A physics inspired and efficient transform for optoacoustic systems

TL;DR

This paper introduces a physics-inspired transform for optoacoustic systems that improves processing speed and accuracy over traditional Fourier-based methods, potentially advancing clinical and home-care imaging.

Contribution

A novel transform based on optoacoustic physics that reduces computational complexity and enhances signal-processing accuracy compared to the DFT.

Findings

Outperforms DFT in computational efficiency

Achieves higher accuracy in signal reconstruction

Validated on both simulations and experimental data

Abstract

Optoacoustic imaging technologies require fast and accurate signal pre-processing algorithms to enable widespread deployment in clinical and home-care settings. However, they still rely on the Discrete Fourier Transform (DFT) as the default tool for essential signal-conditioning operations, which imposes hard limits on both execution speed and signal-retrieval accuracy. Here, we present a new transform whose building blocks are directly inspired by the physics of optoacoustic signal generation. We compared its performance with the DFT and other classical transforms on common signal-processing tasks using both simulations and experimental datasets. Our results indicate that the proposed transform not only sets a new lower bound on computational complexity relative to the DFT, but also substantially outperforms classical transforms on basic signal-processing operations in terms of accuracy. We expect this transform to catalyze broader adoption of optoacoustic methods.