Dynamic Filtering of Adherent and Non-adherent Microbubble Signals Using Singular Value Thresholding and Normalized Singular Spectrum Area Techniques

Ultrasound molecular imaging techniques rely on the separation and identification of three types of signals: static tissue, adherent microbubbles and non-adherent microbubbles. In this study, the image filtering techniques of singular value thresholding (SVT) and normalized singular spectrum area (NSSA) were combined to isolate and identify vascular endothelial growth factor receptor 2-targeted microbubbles in a mouse hindlimb tumor model (n = 24). By use of a Verasonics Vantage 256 imaging system with an L12-5 transducer, a custom-programmed pulse inversion sequence employing synthetic aperture virtual source element imaging was used to collect contrast images of mouse tumors perfused with microbubbles. SVT was used to suppress static tissue signals by 9.6 dB while retaining adherent and non-adherent microbubble signals. NSSA was used to classify microbubble signals as adherent or non-adherent with high accuracy (receiver operating characteristic area under the curve [ROC AUC] = 0.97), matching the classification performance of differential targeted enhancement. The combined SVT + NSSA filtering method also outperformed differential targeted enhancement in differentiating MB signals from all other signals (ROC AUC = 0.89) without necessitating destruction of the contrast agent. The results from this study indicate that SVT and NSSA can be used to automatically segment and classify contrast signals. This filtering method with potential real-time capability could be used in future diagnostic settings to improve workflow and speed the clinical uptake of ultrasound molecular imaging techniques.