bioRxivBioengineering
Improved Ultrasound Localization Microscopy based on Microbubble Uncoupling via Transmit Excitation (MUTE)
J. Kim, M. Lowerison, N. C. Sekaran, Z. Kou, Z. Dong, O. L. Michael, D. A. Llano, P. Song
Abstract
Ultrasound localization microscopy (ULM) demonstrates great potential for visualization of tissue microvasculature at depth with high spatial resolution. The success of ULM heavily depends on the robust localization of isolated microbubbles (MBs), which can be challenging in vivo especially within larger vessels where MBs can overlap and cluster close together. While MB dilution alleviates the issue of MB overlap to a certain extent, it drastically increases the data acquisition time needed for MBs to populate the microvasculature, which is already on the order of several minutes using recommended MB concentrations. Inspired by optical super-resolution imaging based on stimulated emission depletion (STED), here we propose a novel ULM imaging sequence based on microbubble uncoupling via transmit excitation (MUTE). MUTE "silences" MB signals by creating acoustic nulls to facilitate MB separation, which leads to robust localization of MBs especially under high concentrations. The efficiency of localization accomplished via the proposed technique was first evaluated in simulation studies with conventional ULM as a benchmark. Then an in vivo study based on the chorioallantoic membrane (CAM) of chicken embryos showed that MUTE could reduce the data acquisition time by half thanks to the enhanced MB separation and localization. Finally, the performance of MUTE was validated in an in vivo mouse brain study. These results demonstrate the high MB localization efficacy of MUTE-ULM, which contributes to a reduced data acquisition time and improved temporal resolution for ULM.
- organism: chicken / avian
- organism: mouse
- assay: imaging
- research focus: imputation
- approach: experimental assay
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Deep Learning-based Microbubble Localization for Ultrasound Localization Microscopy
X. Chen, M. Lowerison, … P. Song·bioRxiv·Feb 2, 2022
- Context-Aware Deep Learning Enables High-Efficacy Localization of High Concentration Microbubbles for Super-Resolution Ultrasound Localization Microscopy
Y. Shin, M. Lowerison, … P. Song·bioRxiv·Apr 21, 2023
- Curvelet Transform-based Sparsity Promoting Algorithm for Fast Ultrasound Localization Microscopy
Q. You, J. Trzasko, … P. Song·bioRxiv·Feb 2, 2022
- Improved Microbubble Tracking for Super-Resolution Ultrasound Localization Microscopy using a Bi-Directional Long Short-term Memory Neural Network
Xi Chen, Matthew R. Lowerison, … Pengfei Song·bioRxiv·Feb 10, 2025
- Nanobubble-Based Ultrasound Localization Microscopy through Interactive Adaptive Processing
Grigori Shapiro, Yarin Gershman, … Tali Ilovitsh·bioRxiv·Jul 17, 2026
- Backscattering Amplitude in Ultrasound Localization Microscopy
N. Renaudin, S. Pezet, … M. Tanter·bioRxiv·Apr 27, 2023