ArticlePhotoacoustics2025
Lightweight sparse optoacoustic image reconstruction via an attention-driven multi-scale wavelet network.
Article in Photoacoustics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Model-Based Joint Feature Fusion framework for limited-view photoacoustic tomography reconstruction.Photoacoustics · 2026Article
- Volumetric photoacoustic/ultrasound image fusion and enhancement with dual-tree complex wavelet transform.Biomedical optics express · 2026Article
- Attention-driven complementary information fusion network for sparse photoacoustic image reconstruction.Photoacoustics · 2026Article
- Edge-Aware Short-Chain Diffusion Enables High-Fidelity Sparse-Sampling Optoacoustic Tomography.BME frontiers · 2026Article
- Multi-wavelength graph convolutional network for high-performance sparse multispectral optoacoustic tomography.Photoacoustics · 2025Article
- Wavelet-enhanced residual optimal transport for Mamba-based image restoration in photoacoustic tomography.Photoacoustics · 2025Article
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Authors and funding
6 authors.
Funding
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Abstract
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.
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