Gradient-based adaptive wavelet de-noising method for photoacoustic imaging in vivo

TL;DR

This paper introduces a gradient-based adaptive wavelet de-noising method that significantly improves photoacoustic image quality and SNR in vivo, enabling better biomedical imaging with lower laser power.

Contribution

The paper presents a novel de-noising algorithm that adaptively sets thresholds based on energy gradient mutation points, enhancing photoacoustic image quality.

Findings

De-noising quality improved by 20%-40% over traditional methods.

Enhanced contrast and detail clarity in PA images.

Potential for in vivo biomedical imaging with lower laser power.

Abstract

Photoacoustic imaging (PAI) has been applied to many biomedical applications over the past decades. However, the received PA signal usually suffers from poor signal-to-noise ratio (SNR). Conventional solution of employing higher-power laser, or doing long-time signal averaging, may raise the system cost, time consumption, and tissue damage. Another strategy is de-noising algorithm design. In this paper, we propose a new de-noising method, termed gradient-based adaptive wavelet de-noising, which sets the energy gradient mutation point of low-frequency wavelet components as the threshold. We conducted simulation, ex vivo and in vivo experiments to validate the performance of the algorithm. The quality of de-noised PA image/signal by our proposed algorithm has improved by 20%-40%, in comparison to the traditional signal denoising algorithms, which produces better contrast and clearer details. The proposed de-noising method provides potential to improve the SNR of PA signal under single-shot low-power laser illumination for biomedical applications in vivo.