Lightweight sparse optoacoustic image reconstruction via an attention-driven multi-scale wavelet network.

Photoacoustic tomography (PAT) provides high-contrast, high-resolution biomedical images at rapid speeds. However, the quality of these images is highly sensitive to sampling density. Sparse sampling can significantly reduce equipment costs but often leads to image artifacts and degraded quality. While deep learning models have greatly enhanced sparse PAT imaging, their high computational requirements limit their use in resource-constrained environments. To overcome this challenge, we propose AD-WaveNet, a lightweight network that integrates the Discrete 2D Wavelet Transform (DWT) with adaptive attention mechanisms. This approach enhances sparse image reconstruction while reducing computational complexity. The attention mechanisms are specifically designed to exploit the multi-scale decomposition properties of DWT, allowing the model to emphasize key features across various scales. Compared to the latest models, AD-WaveNet reduces computational complexity and parameter count by nearly two orders of magnitude, while maintaining optimal reconstruction quality. This demonstrates AD-WaveNet's significant potential for practical applications in PAT imaging.