Context-aware deep learning enables high-efficacy localization of high concentration microbubbles for super-resolution ultrasound localization microscopy

YiRang Shin, Matthew R. Lowerison, Yike Wang, Xi Chen, Qi You, Zhijie Dong, Mark A. Anastasio and Pengfei Song ()
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YiRang Shin: University of Illinois Urbana–Champaign
Matthew R. Lowerison: University of Illinois Urbana–Champaign
Yike Wang: University of Illinois Urbana–Champaign
Xi Chen: University of Illinois Urbana–Champaign
Qi You: University of Illinois Urbana–Champaign
Zhijie Dong: University of Illinois Urbana–Champaign
Mark A. Anastasio: University of Illinois Urbana–Champaign
Pengfei Song: University of Illinois Urbana–Champaign

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-47154-2

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