ArticleIEEE transactions on medical imaging2026
Microbubble Backscattering Intensity Improves the Sensitivity of Three-Dimensional (3-D) Functional Ultrasound Localization Microscopy (fULM).
Article in IEEE transactions on medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Longitudinal Awake Mouse Brain Imaging Using Functional Ultrasound and Functional Ultrasound Localization Microscopy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Microbubble Track-Based Functional Ultrasound Localization Microscopy in Awake Mice.IEEE transactions on medical imaging · 2026Article
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6 authors.
Funding
Abstract
Functional ultrasound localization micro- scopy (fULM) enables brain-wide mapping of neural activity at micron-scale resolution but suffers from limited sensitivity due to sparse and noisy microbubble (MB) detections. Extending fULM into three dimensions (3D) further exacerbates these challenges because of low-frequency matrix arrays, reduced localization efficiency, and severe data sparsity. To address these limitations, we developed a statistical framework that models MB arrivals in 3D as a Poisson process accounting for localization efficiency, detection probability, and backscattered amplitude. This analysis predicts that integrating amplitude with count-based fULM improves functional sensitivity, particularly under high MB concentrations where localization saturates. Three-dimensional MB advection simulations confirmed these predictions, showing that backscattering fULM (B-fULM) maintains sensitivity at higher MB concentrations where conventional fULM fails. In rat brain experiments, B-fULM yielded stronger and more robust stimulus-evoked responses, with SNR gains of 18% in the somatosensory cortex and 61% in the thalamus, while preserving super-resolved spatial detail ( $33.4~\mu $ m for B-fULM vs $35.7~\mu $ m for fULM). These results establish B-fULM as a practical and sensitive approach for super-resolved 3D functional neuroimaging.
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