ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2022
Deep Learning-Based Microbubble Localization for Ultrasound Localization Microscopy.
Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 59 citations in OpenAlex.
- Deformable detection transformers for domain adaptable ultrasound localization microscopy with robustness to point spread function variations.Scientific reports · 2025Article
- Dual-modal super-resolution ultrasound and NIR-II fluorescence imaging of ischemic stroke with ICG-doped porous PLGA microspheres.Materials today. Bio · 2025Article
- Velocity-Constraint Kalman Filtering for Enhanced Bubble Tracking in Motion-Compensated Ultrasound Localization Microscopy.Research (Washington, D.C.) · 2025Article
- Comparison of high-frequency ultrasound transducers for microvascular localization microscopy in the mouse brain.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Inspiring a convergent engineering approach to measure and model the tissue microenvironment.Heliyon · 2024Review
- Context-aware deep learning enables high-efficacy localization of high concentration microbubbles for super-resolution ultrasound localization microscopy.Nature communications · 2024Article
- A Convolutional Neural Network for Beamforming and Image Reconstruction in Passive Cavitation Imaging.Sensors (Basel, Switzerland) · 2023Article
- Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals.Life (Basel, Switzerland) · 2023Review
- Localization Free Super-Resolution Microbubble Velocimetry Using a Long Short-Term Memory Neural Network.IEEE transactions on medical imaging · 2023Article
- Three-Dimensional Ultrasound Localization Microscopy with Bipartite Graph-Based Microbubble Pairing and Kalman-Filtering-Based Tracking on a 256-Channel Verasonics Ultrasound System with a 32 × 32 Matrix Array.Journal of medical and biological engineering · 2022Article
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Authors and funding
5 authors at 1 institution in 1 country.
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Abstract
Ultrasound localization microscopy (ULM) is an emerging vascular imaging technique that overcomes the resolution-penetration compromise of ultrasound imaging. Accurate and robust microbubble (MB) localization is essential for successful ULM. In this study, we present a deep learning (DL)-based localization technique that uses both Field-II simulation and in vivo chicken embryo chorioallantoic membrane (CAM) data for training. Both radio frequency (RF) and in-phase and quadrature (IQ) data were tested in this study. The simulation experiment shows that the proposed DL-based localization was able to reduce both missing MB localization rate and MB localization error. In general, RF data showed better performance than IQ. For the in vivo CAM study with high MB concentration, DL-based localization was able to reduce the vessel MB saturation time by more than 50% compared to conventional localization. In addition, we propose a DL-based framework for real-time visualization of the high-resolution microvasculature. The findings of this article support the use of DL for more robust and faster MB localization, especially under high MB concentrations. The results indicate that further improvement could be achieved by incorporating temporal information of the MB data.
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Registered trials
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