  Photoacoustic (PA) computed tomography (PACT) reconstructs the initial
pressure distribution from raw PA signals. The standard reconstruction of
medical 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. Most works remove the artifacts from image domain, and
compensate the limited-view from dataset. 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. Specifically, our results could generate superior performance,
whose artifacts are drastically reduced in the output compared to ground-truth
(full-view reconstructed result). In this paper, 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 numerical and in-vivo results
have demonstrated the superior performance of our method to reconstruct the
full-view image without artifacts. Finally, quantitative evaluations show that
our proposed method outperformed the ground-truth in some metrics.
