Next-generation in-vivo fetal neuroimaging

下一代体内胎儿神经影像

• 批准号: 10578814
• 负责人: ALI GHOLIPOUR-BABOLI
• 金额: $ 55.62万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10578814
• 关键词: 3-Dimensional; Acceleration; Affect; Algorithms; Anatomy; Birth; Brain; Cell Proliferation; Cell physiology; Classification; Clinical Trials; Cognitive; Compensation; Complex; Congenital Disorders; Data; Data Set; Development; Fetal Development; Fetus; Gestational Age; Head; Health Care Costs; High Prevalence; Human; Human Development; Hypoxia; Image; Imaging technology; Injury; Investigation; Life; Live Birth; Magnetic Resonance Imaging; Manuals; Measures; Mental Health; Modeling; Morphologic artifacts; Mothers; Motion; Multicenter Studies; Neurodevelopmental Disorder; Neurology; Neurons; Neurosciences; Noise; Outcome; Patient-Focused Outcomes; Patients; Play; Positioning Attribute; Process; Property; Real-Time Systems; Relaxation; Reproducibility; Research; Resolution; Role; Rotation; Sampling; Scanning; Second Pregnancy Trimester; Signal Transduction; Site; Slice; Speed; System; Systems Integration; Techniques; Technology; Testing; Therapeutic Intervention; Time; Tissues; Training; Translating; Uterus; absorption; brain tissue; cognitive disability; congenital heart disorder; convolutional neural network; cost effective; deep learning; deep learning model; density; disability; effective therapy; fetal; fetus at risk; image processing; image reconstruction; improved; in vivo; in vivo imaging; innovation; large datasets; migration; myelination; nervous system disorder; neurodevelopment; neuroimaging; new technology; next generation; novel; prenatal; prospective; real-time images; reconstruction; recurrent neural network; success; virtual

Next-Generation In-Vivo Fetal Neuroimaging The overall objective of this project is to dramatically improve fetal magnetic resonance imaging (MRI) to advance research in early human brain development and neurodevelopmental disorders, the burden of which is, unfortunately, high because of their life-long impact and high prevalence. Fetal MRI has been the technique of choice in studying prenatal brain development. Fetal motion, however, makes MRI slice acquisition unreliable at best, as the fetus frequently moves while the prescribed slices are imaged. Uncompensated fetal motion disrupts 3D coverage of the anatomy and reduces the spatial resolution of slice-to-volume reconstructions. Repeating the scans does not ensure full 3D coverage of the anatomy, but increases total acquisition time. This, in turn, dramatically reduces the success rate and reliability of fetal MRI in studying the development of transient fetal brain compartments that are selectively sensitive to injury over the course of fetal development. To mitigate these issues and improve fetal MRI, we propose to automatically measure fetal brain position and prospectively navigate slices to each new position in real-time. The impact of this approach will be to dramatically increase the success rate and spatial resolution of fetal MRI for the in-vivo investigation of developing brain compartments, while, in parallel, reducing scan time, effectively making fetal MRI less burdensome for the mother, more accurate, and cost effective. By eliminating the manual re- adjustment of stack-of-slice positions, the time that elapses between scans will be virtually continuous. Our proposed technique will also make fetal MRI less operator-dependent and thus, more reproducible across sites, which is essential to conducting multi-center studies and clinical trials. Prospective navigation of fetal MRI slices to compensate for motion requires the development of novel, real-time image processing algorithms to recognize the fetal brain and its position and orientation; to track fetal motion to steer slices; and to detect and re-acquire motion corrupted slices. In this project, we will develop innovative deep learning models to process fetal MRI slices in real-time; will translate those models into an integrated system to prospectively navigate fetal MRI slices; and will validate the system on fetuses scanned at various gestational ages. To assess the utility and impact of the proposed technology, we will measure subplate volume in fetuses. The four specific aims of this study are to 1) assess fetal MRI via variable density image acquisition and reconstruction; 2) achieve real-time recognition of the fetal brain in MRI slices; 3) develop a system of real-time fetal head motion tracking and steering of slices; and 4) measure the subplate volume in the developing fetal brain using MRI. These aims will collectively translate and validate new imaging and image processing techniques to advance fetal MRI, and effectively eliminate a critical barrier to making progress in the fields of developmental neurology and neuroscience.

下一代体内胎儿神经成像 该项目的总体目标是显著改善胎儿磁共振成像（MRI）， 在早期人类大脑发育和神经发育障碍的研究，其中的负担 不幸的是，由于其终身影响和高流行率，胎儿核磁共振成像一直是 是研究胎儿期大脑发育的首选然而，胎儿运动使得MRI切片采集 最好也不可靠，因为在规定的切片成像时胎儿经常移动。失代偿胎儿 运动会破坏解剖结构的3D覆盖，并降低切片到体积的空间分辨率 重建。重复扫描不能确保解剖结构的完整3D覆盖，但会增加总的 收购时间。这反过来又大大降低了胎儿MRI在研究胎儿发育中的成功率和可靠性。 短暂的胎儿脑室的发展，在整个过程中选择性地对损伤敏感， 胎儿发育为了缓解这些问题并改善胎儿MRI，我们建议自动测量 胎儿大脑位置并实时前瞻性地将切片导航到每个新位置。这样做的影响 方法将是显着提高成功率和空间分辨率的胎儿磁共振成像的体内 研究发育中的脑室，同时，平行，减少扫描时间，有效地使胎儿 MRI对母亲的负担更小，更准确，更具成本效益。通过消除手动重新- 通过调整切片堆叠位置，扫描之间经过的时间将实际上是连续的。我们 所提出的技术还将使胎儿MRI更少地依赖于操作者，因此，在整个胎儿期内更可重复。 这对于开展多中心研究和临床试验至关重要。胎儿前瞻性导航 MRI切片补偿运动需要开发新的实时图像处理算法 识别胎儿大脑及其位置和方向;跟踪胎儿运动以引导切片;以及检测 并重新获取运动损坏的切片。在这个项目中，我们将开发创新的深度学习模型， 实时处理胎儿MRI切片;将这些模型转化为一个集成系统， 导航胎儿MRI切片;并将在不同胎龄扫描的胎儿上验证该系统。到 为了评估所提出的技术的效用和影响，我们将测量胎儿的底板体积。四 本研究的具体目的是：1）通过可变密度图像采集和重建评估胎儿MRI; 2)实现了对MRI切片中胎儿大脑的实时识别; 3）开发了一套实时胎儿头部识别系统 切片的运动跟踪和转向;以及4）使用 核磁共振这些目标将共同转化和验证新的成像和图像处理技术， 推进胎儿MRI，并有效地消除了在发育领域取得进展的关键障碍 神经病学和神经科学。