PAM, not spam: Towards quantitative, reproducible, and energy-preserving cavitation imaging

Abstract

Since the development of the original passive acoustic mapping (PAM) algorithm for cavitation imaging in 2008, which employed a conventional beamformer adapted from seismology (time exposure acoustics), sequential algorithmic improvements have sought to enhance spatial resolution, reduce computation time and enable increasingly setup-independent reporting of cavitation activity. These enhancements have included the use of frequency-domain rather than time-domain implementations to improve computational efficiency, the introduction of the Robust Capon Beamformer to reduce artefacts, sub-aperture processing to further increase frame rate and the implementation of corrections for frequency-dependent attenuation and for array sensitivity and diffraction to account for probe- and depth-related variability. Once these corrections are taken into account, current PAM algorithms produce accurate estimates of cavitation source strength at a single location, but not in the surrounding region, leading to inaccurate estimates of the total cavitation energy over the treatment volume. We present an energy-preserving and computationally efficient PAM algorithm based on Lucy-Richardson deconvolution, that enables the calculation of a set-up independent Cavitational Radiated Energy Density (CRED) that ensures cavitation images accurately reflect the total energy of radiated acoustic emissions within the imaging domain.