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影像学 该项目的总体目的是显着改善胎儿磁共振成像（MRI） 对早期人脑发育和神经发育障碍的提前研究，其负担 不幸的是，由于其终身影响和高度流行，因此很高。胎儿MRI一直是技术 研究产前脑发育的选择。但是，胎儿运动使MRI切片获取 充其量是不可靠的，因为胎儿在成像时经常移动。未补偿的胎儿 运动破坏了解剖学的3D覆盖范围，并减少了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全3D覆盖范围，但会增加总数 获取时间。反过来，这大大降低了胎儿MRI的成功率和可靠性 在整个过程中选择性敏感的瞬态胎儿脑室的发展 胎儿发育。为了减轻这些问题并改善胎儿MRI，我们建议自动测量 胎儿大脑位置并前瞻性地导航切片到每个新位置。这个影响 方法将大大提高胎儿MRI的成功率和空间分辨率 研究开发脑部室的研究，而同时还会减少扫描时间，有效地制作胎儿 MRI对母亲的负担减轻了，更准确且具有成本效益。通过消除手册的重新 调整堆叠式位置的位置，扫描之间的时间几乎是连续的。我们的 拟议的技术还将使胎儿MRI依赖性依赖性较少，因此在整个方面更可重复 站点，这对于进行多中心研究和临床试验至关重要。胎儿的预期导航 MRI切片以补偿运动需要开发新颖的实时图像处理算法 认识胎儿大脑及其位置和取向；跟踪胎儿运动以转向切片；并检测 并重新接触动作破坏了切片。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片；并将验证系统在各种胎龄扫描的胎儿。到 评估所提出的技术的效用和影响，我们将测量胎儿中的子板量。四个 这项研究的具体目的是1）通过可变密度的图像采集和重建来评估胎儿MRI； 2）在MRI切片中实现对胎儿大脑的实时识别； 3）开发一个实时胎儿头系统 切片的运动跟踪和转向； 4）使用使用 MRI。这些目标将集体翻译和验证新的成像和图像处理技术 提前胎儿MRI，并有效消除在发展领域取得进展的关键障碍 神经学和神经科学。