Deep learning for super-resolution vascular ultrasound imaging

Based on the intravascular infusion of gas microbubbles, which act as ultrasound contrast agents, ultrasound localization microscopy has enabled super resolution vascular imaging through precise detection of individual microbubbles across numerous imaging frames. However, analysis of high-density regions with significant overlaps among the microbubble point spread functions typically yields high localization errors, constraining the technique to low-concentration conditions. As such, long acquisition times are required for sufficient coverage of the vascular bed. Algorithms based on sparse recovery have been developed specifically to cope with the overlapping point-spread-functions of multiple microbubbles. While successful localization of densely-spaced emitters has been demonstrated, even highly optimized fast sparse recovery techniques involve a time-consuming iterative procedure. In this work, we used deep learning to improve upon standard ultrasound localization microscopy (Deep-ULM), and obtain super-resolution vascular images from high-density contrast-enhanced ultrasound data. Deep-ULM is suitable for real-time applications, resolving about 1250 high-resolution patches (128×128 pixels) per second using GPU acceleration.