Paper Info
Title
Ki-GAN: Knowledge Infusion Generative Adversarial Network for Photoacoustic Image Reconstruction In Vivo
Abstract
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.
Year
DOI
Venue
2019
10.1007/978-3-030-32239-7_31
Lecture Notes in Computer Science
Keywords
DocType
Volume
Photoacoustic computed tomography,Generative adversarial network,Reconstruction,Knowledge infusion
Conference
11764
ISSN
Citations 
PageRank 
0302-9743
2
0.36
References 
Authors
0
7
Name
Order
Citations
PageRank
Hengrong Lan153.83
Kang Zhou2614.87
Changchun Yang321.38
Jun Cheng421420.65
Jiang Liu529942.50
Shenghua Gao6160766.89
Fei Gao7710.00