参数的SUPER-IVIM-DC估计具有较低的归一化均方根误差。此外，与经典和基于DNN的方法相比，来自胎儿肺的有限DWI数据的伪扩散分数参数的SUPER-IVIM-DC估计与胎龄相关性更好（0.555 vs. 0.463和0.310）。SUPER-IVIM-DC有可能减少与DWI数据IVIM分析相关的长采集时间，并为无创性胎肺成熟度评估提供临床可行的生物标志物。
