Benchmarking the Performance of Microbubble Localization Algorithms for Ultrasound Localization Microscopy

This article was originally published here

Nat Biomed Ing. 2022 Feb 17. doi: 10.1038/s41551-021-00824-8. Online ahead of print.

ABSTRACT

Ultrafast ultrasound localization microscopy can be used to detect acoustic scattering below the wavelength of intravenously injected microbubbles to obtain hemodynamic maps of the vasculature of animals and humans. The quality of the hemodynamic maps depends on the signal-to-noise ratios and the algorithms used for the localization of the microbubbles and the rendering of their trajectories. Here we report the results of the comparative performance analysis of seven microbubble localization algorithms. We used metrics for localization errors, localization success rates, processing times, and a measure of the reprojection of microbubble localization onto the original beam-like grid. We combined eleven parameters into an overall score and tested the algorithms in three simulated microcirculation datasets and in angiography datasets of the brain of a live rat after craniotomy, an excised rat kidney, and an excised rat kidney. mammary tumor in a living mouse. The algorithms, metrics and datasets, which we have made available at https://github.com/AChavignon/PALA and https://doi.org/10.5281/zenodo.4343435, will facilitate the identification or generation of optimal microbubbles. location algorithms for specific applications.

PMID:35177778 | DO I:10.1038/s41551-021-00824-8

Sharon D. Cole