A jointed feature fusion framework for photoacoustic image reconstruction

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). More-over, 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.

Automated summary

In this paper, a jointed feature fusion framework (JEFF-Net) based on deep learning is proposed to reconstruct the PA image using limited-view data. Cross-domain features from limited-view position-wise data and the reconstructed image are fused by backtracked supervision to restrain the artifacts. Experimental results demonstrate superior performance with a 135% improvement in SSIM for simulation and a 40% improvement in gCNR for in-vivo cases compared to the ground-truth.