Advancing High-resolution Ultrasound Imaging with Deep Learning
A new technique using deep learning to advance the post-processing pipeline of ultrasound localization microscopy (ULM) makes it more practical for clinical settings.
Researchers at the Beckman Institute for Advanced Science and Technology developed a technique called Localization with Context Awareness Ultrasound Localization Microscopy, or LOCA-ULM. Their work appears in the journal Nature Communications.
By making ULM faster and better, more people will use the technology. Deep learning-based computational imaging tools will push ULM’s spatial and temporal resolution limits. Ultrasound localization microscopy works by injecting microbubbles into blood vessels, where they act as contrast agents. Ultrasound waves can penetrate deep tissues in the body, pinpointing the location of these microbubbles, each only several microns in size, as they travel through the bloodstream. The microbubbles track blood flow speed and create spatial images of blood vessels at the microscale.
The current imaging speed of ULM limits its practical application as a diagnostic tool. Increasing imaging speed requires a higher concentration of microbubbles in the bloodstream, making post-processing much more difficult. In comparison, the new method demonstrates higher imaging performance and processing speed, increased sensitivity for functional ULM, and overall superior in vivo imaging. It also demonstrates improved computational and microbubble localization performance.
To make microbubble localization faster, more accurate, and more efficient, the team developed a simulation model based on a generative adversarial network called GAN, creating realistic microbubble signals to train the deep context-aware neural network DECODE.
