Vector Flow Velocity Imaging of Human Placenta using Angle-resolved Ultrasound and Deep Learning

这份建议书描述了一个为期五年的研究和职业发展计划，为李友博士的职业生涯做准备 作为一名独立调查员该计划将建立在李博士的多学科背景，作为生物医学， 工程师，在医疗超声成像培训，通过提供产科专业知识，机器的应用， 学习医学影像学和转化研究。PI将在斯坦福大学接受Dr. 杰里米·达尔（初级导师，医学超声），弗吉尼亚·韦恩（共同导师，妇产科），和 Matthew Lungren（放射学和人工智能联合导师）。人类胎盘在人类发育中起着至关重要的作用， 发育，其异常可能会对母亲和胎儿造成重大后果。 特别是先兆子痫，是一种常见的疾病，在美国每33例妊娠中就有1例受到影响。 在与妊娠有关的孕产妇死亡中，许多胎盘异常，包括 子痫前期与胎盘血流动力学和血管生长有关。尽管严重的 胎盘异常的后果，我们对胎盘和胎盘异常的理解是缺乏的。 造成这一认识上的差距的一个主要原因是无法在体内观察胎盘的血流动力学。 目前，B型和多普勒超声是对胎盘及其周围组织进行成像的主要成像方式。 脉管系统然而，多普勒超声的可视化和测量能力存在显著的局限性。 胎盘脉管系统中的详细流速。它对胎盘中的小血管敏感性低，包括 螺旋动脉和绒毛膜绒毛，只能测量沿着超声束方向的流速， 如果定量信息 需要的话这些局限性使得常规多普勒超声不适合于成像血流动力学 人类胎盘复杂脉管系统的一部分提供胎盘的完整和详细表征 血流动力学，我们建议开发一种使用深度神经网络的矢量流速成像技术 模型和多角度平面波超声发射。这项技术将能够定量成像 胎盘中毫米直径血管的流速大小和流向， 大视野。目标1和2将集中在技术的技术发展，以提供一个半 基于研究超声扫描仪的实时矢量流成像系统，为Aim 3铺平道路，Aim 3将 重点是在初步临床研究中验证该技术的临床价值，以成像血流动力学 在螺旋动脉和绒毛膜绒毛中。该项目的成功完成将提供一个新的 科学和早期临床评估胎盘血流动力学、发育和 异常，包括绒毛树结构的发育、胎盘植入、先兆子痫，以及 胎盘功能不全在项目期间，PI将接受机器学习，产科，翻译 研究，和职业发展技能，这将过渡到一个独立的教师PI。

This proposal describes a five-year research and career development program to prepare Dr. You Li for a career as an independent investigator. The program will build upon Dr. Li’s multidisciplinary background as a biomedical engineer, trained in medical ultrasound imaging, by providing expertise in obstetrics, the application of machine learning in medical imaging, and translational research. The PI will be mentored at Stanford University by Drs. Jeremy Dahl (primary mentor, medical ultrasound), Virginia Winn (co-mentor, obstetrics and gynecology), and Matthew Lungren (co-mentor, radiology and artificial intelligence). Human placenta plays a vital role in human development, and its abnormalities may cause significant consequences to both the mother and the fetus. Preeclampsia, in particular, is a common disorder that affects approximately 1 in 33 pregnancies in the United States and accounts for 18% of pregnancy-associated maternal death. Many placental abnormalities, including preeclampsia, are related to the hemodynamics and growth of vessels in placenta. Despite the severe consequences of placental abnormalities, our understanding in placenta and placental abnormalities is lacking. One primary reason for the gap of knowledge is the inability to observe the hemodynamics of placenta in vivo. Currently, B-mode and Doppler ultrasound are the primary imaging modalities in imaging the placenta and its vasculature. However, significant limitations exist in the ability of Doppler ultrasound to visualize and measure detailed flow velocities in placental vasculature. It has low sensitivity to small vessels in the placenta, including spiral arteries and chorionic villi, and can only measure flow velocity along the ultrasound beam direction, requiring tedious manual angle correction for flow along any of the visible vessels if quantitative information is desired. These limitations make conventional Doppler ultrasound poorly suited for imaging the hemodynamics of the complex vasculature of human placenta. To provide full and detailed characterization of placental hemodynamics, we propose to develop a vector flow velocity imaging technique using deep neural network models and multiple angle plane wave ultrasound transmits. This technique will be able to quantitatively image both the flow velocity magnitudes and flow directions of millimeter-diameter vessels in human placenta over a large field of view. Aim 1 and 2 will be focused on the technical development of the technique to provide a semi- real-time vector flow imaging system based on a research ultrasound scanner, paving way for Aim 3, which will be focused on validating the clinical value of the technique on a pilot clinical study to image the hemodynamics in spiral arteries and chorionic villi of pregnant women. Successful completion of the project will provide a novel technique for the scientific and early clinical assessment of placental hemodynamics, development, and abnormalities including the development of the villous tree structure, placenta accreta, preeclampsia, and placental insufficiency. During the project, the PI will receive training in machine learning, obstetrics, translational research, and career development skills, which will transition the PI into an independent faculty.