Predictive Models of Plane-wave-based Point Spread Function Angulation

Ultrasound localization microscopy (ULM) methods ascribe precise spatial coordinates to individual circulating microbubbles whose trajectories are superimposed to trace out a super-resolution image of the vasculature. Conventional localization algorithms use data from coarsely-pixelated B-mode images. In prior work, a novel localization algorithm was developed based on raw complex RF data from point spread functions (PSFs) obtained using a multi-angle plane wave acquisition sequence and four parallel receive apodizations. We seek to characterize point spread functions over a broad FOV. Based on these data, we propose models to predict PSF angulation within well-defined support regions as a function of position, plane-wave transmit angle, and aperture size. With these models, we aim to extend the support for a localization algorithm valid over the full FOV.