Ultrasound localization microscopy lite (ULM lite): ultrasound localization microscopy with resource-efficient signal...

Area of Science:

• Biomedical Imaging
• Medical Technology
• Neuroscience

Background:

• Ultrasound localization microscopy (ULM) offers high-resolution, non-invasive monitoring of vascular hemodynamics and neuronal activity in vivo.
• Current ULM methods generate extensive data, limiting widespread application and resource efficiency.
• Significant data reduction is needed to enhance ULM's practicality for deep neuronal activity visualization.

Purpose of the Study:

• To introduce and validate a sub-Nyquist sampling method for radio-frequency (RF) signals in ULM.
• To assess the impact of data reduction on image quality metrics like signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR).
• To demonstrate the in vivo feasibility and resource efficiency of the proposed ULM approach.

Main Methods:

• Implementation of sub-Nyquist sampling on band-limited RF signals within the ULM framework.
• Experimental validation of the sub-Nyquist sampling method in vivo.
• Quantitative analysis of SNR and CNR for sub-Nyquist sampled images compared to conventional methods.

Main Results:

• Sub-Nyquist sampling successfully reduced data size by approximately one-third.
• Images acquired using the proposed method achieved high SNR and CNR, comparable to conventional ULM.
• The method demonstrated in vivo feasibility without compromising essential image quality parameters.

Conclusions:

• Sub-Nyquist sampling is a viable strategy for reducing ULM data requirements.
• This approach enhances the efficiency and practicality of ULM for non-invasive deep neuronal activity imaging.
• The findings support the broader adoption of ULM by addressing current data size limitations.