Investigation of Microbubble Detection Methods for Super-Resolution Imaging of Microvasculature

Ultrasound super-resolution techniques use the response of microbubble (MB) contrast agents to visualize the microvasculature. Techniques that localize isolated bubble signals first require detection algorithms to separate the MB and tissue responses. This work explores the three main MB detection techniques for super-resolution of microvasculature. Pulse inversion (PI), differential imaging (DI), and singular value decomposition (SVD) filtering were compared in terms of the localization accuracy, precision, and contrast-to-tissue ratio. MB responses were simulated based on the properties of Sonovue and using the Marmottant model. Nonlinear propagation through tissue was modeled using the k-Wave software package. For the parameters studied, the results show that PI is most appropriate for low frequency applications, but also most dependent on transducer bandwidth. SVD is preferable for high frequency acquisition where localization precision on the order of a few microns is possible. PI is largely independent of flow direction and speed compared to SVD and DI, so is appropriate for visualizing the slowest flows and tortuous vasculature. SVD is unsuitable for stationary MBs and can introduce a localization error on the order of hundreds of microns over the speed range 0-2 mm/s and flow directions from lateral (parallel to probe) to axial (perpendicular to probe). DI is only suitable for flow rates >0.5 mm/s or as flow becomes more axial. Overall, this study develops an MB and tissue nonlinear simulation platform to improve understanding of how different MB detection techniques can impact the super-resolution process and explores some of the factors influencing the suitability of each.