k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields

TLDR

k‑Wave is designed to make realistic photoacoustic modeling simple and fast. The paper introduces k‑Wave, a freely available MATLAB toolbox for simulating and reconstructing photoacoustic wave fields. k‑Wave implements k‑space pseudo‑spectral time‑domain simulations of coupled acoustic equations in 1–3 D, offers flexible time‑reversal reconstruction for arbitrary detector geometries, and provides a one‑step FFT‑based reconstruction for planar detectors. The toolbox’s architecture is illustrated with novel modeling examples, showing that data interpolation improves sparse‑array time‑reversal reconstructions, that time‑reversal works for finite planar detectors as well as one‑step FFT, and that GPU parallel execution substantially speeds up computations.

Abstract

A new, freely available third party MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields is described. The toolbox, named k-Wave, is designed to make realistic photoacoustic modeling simple and fast. The forward simulations are based on a k-space pseudo-spectral time domain solution to coupled first-order acoustic equations for homogeneous or heterogeneous media in one, two, and three dimensions. The simulation functions can additionally be used as a flexible time reversal image reconstruction algorithm for an arbitrarily shaped measurement surface. A one-step image reconstruction algorithm for a planar detector geometry based on the fast Fourier transform (FFT) is also included. The architecture and use of the toolbox are described, and several novel modeling examples are given. First, the use of data interpolation is shown to considerably improve time reversal reconstructions when the measurement surface has only a sparse array of detector points. Second, by comparison with one-step, FFT-based reconstruction, time reversal is shown to be sufficiently general that it can also be used for finite-sized planar measurement surfaces. Last, the optimization of computational speed is demonstrated through parallel execution using a graphics processing unit.