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

项目摘要/摘要 我们的前提是人类大脑发育的胎儿阶段是最有活力、最脆弱和 对终身行为和认知功能最重要的。因为许多神经性疾病都有自己的 在胎儿生命的起源中，需要准确地量化正常和异常的胎儿大脑发育 无论是从胎儿大脑结构的角度还是从身体运动的角度。更好的成像工具将使我们能够探索 胎儿神经紊乱以及环境暴露，如阿片类药物，母亲肥胖，或 新冠肺炎，会影响早期的大脑结构和身体运动。磁共振成像(MRI)T2加权， 单次拍摄的快速自旋回波(例如HASTE)图像提供了了解结构的这一关键阶段的独特窗口 大脑发育，有可能发现细微的异常。然而，胎儿大脑核磁共振是具有挑战性的，因为 对于胎儿运动，这会导致图像伪影、双斜获取和不完全的脑覆盖。作为一名 结果，训练有素的磁共振技术人员必须“追逐胎儿”来收集必要的图像来诊断胎儿的存在 或无病变，导致较长的扫描时间和较高的射频能量沉积。因此，胎脑MRI是 效率低下，仅限于专门的中心，而且诊断仍然很困难，因为胎儿运动会产生每一张图像 为独立切片，不能引用另一切片，确认可疑发现 很难。同时，胎儿运动是功能神经完整性的重要衡量标准，它告诉人们 出生后的结局。然而，目前的临床磁共振和超声对胎儿运动的评估并不完全。 同时捕捉身体所有部位的复杂3D运动。更好地评估胎儿神经健康状况 需要新的工具来自动高效地获得连贯、高质量的HASTE胎儿大脑体积和 目的：研究胎儿3D全身运动的特点。为了解决这些未得到满足的需求，我们将利用卷积神经 网络(CNN)模型，并提出以下目标：(1)开发自动驾驶引擎以实现高效收购 高质量的HASH胎儿脑体积和(2)实现自动胎儿全身运动跟踪和 人物刻画。我们将在一项前瞻性研究中部署建议的工具，将胎儿与Chiari II进行比较 畸形(脊柱裂)，一种已知有脑部异常的疾病，通常与 腿部运动，对典型的胎儿，目的如下：(3)评估自动驾驶加速引擎的性能 以及Chiari II与典型胎儿的全身运动特征。对于目标1和目标2，我们将包括数据 来自CNN推广的协作站点和策略，以增强部署我们的 其他扫描仪的工具。能够自动获得高质量连贯的胎儿脑体积和 表征胎儿运动将改善胎儿治疗的分层和对胎儿反应的表征 干预措施。成功还将使没有胎儿成像专家的网站能够在当地对胎儿进行评估和分类 将疑似异常的患者送到专门的治疗中心，以及方便以大量人口为基础 研究了解环境影响对早期大脑发育和胎儿行为的影响。

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.