Superposed Wave (s-Wave): Accelerating Photoacoustic Simulation

TL;DR

This paper introduces s-Wave, a simple and efficient method for photoacoustic simulation that significantly reduces computation time compared to traditional methods like k-Wave, especially in 3D sparse cases.

Contribution

The paper proposes a novel superposed Wave (s-Wave) approach that accelerates photoacoustic simulation by using precomputed sensor data and phase/amplitude adjustments, reducing computation time.

Findings

s-Wave is over 2000 times faster than k-Wave in 3D sparse configurations.

s-Wave achieves nearly identical sensor data to k-Wave with much less computation.

The method is validated on multiple 2D and 3D samples.

Abstract

Photoacoustic imaging develops very fast in recent years due to its superior performance in many preclinical and clinical applications. However, it is still in a developing stage, and a lot of experiments have to be performed in a simulation setting. To simulate photoacoustic imaging in a computer, k-Wave is currently the most popular MATLAB toolbox. Lots of research groups choose k-Wave toolbox to perform the forward projection process, which also can be described as forward model. However, by solving complex partial differential equation, k-Wave suffers a lot from computation time. To accelerate photoacoustic simulation, in this paper, we propose a straightforward simulation approach based on superposed Wave (s-Wave). Specifically, we treat the initial pressure distribution as a set of single pixels. Then by pre-obtaining a standard sensor data from single pixel, we can easily use loop and multiplication operators to change phase and amplitude of sensor data for given pixels. We use three different 2D samples and two 3D samples to test the time cost. The result of our proposed s-Wave method shows much less time consumption compared with k-wave. Especially in a sparse configuration in 3D, s-Wave is more than 2000 times faster than k-Wave, whiling getting nearly same sensor data.