Fast motion-robust fetal neuroimaging with MRI

使用 MRI 进行快速运动稳健的胎儿神经成像

• 批准号: 10197182
• 负责人: Malte Hoffmann
• 金额: $ 10.97万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-17 至 2022-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10197182
• 关键词: 3-Dimensional; Address; Algorithmic Analysis; Amniotic Fluid; Anatomy; Automation; Brain; Brain imaging; Calibration; Childhood; Clinical; Clinical Research; Clinical assessments; Data Set; Development; Diagnostic; Echo-Planar Imaging; Fetal Development; Fetus; Geometry; Goals; Head; Image; Individual; Label; Lead; Learning; Magnetic Resonance Imaging; Manuals; Masks; Measures; Morphologic artifacts; Motion; Neurosciences; Patients; Phase; Physics; Physiologic pulse; Population; Positioning Attribute; Radial; Recording of previous events; Research; Residual state; Resolution; Sampling; Scanning; Scientist; Signal Transduction; Slice; Speed; Technology; Thick; Three-Dimensional Imaging; Time; Tissues; Training; Training Programs; Translating; Ultrasonography; Update; Work; base; clinical investigation; convolutional neural network; cost; deep learning; echo detection; experience; fetal; image archival system; improved; innovation; interest; neuroimaging; novel; prospective; radiologist; reconstruction; repaired; research study; response; skills; success; tool; two-dimensional

PROJECT SUMMARY/ABSTRACT Fetal-brain magnetic resonance imaging (MRI) has become an invaluable tool for studying the early development of the brain and can resolve diagnostic ambiguities that may remain after routine ultrasound exams. Unfortunately, high levels of fetal and maternal motion (1) limit fetal MRI to rapid two-dimensional (2D) sequences and frequently introduce dramatic artifacts such as (2) image misorientation relative to the standard sagittal, coronal, axial planes needed for clinical assessment and (3) partial to complete signal loss. These factors lead to the inefficient practice of repeating ~30 s stack-of-slices acquisitions until motion-free images have been obtained. Throughout the session, technologists manually adjust the orientation of scans in response to motion, and about 38% of datasets are typically discarded. Thus, subject motion is the fundamental impediment to reaping the full benefits of MRI for answering clinical and investigational questions in the fetus. The overarching goal of this project is to overcome the challenges posed by motion by exploiting innovations in deep learning, which have enabled image-analysis algorithms with unprecedented speed and reliability. We propose to integrate these into the MRI acquisition pipeline to unlock the potential of fetal MRI. We will develop practical pulse-sequence technology for automated and dynamically motion-corrected fetal neuroimaging without the need for external hardware or calibration. We hypothesize that this will radically improve the quality and success rates of clinical and research studies, while dramatically reducing patient discomfort and cost. We propose as Aim 1 to eradicate (2) the vulnerability of acquisitions to image-brain misorientation with rapid, automated prescription of standard anatomical planes. In Aim 2, we propose to address (3) motion during the scan with real-time correction of fetal-head motion. An anatomical stack-of-slices acquisition will be interleaved with volumetric navigators. These will be used to measure motion as it happens in the scanner and to adaptively update the slice tilt/position. We propose as Aim 3 to develop a 3D radial sequence and estimate motion between subsets of radial spokes for real-time self-navigation. Adaptively updating the orientation of spokes and selectively re-acquiring corrupted subsets at the end of the scan will enable 3D imaging of the fetal brain (1). Since the applicant has a physics background, the proposed training program at MIT and HMS will focus on deep learning and fetal development/neuroscience during the K99 phase to develop the skills needed for transitioning to independence in the R00 phase. The applicant’s goal is to become a fetal image acquisition and analysis scientist acting as bridge between deep learning, MRI and clinical fetal-imaging applications to shift the boundaries of what is currently possible with state-of-the-art technology. Fulfilling the research aims will promote this, as it will result in a practical framework for automation and motion correction, applicable to a wide variety of fetal neuroimaging sequences.

项目概要/摘要 胎儿脑磁共振成像（MRI）已成为研究早期发育的宝贵工具 大脑的，可以解决常规超声检查后可能残留的诊断模糊性。 不幸的是，胎儿和母亲的高水平运动 (1) 将胎儿 MRI 限制为快速二维 (2D) 序列 并经常引入戏剧性的伪影，例如 (2) 相对于标准矢状面的图像定向错误， 临床评估所需的冠状面、轴向面以及（3）部分至完全信号丢失。 这些因素导致重复约 30 秒的切片堆栈采集直到无运动的低效实践 在整个过程中，技术人员手动调整扫描方向。 对运动的响应，大约 38% 的数据集通常被丢弃，因此，主体运动是基础。 阻碍充分利用 MRI 来回答胎儿的临床和研究问题。 该项目的总体目标是通过利用创新来克服运动带来的挑战 深度学习使图像分析算法具有前所未有的速度和可靠性。 建议将这些集成到 MRI 采集管道中，以释放胎儿 MRI 的潜力。 用于自动化和动态运动校正胎儿神经成像的实用脉冲序列技术 无需外部硬件或校准，我们努力希望这将从根本上提高质量。 和临床和研究的成功率，同时大大减少患者的不适和成本。 我们建议目标 1 是通过快速、 标准解剖平面的自动处方 在目标 2 中，我们建议解决 (3) 运动过程中的问题。 实时校正胎儿头部运动的扫描将交错进行解剖堆叠切片采集。 这些将用于测量扫描仪中发生的运动并自适应地进行测量。 我们建议目标 3 开发 3D 径向序列并估计之间的运动。 用于实时自我导航的径向辐条子集和自适应更新辐条的方向。 在扫描结束时选择性地重新获取损坏的子集将能够对胎儿大脑进行 3D 成像 (1)。 由于申请人具有物理学背景，因此在麻省理工学院和 HMS 拟议的培训计划将侧重于 K99 阶段的深度学习和胎儿发育/神经科学，以培养所需的技能 在R00阶段过渡到独立，申请人的目标是成为胎儿图像采集和处理人员。 分析科学家充当深度学习、MRI 和临床胎儿成像应用之间的桥梁，以改变 目前最先进技术的可能性边界将促进实现研究目标。 这将产生一个实用的自动化和运动校正框架，适用于各种领域 胎儿神经影像序列。