Better Than Ground-truth? Beyond Supervised Learning for Photoacoustic Imaging Reconstruction

• URL: http://arxiv.org/abs/2012.02472v2
• Date: Mon, 21 Dec 2020 01:54:11 GMT
• Title: Better Than Ground-truth? Beyond Supervised Learning for Photoacoustic Imaging Reconstruction
• Authors: Hengrong Lan, Changchun Yang, Feng Gao, and Fei Gao
• Abstract summary: Photoacoustic computed tomography (PACT) reconstructs the initial pressure distribution from raw PA signals. Recently, supervised deep learning has been used to overcome limited-view problem that requires ground-truth. We propose a beyond supervised reconstruction framework (BSR-Net) based on deep learning to compensate the limited-view issue.
• Score: 4.748104083612737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Photoacoustic computed tomography (PACT) reconstructs the initial pressure distribution from raw PA signals. Standard reconstruction always induces artifacts using limited-view signals, which are influenced by limited angle coverage of transducers, finite bandwidth, and uncertain heterogeneous biological tissue. Recently, supervised deep learning has been used to overcome limited-view problem that requires ground-truth. However, even full-view sampling still induces artifacts that cannot be used to train the model. It causes a dilemma that we could not acquire perfect ground-truth in practice. To reduce the dependence on the quality of ground-truth, in this paper, for the first time, we propose a beyond supervised reconstruction framework (BSR-Net) based on deep learning to compensate the limited-view issue by feeding limited-view position-wise data. A quarter position-wise data is fed into model and outputs a group full-view data. Specifically, our method introduces a residual structure, which generates beyond supervised reconstruction result, whose artifacts are drastically reduced in the output compared to ground-truth. Moreover, two novel losses are designed to restrain the artifacts. The numerical and in-vivo results have demonstrated the performance of our method to reconstruct the full-view image without artifacts.

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