Statistical BURST imaging for high-fidelity biomolecular ultrasound

Ultrasound is emerging as a method for molecular and cellular imaging by connecting the versatile physics of sound waves to protein-based contrast agents such as gas vesicles (GVs). BURST is a common imaging mode that leverages the strong, transient echoes generated when GVs collapse under acoustic pressure to enable highly sensitive ultrasound visualization of cells and biomolecules, down to the single cell level. However, BURST is vulnerable to fluctuating background signals, with large-amplitude fluctuations in scattering, as often present <i>in vivo</i> , obscuring genuine GV responses. In this study, we mathematically examine this limitation and show that incorporating statistical metrics such as correlation or temporal contrast-to-noise ratio effectively suppresses unwanted non-GV voxels and quantifies detection confidence, including in image sequences in which GV collapse spans multiple frames. Compared with prior methods, our approach enhances the clarity of BURST images and provides probabilistic interpretations of GV signals, facilitating more reliable analysis of ambiguous <i>in vivo</i> molecular imaging, as we demonstrate in imaging tumor-homing probiotics and gene expression in the brain.