Speaker
Description
The forward and adjoint problems in photoacoustic tomography can be modelled as an initial value and a time varying source problem for the free space wave equation. Despite the advances made in recent years (parallel interrogation with up to 64 beams), the data acquisition time in state-of-the-art PAT scanners is still a bottle-neck resulting in sparse, limited angle data. The solution of inverse problems with incomplete data necessitates iterative methods involving repeated calls to the forward and adjoint solvers, which is the most compute intensive part of the process. Inspired by the Multiscale Gaussian Beam decomposition proposed by Qian and Ying (Multiscale Modeling & Simulation, 8 (2010), pp. 1803–1837), we devise an efficient hybrid wave solver, leveraging Gaussian Beams for efficient and highly parallel propagation of high frequency components of the solution, and a pseudo-spectral method for accurate propagation of the low frequency components. We discuss the accuracy and performance of our method on an example of the forward problem in PAT.
