Integrated Sensing: Non-Invasive Ultrasound-Based Micro-Flow Imaging System for Biomedical Applications

对于目前的临床成像系统来说，对体内微尺度血流的无创成像和定量仍然是一个挑战。本文提出了一种用于微尺度流动检测和定量的超声成像系统的开发。将使用两套商用相控阵宽带换能器。这些将与定制的电子和分析系统相结合，对流场中充满气体的微气泡（直径：3 - 8微米）进行成像。这个问题的第一部分是精确的粒子检测和跟踪。第二部分是成像系统的发展，揭示了局部流动特征。高反射微气泡的非线性后向散射将在射频域进行分析，以检测粒子。一旦检测到粒子，将使用粒子跟踪算法在指定距离内跟踪粒子。这将允许计算局部粒子速度。该项目的目标是：1)描述基于超声的微流成像系统的设计规范。2)结合现有的宽带超声换能器，结合定制的电子设备和算法开发，构建该系统的原型版本。3)使用模拟循环流环路验证该原型系统的性能，并使用独立参考技术对速度和流量进行量化。在本项目结束时，将完成基于超声的微流成像系统的开发工作。然后，工作可以转向在硬件中实施该系统，以便在体内条件下进行测试。

The non-invasive imaging and quantitation of micro-scale flow within the body is still a challenge for current clinical imaging systems. The development of an ultrasound-based imaging system for detection and quantitation of micro-scale flows is proposed here. Two sets of commercially available phased-array broadband transducers will be used. These will be coupled to custom electronics and analysis systems to image gas-filled micro-bubbles (diameters: 3 - 8 microns) within the flow field. The first part of this problem is precise particle detection and tracking. The second part is the development of an imaging system that reveals local flow characteristics. The non linear components of backscatter from the highly reflective micro-bubbles will be analyzed in the RF domain to detect particles. Once the particle is detected, a particle tracking algorithm will be employed to track the particles over a specified distance. This will allow calculation of local particle velocity. The project aims are:1) Characterize the design specifications for an ultrasound-based micro-flow imaging system.2) Build a prototype version of this system using a combination of existing broadband ultrasound transducers, coupled with custom electronics and algorithm development.3) Validate the performance of this prototype system using a mock circulatory flow loop with velocity and flow quantified using independent reference techniques.At the end of this project, significant work toward the development of an ultrasound-based micro-flow imaging system will be complete. The work can then move toward implementation of the system in hardware for testing under in vivo conditions.