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

项目概要/摘要 我们的前提是，胎儿阶段的人类大脑发育是最动态的，最脆弱的， 对终生的行为和认知功能最重要。正如许多神经系统疾病 在胎儿生命的起源，有必要准确地量化正常和异常的胎儿大脑发育， 胎儿大脑结构和身体运动的角度。更好的成像工具将使我们能够探索 胎儿神经系统疾病以及环境暴露，如阿片类药物，母亲肥胖，或 COVID-19影响早期大脑结构和身体运动。磁共振成像（MRI）T2加权， 单次快速自旋回波（如HASTE）图像提供了一个独特的窗口，进入这一关键阶段的结构 大脑发育，具有检测细微异常的潜力。然而，胎儿脑MRI具有挑战性， 胎儿运动，这导致图像伪影，双斜采集和不完整的大脑覆盖。作为 因此，受过训练的磁共振技术人员必须“追逐胎儿”，以收集必要的图像，以诊断胎儿的存在。 或没有损伤，导致扫描时间长和RF能量沉积更高。因此，胎儿脑MRI是 效率低，局限于专门的中心，诊断仍然很困难，因为每个图像都有胎儿运动的结果。 作为一个独立的切片，不能引用到另一个切片，确认可疑的发现 难与此同时，胎动是功能神经完整性的重要衡量标准， 产后结果。然而，目前的临床MR和超声评估胎儿运动并不完全， 同时捕捉所有身体部位的复杂3D运动。更好地评估胎儿神经系统健康 需要新的工具来自动和有效地获得连贯的、高质量的HASTE胎儿脑体积， 来表征3D胎儿全身运动。为了解决这些未满足的需求，我们将利用卷积神经网络， 网络（CNN）模型，并提出以下目标：（1）开发一个自动驾驶引擎，以实现有效的收购 高质量HASTE胎儿脑体积和（2）实现自动化胎儿全身运动跟踪， 特征化我们将在一项前瞻性研究中部署所提出的工具，比较胎儿与基亚里II型 畸形（脊柱裂），一种已知具有大脑异常的疾病，通常与减少 腿部运动，以典型的胎儿，具有以下目的：（3）评估自动驾驶HASTE引擎的性能 基亚里II与典型胎儿的全身运动特征。对于目标1和2，我们将包括数据 从合作网站和CNN推广战略，以增加鲁棒性和潜力，部署我们的 其他扫描仪的工具。自动获得高质量连贯胎儿脑体积的能力， 表征胎动将改善胎儿治疗的分层和对胎儿反应的表征 干预措施。成功也将使没有胎儿成像专家的网站在当地评估和分类胎儿 将疑似异常的患者送到专门的治疗中心，并促进基于大量人群的 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.