Current Awardee

NCSA AI and Compute for Super-Resolution Ultrasound Advancement

Pengfei Song
Pengfei Song

College: Grainger College of Engineering
Award year: 2021-2022
NCSA collaborators: Peter Groves, Colleen Bushell

Super-resolution ultrasound localization microscopy (ULM) has great potential as a medical imaging technology due to its unique combination of imaging penetration and spatial resolution. Currently, capturing an image using ULM takes a prohibitive amount of time, and consequently the technology has not been deployed in a clinical setting. To address this problem, the team is exploring the use of deep learning to more efficiently utilize the microbubble signal to shorten the data acquisition time and post-processing. However, deploying deep learning for microbubble signal processing in ULM requires a large amount of labeled data for neural network training, which is difficult to accomplish in an experimental setting. Relying on synthesized data is a viable alternative solution for DL but it requires in vivo microvascular graph models to generate realistic ultrasound simulation data for contrast-enhanced blood flow. Such a database of in vivo microvascular graph models does not currently exist, which is what the team is proposing to develop in this project. Successful completion of this project will produce a large set of labeled micro vessel data that can be shared with the ULM research community to facilitate the development of fast ULM techniques to ultimately achieve the clinical translation of ULM.