Optimizingin vivodata acquisition for robust clinical microvascular imaging using ultrasound localization microscopy

Objective. Ultrasound localization microscopy (ULM) enables microvascular imaging at spatial resolutions beyond the acoustic diffraction limit, offering significant clinical potentials. However, ULM performance relies heavily on microbubble (MB) signal sparsity, the number of detected MBs, and signal-to-noise ratio (SNR), all of which vary in clinical scenarios involving bolus MB injections. These sources of variations underscore the need to optimize MB dosage, data acquisition timing, and imaging settings in order to standardize and optimize ULM of microvasculature. This pilot study aims to investigate the temporal changes in MB signals during bolus injections in both pig and human models to optimize data acquisition for clinical ULM.Approach.Quantitative indices, mainly including individual MB SNR, normalized cross-correlation (NCC) of the MB signal with the point-spread function, and the number of localizable MBs, were developed to evaluate MB signal quality and guide the selection of acquisition timing. The effects of transmitted voltage and dosage on signal quality for MB localization were also explored.Main results. In both pig and human studies, MB localization quality (primarily indicated by NCC) reached a minimum at peak MB concentration, then improved as MB counts decreased during the wash-out phase. An optimal acquisition window was identified by balancing localization quality (empirically, NCC > 0.57) and MB concentration. In the pig model, a relatively short time window (approximately 10 s) for optimal acquisition was identified during the rapid wash-out phase, highlighting the need for real-time MB signal monitoring during data acquisition. The slower wash-out phase in humans allowed for a more flexible imaging window of 1-2 min, while trade-offs were observed between localization quality and MB density (or acquisition length) at different wash-out phase timings. Guided by these findings, robust ULM imaging was achieved in both pig and human kidneys using a short period of data acquisition (3.6 s and 9.6 s of data), demonstrating its feasibility in clinical practice.Significance.This study provides insights into optimizing data acquisition for consistent and reproducible ULM, paving the way for its standardization and broader clinical applications.