Validation of NSSA-Based Classification of Microbubble Signals Using Differential Targeted Enhancement

Previous experiments from our laboratory suggested that normalized singular spectrum area (NSSA), a statistical property of data, can be used to differentiate between fast-moving and slow-moving signal. We showed qualitative evidence that signals from free-flowing microbubbles (MBs) exhibit higher NSSA values, static tissue signals exhibit lower NSSA values, and adherent MB signals exhibit intermediate NSSA values. In this study, we seek to validate the correlation between MB adherence and NSSA value. To achieve this, we combined NSSA measurements with differential targeted enhancement imaging, which is the current “gold standard” for preclinical measurements of MB adherence.