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

基本信息

  • 批准号:
    10545512
  • 负责人:
  • 金额:
    $ 5.62万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-06-01 至 2022-11-30
  • 项目状态:
    已结题

项目摘要

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)已成为研究早期发育的宝贵工具, 并能解决常规超声波检查后可能存在的诊断模糊性。 不幸的是,胎儿和母体的高水平运动(1)限制了胎儿MRI的快速二维(2D)序列 并且经常引入显著的伪像,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、轴向平面和(3)部分至完全信号丢失。 这些因素导致重复约30 s层堆叠采集直到无运动的做法效率低下 图像已经获得。在整个过程中,技术人员手动调整扫描方向, 通常,大约38%的数据集会被丢弃。因此,主体运动是基本的 阻碍获得MRI的全部益处,以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。我们 建议将这些集成到MRI采集管道中,以释放胎儿MRI的潜力。我们将开发 用于自动和动态运动校正胎儿神经成像的实用脉冲序列技术 而不需要外部硬件或校准。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,同时显著降低患者的不适和成本。 我们提出的目标1是消除(2)采集对图像-大脑错误定向的脆弱性, 标准解剖平面的自动处方。在目标2中,我们建议在 实时校正胎儿头部运动的扫描。将交错进行解剖切片堆叠采集 用体积导航仪。这些将被用来测量运动,因为它发生在扫描仪和自适应 更新切片倾斜/位置。我们提出的目标3是开发一个3D径向序列,并估计 用于实时自我导航的径向辐条子集。自适应地更新辐条的方向, 在扫描结束时选择性地重新获取损坏的子集将使得能够对胎儿大脑进行3D成像(1)。 由于申请人有物理学背景,麻省理工学院和HMS的拟议培训计划将侧重于 在K99阶段进行深度学习和胎儿发育/神经科学,以发展所需的技能, 在R 00阶段向独立过渡。申请人的目标是成为一个胎儿图像采集和 分析科学家作为深度学习,MRI和临床胎儿成像应用程序之间的桥梁, 目前最先进的技术所能达到的极限。实现研究目标将促进 这将导致自动化和运动校正的实际框架,适用于各种各样的 胎儿神经成像序列。

项目成果

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Malte Hoffmann其他文献

Malte Hoffmann的其他文献

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{{ truncateString('Malte Hoffmann', 18)}}的其他基金

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
  • 财政年份:
    2020
  • 资助金额:
    $ 5.62万
  • 项目类别:
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
  • 财政年份:
    2020
  • 资助金额:
    $ 5.62万
  • 项目类别:

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