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

使用角度分辨超声和深度学习对人胎盘进行矢量血流速度成像

基本信息

批准号：
10490424
负责人：
You Li
金额：
$ 12.64万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-17 至 2023-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10490424
关键词：
Acoustics Affect Artificial Intelligence Biology Biomedical Engineering Blood Blood flow Caliber Chorionic villi Clinic Clinical Clinical Research Clinical assessments Complex Data Data Science Development Discipline of obstetrics Disease Doppler Ultrasound Faculty Fetal Growth Retardation Fetus Future Growth Gynecology Human Human Development Image Imaging Device Imaging Techniques Imaging technology Investigation Knowledge Lateral Machine Learning Manuals Maternal Mortality Measurement Measures Medical Imaging Mentors Methodology Modeling Monitor Mothers Network-based Neural Network Simulation Noise Output Pattern Physiological Placenta Placenta Accreta Placenta Diseases Placental Insufficiency Placentation Play Pre-Eclampsia Pregnancy Pregnant Women Premature Labor Program Development Radiology Specialty Research Research Personnel Resolution Role Spiral Artery of the Endometrium Structure System Techniques Technology Testing Time Tissues Training Translating Translational Research Trees Ultrasonic wave Ultrasonography Uncertainty United States Universities Villous Virginia Visualization base career career development cohort deep learning deep learning model deep neural network design early pregnancy fetal hemodynamics human imaging imaging modality imaging system in vivo machine learning model millimeter multidisciplinary neural network neural network architecture new technology novel programs quantitative imaging research and development simulation skill acquisition skills technique development tenure track tool ultrasound vector

项目摘要

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.

该提案描述了一个为期五年的研究和职业发展计划，旨在为李尤博士的职业生涯做好准备作为一名独立研究者，该项目将建立在李博士作为生物医学的多学科背景之上。工程师，接受过医学超声成像培训，提供产科、机器应用方面的专业知识 PI 将在斯坦福大学接受医学成像和转化研究方面的学习。 Jeremy Dahl（主要导师，医学超声）、Virginia Winn（共同导师，妇产科）和 Matthew Lungren（放射学和人工智能联合导师）。人类胎盘在人类中发挥着至关重要的作用。发育及其异常可能会对母亲和胎儿造成严重后果。尤其是先兆子痫是一种常见疾病，在美国大约每 33 次妊娠中就有 1 次受到影响妊娠相关孕产妇死亡中 18% 是由多种胎盘异常引起的。先兆子痫尽管严重，但与血流动力学和胎盘血管生长有关。由于胎盘异常的后果，我们对胎盘和胎盘异常的了解还很缺乏。造成知识差距的一个主要原因是无法观察体内胎盘的血流动力学。目前，B超和多普勒超声是胎盘及其组织成像的主要成像方式。然而，多普勒超声的可视化和测量能力存在重大限制。它对胎盘中的小血管（包括胎盘小血管）的敏感度较低。螺旋动脉和绒毛膜绒毛，只能测量沿超声波束方向的流速，如果定量信息是，则需要对沿任何可见血管的流动进行繁琐的手动角度校正这些限制使得传统的多普勒超声不太适合血流动力学成像。人类胎盘复杂脉管系统的研究提供胎盘的完整和详细的表征。血流动力学，我们建议使用深度神经网络开发矢量流速度成像技术模型和多角度平面波超声波传输该技术将能够定量成像。人类胎盘中毫米直径血管的流速大小和流动方向目标 1 和 2 将集中于提供半视野技术的技术开发。基于研究型超声扫描仪的实时矢量流成像系统，为 Aim 3 铺平道路，这将专注于验证该技术在血流动力学成像试点临床研究中的临床价值该项目的成功完成将为孕妇的螺旋动脉和绒毛膜绒毛提供一种新颖的方法。用于对胎盘血流动力学、发育和发育进行科学和早期临床评估的技术异常，包括绒毛树结构发育、植入性胎盘、先兆子痫和在项目期间，PI 将接受机器学习、产科、转化方面的培训。研究和职业发展技能，这将使 PI 转变为独立的教师。