Microbubble signal classification using NSSA-based filtering methods

Microbubbles (MBs) are capable of binding specifically to molecular markers on the vascular endothelium to enable sensitive detection of early stage disease. Traditional microbubble imaging techniques rely on the nonlinearity of microbubble signal to differentiate it from surrounding tissue. However, the presence of harmonic energy among echogenic tissue interfaces can limit the effectiveness of these methods in enhancing microbubble contrast. In many cases, it is challenging to achieve any quantitative separation between static tissue signal and bound MB signal (Fig. 1B). In this study, we use normalized singular spectrum area (NSSA) to differentiate between static tissue signal, bound MB signal, and free MB signal in a mouse hindlimb tumor.