Fetal MRI: robust self-driving brain acquisition and body movement quantification

胎儿 MRI：强大的自动驾驶大脑采集和身体运动量化

• 批准号: 10390574
• 负责人: ELFAR ADALSTEINSSON
• 金额: $ 72.94万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10390574
• 关键词: 3-Dimensional; Address; Anatomy; Archives; Automobile Driving; Behavior; Behavioral; Body part; Brain; COVID-19; COVID-19 impact; Clinical; Clinical Research; Collection; Communicable Diseases; Complex; Data; Databases; Defect; Deposition; Development; Diagnosis; Disease; Echo-Planar Imaging; Ensure; Environmental Exposure; Environmental Impact; Eye; Fetus; Gestational Age; Head; Health; Human; Image; Imaging Device; Intervention; Joints; Leg; Lesion; Life; Limb structure; Location; Magnetic Resonance Imaging; Measures; Morphologic artifacts; Motion; Movement; Nervous System Physiology; Network-based; Neural Network Simulation; Neurologic; Opioid; Outcome; Paraplegia; Pathology; Performance; Phase; Population Study; Positioning Attribute; Pregnancy; Prospective Studies; Protocols documentation; Resolution; Sampling; Scanning; Series; Site; Slice; Specialized Center; Spinal; Spinal Dysraphism; Stratification; Structure; Substance abuse problem; T2 weighted imaging; Testing; Time; Training; Triage; Update; base; brain abnormalities; brain magnetic resonance imaging; brain volume; clinically relevant; cognitive function; convolutional neural network; fetal; foot; improved; malformation; maternal obesity; motor deficit; neonatal brain; nervous system disorder; novel; postnatal; prospective; reconstruction; response; sex; success; tool; treatment center; ultrasound

PROJECT SUMMARY/ ABSTRACT Our premise is that the fetal stage of human brain development is the most dynamic, the most vulnerable and the most important for lifelong behavioral and cognitive function. As many neurological disorders have their genesis in fetal life, there is a need to accurately quantify normal and abnormal fetal brain development from both the perspective of fetal brain structure and body motion. Better imaging tools would enable us to explore how fetal neurological disorders as well as environmental exposures, such as opioids, maternal obesity, or COVID-19, impact early brain structure and body movements. Magnetic resonance imaging (MRI) T2-weighted, single-shot fast-spin-echo (e.g. HASTE) images provide a unique window into this critical phase of structural brain development, with the potential to detect subtle abnormalities. However, fetal brain MRI is challenging due to fetal motion, which leads to image artifacts, double oblique acquisitions and incomplete brain coverage. As a result, trained MR technologists must “chase the fetus” to amass the necessary images to diagnose the presence or absence of lesions, resulting in long scan times and higher RF energy deposition. Thus, fetal brain MRI is inefficient, limited to specialized centers, and diagnosis is still difficult because fetal motion results in each image being an independent slice that cannot be referenced to another slice, making confirmation of suspicious findings difficult. At the same time, fetal motion is an important measure of functional neurological integrity, informing postnatal outcomes. However, current clinical MR and ultrasound assessments of fetal motion do not fully capture the complex 3D motions of all body parts simultaneously. Better assessment of fetal neurological health requires novel tools to automatically and efficiently obtain coherent, high quality HASTE fetal brain volumes and to characterize 3D fetal whole-body motion. To address these unmet needs, we will leverage convolutional neural network (CNN) models and propose the following aims: (1) Develop a self-driving engine for efficient acquisition of high-quality HASTE fetal brain volumes and (2) Enable automated fetal whole-body motion tracking and characterization. We will deploy the proposed tools in a prospective study that compares fetuses with Chiari II malformation (spina bifida), a disorder known to have brain abnormalities and often associated with decreased leg movement, to typical fetuses with the following aim: (3) Assess performance of the self-driving HASTE engine and whole-body motion characterization in Chiari II vs typical fetuses. For Aims 1 and 2, we will include data from collaborating sites and strategies for CNN generalization to increase robustness and potential to deploy our tools to other scanners. The ability to automatically obtain high-quality coherent fetal brain volumes and characterize fetal motion will improve stratification for fetal treatments and characterization of response to fetal interventions. Success will also enable sites without fetal imaging experts to locally assess and triage fetuses with suspected abnormalities to specialized treatment centers, as well as facilitate large population-based studies to understand the impact of environmental influences on early brain development and fetal behavior.

项目摘要/摘要 我们的前提是人脑发育的胎儿阶段是最动态的，最脆弱的， 对于终身行为和认知功能，最重要的 胎儿生命中的起源，需要准确地量化正常和异常的胎儿脑发育 胎儿大脑结构和身体运动的视角。 胎儿神经局部疾病以及环境暴露如何，例如阿片类药物，母体肥胖或 COVID-19，影响早期的大脑结构和身体运动。 单杆快速旋转回声（例如急速）图像为结构的关键阶段提供了独特的窗口 大脑发育，有可能发现微妙的异常。 胎儿运动，导致图像伪像，双倾斜的采集和无限制的大脑覆盖率。 结果，训练有素的MR技术人员必须“追逐胎儿”积累图像以诊断存在 或缺乏病变，导致长时间扫描时间和更高的RF能量沉积。 无效的，仅限于专业中心，诊断仍然是difficalt vilal vilal运动，因为胎儿运动在每个图像中都会导致 成为一个独立的切片，无法提及另一片，证实了可疑发现 difficalt。 但是，产后结果。 同时捕获所有身体部位的复杂3D运动。 需要工具以自动有效地获得连贯，高质量的仓鼠大脑和量 为了表征3D胎儿全身运动。 网络（CNN）模型并提出以下目的：（1）开发自动驾驶引擎以提高效率 高质量的仓鼠大脑体积和（2）启用自动胎儿全身运动跟踪和 特征。 畸形（脊柱裂），一种已知脑异常的疾病，通常与降低有关 腿部运动，典型的胎儿，其目的：（3）评估自动驾驶急速发动机的性能 Chiari II与典型的胎儿中的全身运动表征。 CNN概括的策略和策略，以提高鲁棒性和部署的潜力 其他扫描仪的工具。 胎儿运动的特征将改善胎儿治疗的分层和对胎儿反应的表征 干预措施。 怀疑对专门治疗中心的异常情况，并促进了大型基于人群的 了解环境影响对早期大脑发育和胎儿行为的影响。