Image-Based PSF Estimation for Ultrasound Training Simulation

A key aspect for virtual-reality based ultrasound training is the plausible simulation of the characteristic noise pattern known as ultrasonic speckle. The formation of ultrasonic speckle can be approximated efficiently by convolving the ultrasound point-spread function (PSF) with a distribution of point scatterers. Recent work extracts the latter directly from ultrasound images for use in forward simulation, assuming that the PSF can be known, e.g., from experiments. In this paper, we investigate the problem of automatically estimating an unknown PSF for the purpose of ultrasound simulation, such as to use in convolutionbased ultrasound image formation. Our method estimates the PSF directly from an ultrasound image, based on homomorphic filtering in the cepstrum domain. It robustly captures local changes in the PSF as a function of depth, and hence is able to reproduce continuous ultrasound beam profiles. We compare our method to numerical simulations as the ground truth to study PSF estimation accuracy, achieving small approximation errors of ≤ 15% FWHM. We also demonstrate simulated in-vivo images, with beam profiles estimated from real images.