[论文解读] BUbble Flow Field: a Simulation Framework for Evaluating Ultrasound Localization Microscopy Algorithms

Marcelo Lerendegui, Kai Riemer+3 more|arXiv (Cornell University)|Nov 1, 2022

Photoacoustic and Ultrasonic Imaging被引用 14

一句话总结

本文提出 BUbble Flow Field，一种用于评估超声定位显微镜（ULM）算法的仿真框架，通过流体动力学原理在血管网络中模拟血流。该框架基于分叉处的流量比例，模拟气泡在血管网络中的轨迹，从而生成具有物理准确血流动力学特性的真实轨迹，用于算法验证。

ABSTRACT

Ultrasound contrast enhanced imaging has seen widespread uptake in research and clinical diagnostic imaging. This includes applications such as vector flow imaging, functional ultrasound and super-resolution Ultrasound Localization Microscopy (ULM). All of these require testing and validation during development of new algorithms with ground truth data. In this work we present a comprehensive simulation platform BUbble Flow Field (BUFF) that generates contrast enhanced ultrasound images in vascular tree geometries with realistic flow characteristics and validation algorithms for ULM. BUFF allows complex micro-vascular network generation of random and user-defined vascular networks. Blood flow is simulated with a fast Computational Fluid Dynamics (CFD) solver and allows arbitrary input and output positions and custom pressures. The acoustic field simulation is combined with non-linear Microbubble (MB) dynamics and simulates a range of point spread functions based on user-defined MB characteristics. The validation combines both binary and quantitative metrics. BFF's capacity to generate and validate user-defined networks is demonstrated through its implementation in the Ultrasound Localisation and TRacking Algorithms for Super Resolution (ULTRA-SR) Challenge at the International Ultrasonics Symposium (IUS) 2022 of the Institute of Electrical and Electronics Engineers (IEEE). The ability to produce ULM images, and the availability of a ground truth in localisation and tracking enables objective and quantitative evaluation of the large number of localisation and tracking algorithms developed in the field. BUFF can also benefit deep learning based methods by automatically generating datasets for training. BUFF is a fully comprehensive simulation platform for testing and validation of novel ULM techniques and is open source.

研究动机与目标

• 解决当前缺乏标准化、物理上准确的仿真框架来评估超声定位显微镜（ULM）算法的问题。
• 利用 Hagen-Poiseuille 方程和线性方程组建模血管网络中的血流动力学。
• 生成反映复杂血管网络中真实流动行为的真值气泡轨迹。
• 通过已知流动和位置真值的合成数据，实现对 ULM 重建算法的定量评估。

提出的方法

• 将血管网络建模为有向图，使用关联矩阵 I 编码节点-边关系。
• 将关联矩阵划分为 I_h（悬挂节点）和 I_nh（非悬挂节点），以区分已知和未知压力。
• 通过线性系统 Mx = b 求解未知节点压力，其中 M = I_nh^T C I_nh 且 b = I_nh^T C I_h P_0。
• 应用 Hagen-Poiseuille 定律，根据血管半径和流体黏度计算边的流动导纳 C。
• 通过按流量比例分配分叉路径的概率来生成气泡轨迹，确保层流一致性。
• 以与中心线速度成比例的速度沿路径模拟气泡运动，通过流线约束保持径向位置。

实验结果

研究问题

• RQ1 如何构建一个物理上准确的血管流动仿真，以作为 ULM 算法的真值基准？
• RQ2 模拟的气泡轨迹在多大程度上反映了复杂血管网络中真实的血流动态行为？
• RQ3 能否可靠地利用分叉处的基于流动的概率来生成具有统计代表性的气泡轨迹？
• RQ4 该框架如何确保网络中模拟的流动、压力与粒子运动之间的一致性？
• RQ5 网络拓扑结构和血管几何形状对生成轨迹的准确性和真实性有何影响？

主要发现

• 该框架成功利用基于关联矩阵和导纳值的线性系统，计算出血管网络中的压力和流量分布。
• 气泡轨迹的概率被准确计算为沿每条路径的基于流量的分叉概率的乘积。
• 通过方程 I_nh^T Q_e = 0 确保在所有非悬挂节点处实现流量守恒。
• 边的流量通过 Q_e = C P_e 计算，其中导纳 C 由血管半径和流体黏度推导得出。
• 该方法可生成大量反映真实流动动力学和网络拓扑的气泡轨迹。
• 只要悬挂节点的压力已知，其可被放置在网络中的任意位置而不影响求解结果。