Next-generation in-vivo fetal neuroimaging

下一代体内胎儿神经影像

基本信息

批准号：
10280126
负责人：
ALI GHOLIPOUR-BABOLI
金额：
$ 58.86万
依托单位：
BOSTON CHILDREN'S HOSPITAL
依托单位国家：
美国
项目类别：
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/10280126
关键词：
3-Dimensional Acceleration Affect Algorithms Anatomy Birth Brain Cell Proliferation Cell physiology Classification Clinical Trials Cognitive Complex Congenital Disorders Data Data Set Development Fetal Development Fetus Gestational Age Head Health Care Costs High Prevalence Human Hypoxia Image Imaging Techniques 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 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 density disability effective therapy fetal fetus at risk image processing image reconstruction imaging study 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

项目摘要

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该项目的总体目标是显着改善胎儿磁共振成像 (MRI) 推进早期人类大脑发育和神经发育障碍的研究，其负担不幸的是，由于其终生影响和高患病率，该比例很高。胎儿 MRI 已成为技术研究产前大脑发育的首选。然而，胎儿运动使得 MRI 切片采集成为可能充其量是不可靠的，因为在对规定的切片进行成像时胎儿经常移动。未代偿的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。重复扫描并不能确保解剖结构的完整 3D 覆盖，但会增加总数采集时间。这反过来又大大降低了胎儿 MRI 研究胎儿的成功率和可靠性。胎儿大脑区室的发育对胎儿发育过程中的损伤选择性敏感胎儿发育。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前瞻性地将切片实时导航到每个新位置。这的影响方法将显着提高胎儿 MRI 体内检查的成功率和空间分辨率研究发育中的脑室，同时减少扫描时间，有效地使胎儿 MRI 对母亲来说减轻了负担，更准确且更具成本效益。通过消除手动重新通过调整切片堆栈位置，扫描之间经过的时间实际上是连续的。我们的所提出的技术还将减少胎儿 MRI 对操作员的依赖性，从而在各个方面更具可重复性站点，这对于进行多中心研究和临床试验至关重要。胎儿前瞻性导航用于补偿运动的 MRI 切片需要开发新颖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；并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；将把这些模型转化为一个集成系统，以便前瞻性地导航胎儿 MRI 切片；并将在不同胎龄扫描的胎儿上验证该系统。到为了评估所提出技术的实用性和影响，我们将测量胎儿的底板体积。四个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MRI切片中胎儿大脑的实时识别； 3）开发实时胎头系统切片的运动跟踪和转向； 4) 使用以下方法测量发育中的胎儿大脑中的亚板体积核磁共振成像。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，有效消除发育领域取得进展的关键障碍神经病学和神经科学。