Analytic Optimization-Based Microbubble Tracking in Ultrasound Super-Resolution Microscopy

TL;DR

This paper introduces an analytic optimization-based method for tracking microbubbles in ultrasound super-resolution microscopy, improving vascular mapping by considering physical plausibility and fuzzy parity inference.

Contribution

It presents a novel microbubble tracking technique formulated as a bubble-set registration problem with analytical optimization and fuzzy parity inference, advancing ULM accuracy.

Findings

Effective in synthetic datasets

Validated on in vivo datasets

Outperforms existing methods

Abstract

Ultrasound localization microscopy (ULM) refers to a promising medical imaging modality that systematically leverages the advantages of contrast-enhanced ultrasound (CEUS) to surpass the diffraction barrier and delineate the microvascular map. Localization and tracking of microbubbles (MBs), two significant steps of ULM, facilitate generating the vascular map and the velocity distribution, respectively. Herein, we propose a novel MB tracking technique considering temporal pairing as a bubble-set registration problem. Iterative registration is performed between the bubble sets in two consecutive time instants by analytically optimizing a cost function that takes position and point-spread function (PSF) similarities as well as physically plausible levels of bubbles' movement into account. Furthermore, we infer MBs' parity in a fuzzy manner instead of binary. The proposed technique performs well in validation experiments with two synthetic and two in vivo datasets provided by the Ultrasound Localisation and TRacking Algorithms for Super Resolution (ULTRA-SR) Challenge.