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We report the potentials of nanometer-sized contrast agents which are called gas vesicles (GVs) for super-resolution ultrasound (SRUS) imaging to diagnose of vasculature deep inside tissue. Thus, we developed the GVs and ultrasound localization microscopy (ULM) based on singular value decomposition and 2D cross-correlation techniques. Furthermore, the SRUS imaging of the vessel-mimicking phantom with the GVs was performed. These results demonstrate that GVs could have potentials as novel contrast agents at nanoscale for implementing the SRUS imaging, thus indicating that ULM with GVs would be used for better visualization of micro-vasculature in vivo.
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Context-aware deep learning enables high-efficacy localization of high concentration microbubbles for super-resolution ultrasound localization microscopy
Abstract Ultrasound localization microscopy (ULM) enables deep tissue microvascular imaging by localizing and tracking intravenously injected microbubbles circulating in the bloodstream. However, conventional localization techniques require spatially isolated microbubbles, resulting in prolonged imaging time to obtain detailed microvascular maps. Here, we introduce LOcalization with Context Awareness (LOCA)-ULM, a deep learning-based microbubble simulation and localization pipeline designed to enhance localization performance in high microbubble concentrations. In silico, LOCA-ULM enhanced microbubble detection accuracy to 97.8% and reduced the missing rate to 23.8%, outperforming conventional and deep learning-based localization methods up to 17.4% in accuracy and 37.6% in missing rate reduction. In in vivo rat brain imaging, LOCA-ULM revealed dense cerebrovascular networks and spatially adjacent microvessels undetected by conventional ULM. We further demonstrate the superior localization performance of LOCA-ULM in functional ULM (fULM) where LOCA-ULM significantly increased the functional imaging sensitivity of fULM to hemodynamic responses invoked by whisker stimulations in the rat brain.
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- Award ID(s):
- 2523603
- PAR ID:
- 10499014
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 15
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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