A physics inspired and efficient transform for optoacoustic systems

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.