Ki-GAN: Knowledge Infusion Generative Adversarial Network for Photoacoustic Image Reconstruction In Vivo

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11764)


Photoacoustic computed tomography (PACT) breaks through the depth restriction in optical imaging, and the contrast restriction in ultrasound imaging, which is achieved by receiving thermoelastically induced ultrasound signal triggered by an ultrashort laser pulse. The photoacoustic (PA) images reconstructed from the raw PA signals usually utilize conventional reconstruction algorithms, e.g. filtered back-projection. However, the performance of conventional reconstruction algorithms is usually limited by complex and uncertain physical parameters due to heterogeneous tissue structure. In recent years, deep learning has emerged to show great potential in the reconstruction problem. In this work, for the first time to our best knowledge, we propose to infuse the classical signal processing and certified knowledge into the deep learning for PA imaging reconstruction. Specifically, we make these contributions to propose a novel Knowledge Infusion Generative Adversarial Network (Ki-GAN) architecture that combines conventional delay-and-sum algorithm to reconstruct PA image. We train the network on a public clinical database. Our method shows better image reconstruction performance in cases of both full-sampled data and sparse-sampled data compared with state-of-the-art methods. Lastly, our proposed approach also shows high potential for other imaging modalities beyond PACT.


Photoacoustic computed tomography Generative adversarial network Reconstruction Knowledge infusion 

Supplementary material

486666_1_En_31_MOESM1_ESM.pdf (1.9 mb)
Supplementary material 1 (PDF 1942 kb)


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Information Science and TechnologyShanghaiTech UniversityShanghaiChina
  2. 2.Cixi Institute of Biomedical EngineeringChinese Academy of SciencesNingboChina
  3. 3.Department of Computer Science and EngineeringSouthern University of Science and TechnologyGuangdongChina

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