ArticlePhotoacoustics2023
A jointed feature fusion framework for photoacoustic image reconstruction.
Article in Photoacoustics, 2023. 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
- Progressive dual-branch adversarial diffusion for sparse-view photoacoustic tomography reconstruction.Photoacoustics · 2026Article
- Advances in photoacoustic imaging reconstruction and quantitative analysis for biomedical applications.Visual computing for industry, biomedicine, and art · 2026Review
- Attention-driven complementary information fusion network for sparse photoacoustic image reconstruction.Photoacoustics · 2026Article
- Structure and oxygen saturation recovery of sparse photoacoustic microscopy images by deep learning.Photoacoustics · 2025Article
- Fisheye piezo polymer detector for scanning optoacoustic angiography of experimental neoplasms.Photoacoustics · 2023Article
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Authors and funding
3 authors.
Funding
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Abstract
The standard reconstruction of Photoacoustic (PA) computed tomography (PACT) image could cause the artifacts due to interferences or ill-posed setup. Recently, deep learning has been used to reconstruct the PA image with ill-posed conditions. In this paper, we propose a jointed feature fusion framework (JEFF-Net) based on deep learning to reconstruct the PA image using limited-view data. The cross-domain features from limited-view position-wise data and the reconstructed image are fused by a backtracked supervision. A quarter position-wise data (32 channels) is fed into model, which outputs another 3-quarters-view data (96 channels). Moreover, two novel losses are designed to restrain the artifacts by sufficiently manipulating superposed data. The experimental results have demonstrated the superior performance and quantitative evaluations show that our proposed method outperformed the ground-truth in some metrics by 135% (SSIM for simulation) and 40% (gCNR for in-vivo) improvement.
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