Fast motion-robust fetal neuroimaging with MRI

项目摘要/摘要 胎儿脑磁共振成像(MRI)已成为研究胎儿早期发育的宝贵工具 并可以解决常规超声检查后可能仍然存在的诊断模糊问题。 不幸的是，胎儿和母体的高水平运动将胎儿MRI限制在快速二维(2D)序列 并且经常引入戏剧性的伪像，例如(2)图像相对于标准矢状面的方向错误， 临床评估所需的冠状、轴位平面；(3)部分至完全信号消失。 这些因素导致重复~30次S层叠切片采集直到静止为止的低效做法 已经获得了图像。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，主体运动是根本 阻碍充分发挥磁共振在回答胎儿临床和研究问题方面的优势。 该项目的总体目标是通过利用 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 建议将这些整合到MRI采集管道中，以释放胎儿MRI的潜力。我们将发展 用于自动动态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。我们假定这将从根本上提高产品质量。 临床和研究研究的成功率，同时显著降低患者的不适感和成本。 我们提出的目标1是消除(2)图像-大脑定位错误的脆弱性， 标准解剖平面的自动处方。在目标2中，我们建议讨论第(3)项议案 扫描时可实时校正胎头运动。解剖切片堆叠的采集将交错进行 使用体积导航器。这些将被用来测量扫描仪中发生的运动，并自适应地 更新切片倾斜/位置。我们建议作为目标3开发一个3D径向序列并估计 用于实时自动导航的径向辐条子集。自适应更新轮辐和轮辐的方向 在扫描结束时选择性地重新获取受损的子集将使胎儿大脑的3D成像成为可能(1)。 由于申请者有物理背景，麻省理工学院和HMS拟议的培训计划将重点放在 K99阶段的深度学习和胎儿发育/神经科学，以发展所需的技能 在R00阶段过渡到独立。申请者的目标是成为一名胎儿图像采集和 分析科学家充当深度学习、核磁共振和临床胎儿成像应用之间的桥梁，将 目前最先进的技术所能实现的范围。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 胎儿神经成像序列。

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.