Pattern-learning-based Noise Elimination Algorithm in Photoacoustic Sensing and Imaging

As one of the fastest-growing imaging modalities in recent years, photoacoustic (PA) imaging has attracted tremendous research interest for various applications including anatomical, functional and molecular imaging. However, the PA signal's amplitude is usually quite weak and can be easily distorted by instrumental noise and interference, which can severely degrade the image quality. To improve the PA signal's signal-to-noise ratio efficiently, this paper introduces a pattern-learning based PA (PLPA) detection method to eliminate the periodically interference noise for PA sensing and imaging. Both simulation and experimental results are demonstrated to prove the validity of the proposed algorithm.