Researchers at the Beckman Institute for Advanced Science and Technology have developed a new technique to make ultrasound localization microscopy (ULM), an emerging diagnostic tool for high-resolution microvascular imaging, faster and easier to use. Their method, called Localization with Context Awareness Ultrasound Localization microscopy (LOCA-ULM), uses deep learning to improve the post-processing pipeline of ULM.
"I'm really excited about making ULM faster and better so that more people will be able to use this technology," said YiRang Shin, first author of the paper published in Nature Communications. "I think deep learning-based computational imaging tools will continue to play a major role in pushing the spatial and temporal resolution limits of ULM."
ULM works by injecting microbubbles into blood vessels, which act as contrast agents. Ultrasound waves can then pinpoint the location of these microbubbles as they travel through the bloodstream, allowing researchers to track blood flow speed and create spatial images of blood vessels at the microscale. However, the current imaging speed of ULM has limited its practical application as a diagnostic and research tool.
The researchers' new method demonstrates higher imaging performance and processing speed, increased sensitivity for functional ULM, and overall superior in vivo imaging. It also shows improved computational and microbubble localization performance and is adaptable to different microbubble concentrations.
"It really beats conventional microbubble localization methods; this is the way to go," said Pengfei Song, senior author of the study.