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: 10756678
• 负责人: Malte Hoffmann
• 金额: $ 24.9万
• 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 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10756678
• 关键词: 3-Dimensional; Address; Algorithmic Analysis; Amniotic Fluid; Anatomy; Archives; Automation; Brain; Brain imaging; Calibration; Childhood; Clinical; Clinical Research; Clinical assessments; Data Set; Development; Diagnostic; Echo-Planar Imaging; Exclusion; 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; Update; Work; brain magnetic resonance imaging; convolutional neural network; cost; deep learning; echo detection; experience; fetal; improved; innovation; interest; neuroimaging; novel; prospective; radiologist; reconstruction; repaired; research study; response; skills; success; tool; two-dimensional; ultrasound

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.

项目摘要/摘要 胎儿脑磁共振成像（MRI）已成为研究早期发展的宝贵工具 大脑并可以解决常规超声检查后可能保留的诊断歧义。 不幸的是，高水平的胎儿和母体运动（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）相对于标准矢状的图像不良方向， 临床评估所需的冠状，轴向平面以及（3）部分以完成信号损失。 这些因素导致重复约30 s的切片堆栈的效率低下，直到无运动 图像已经获得。在整个会话中，技术手动调整扫描的方向 对运动的响应，大约38％的数据集通常被丢弃。那是主题运动是基本的 在回答胎儿中回答临床和研究问题的全部障碍中，障碍。 该项目的总体目标是克服通过利用创新来克服运动所带来的挑战 深度学习，这使图像分析算法具有前所未有的速度和可靠性。我们 提议将它们整合到MRI采集管道中，以释放胎儿MRI的潜力。我们将发展 自动化和动态运动校正的胎儿神经影像学的实用脉冲序列技术 无需外部硬件或校准。我们假设这将从根本上提高质量 以及临床和研究的成功率，同时大大降低了患者的不适和成本。 我们提出作为目标1的目标（2）收购对图像 - 脑不适的脆弱性，并以快速的， 标准解剖平面的自动处方。在AIM 2中，我们建议解决（3）在（3）期间的运动 通过实时校正胎头运动进行扫描。解剖堆栈的收购将交错 带有体积的导航器。这些将用于测量扫描仪中发生的运动并适应 更新切片倾斜/位置。我们建议作为目标3发展3D径向序列并估计运动 径向辐条的子集用于实时自动化。自适应更新辐条的方向和 在扫描结束时有选择地重新获得损坏的子集将使胎儿大脑的3D成像（1）。 由于申请人具有物理背景，MIT和HMS的拟议培训计划将重点关注 在K99阶段，深度学习和胎儿的发展/神经科学发展所需的技能 在R00阶段过渡到独立性。申请人的目标是成为胎儿形象的获取和 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 最先进的技术目前可能发生的边界。实现研究目标将促进 这将导致一个实用的自动化和运动校正框架，适用于各种各样的 胎儿神经成像序列。