A jointed feature fusion framework for photoacoustic image reconstruction

Hengrong Lan; Changchun Yang; Fei Gao

doi:10.1016/j.pacs.2022.100442

A jointed feature fusion framework for photoacoustic image reconstruction

Photoacoustics. 2022 Dec 20:29:100442. doi: 10.1016/j.pacs.2022.100442. eCollection 2023 Feb.

Authors

Hengrong Lan^{1

2}, Changchun Yang¹, Fei Gao^{1

3}

Affiliations

¹ Hybrid Imaging System Laboratory, Shanghai Engineering Research Center of Intelligent Vision and Imaging, School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China.
² Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China.
³ Shanghai Clinical Research and Trial Center, Shanghai 201210, China.

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.

Keywords: Convolutional neural network; Deep learning; Photoacoustic tomography; Reconstruction.