Signal Detection of Point Targets Using Eigen-Images for Super-Resolution Ultrasound Imaging and Gas Vesicle...

Accurate signal detection of ultrasound contrast agents, such as microbubble (MB) and gas vesicle (GV), in the presence of clutter and noise is essential to increase image quality in super-resolution ultrasound imaging (SRUS) and achieve precise GV localization. We developed and evaluated an eigen-image based signal detection method using singular value decomposition (SVD) and changepoint detection to automatically segment the data that are closely related to physical events such as MB flow and GV collapse. Eigen-image based method was compared with the elbow point and hard thresholding method when selecting MB signals after SVD of raw data, acquired from phantom and in vivo experiments. Image reconstructed by eigen-image based method was also compared with unregistered difference image for GV localization when moving GVs in a phantom were collapsed by ultrafast plane waves. The eigen-image based MB signal detection method resulted in higher vessel density (VD) visualization in both the phantom and in vivo mouse tumor. It also achieved increased signal-to-noise ratio (SNR) in both cases. Moreover, this method localized moving GVs more efficiently than the difference imaging method, without requiring pixel registration based on landmarks. The eigen-image based method offers a reliable and automated approach to MB and GV signal detection for both SRUS and point target localization. This approach is a valuable tool for medical imaging providing high-quality vessel images along with accurate locations of moving ultrasound contrast agents, which can be potentially translatable to clinical diagnosis and pre-clinical research.