Ki-GAN: Knowledge infusion generative adversarial network for photoacoustic image reconstruction in vivo

Lan，Hengrong1; Zhou，Kang1,2; Yang，Changchun1; Cheng，Jun2; Liu，Jiang2,3; Gao，Shenghua1; Gao，Fei1

10.1007/978-3-030-32239-7_31

2019

0302-9743

1611-3349

LECT NOTES ARTIF INT

11764 LNCS

273-281

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

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.

Generative adversarial network Knowledge infusion Photoacoustic computed tomography Reconstruction

其他

英语

CPCI-S ; EI

Computer Science ; Engineering ; Microscopy ; Neurosciences & Neurology ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging

Computer Science, Artificial Intelligence ; Computer Science, Software Engineering ; Engineering, Biomedical ; Microscopy ; Neuroimaging ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging

WOS:000548734200031

20194807768342

Computerized tomography ; Ultrashort pulses ; Deep learning ; Medical imaging ; Generative adversarial networks ; Ultrasonic imaging ; Uncertainty analysis

Biomedical Engineering:461.1 ; Ergonomics and Human Factors Engineering:461.4 ; Artificial Intelligence:723.4 ; Computer Applications:723.5 ; Imaging Techniques:746 ; Probability Theory:922.1

2-s2.0-85075640123

Scopus

1.School of Information Science and Technology,ShanghaiTech University,Shanghai,201210,China
2.Cixi Institute of Biomedical Engineering,Chinese Academy of Sciences,Ningbo,315201,China
3.Department of Computer Science and Engineering,Southern University of Science and Technology,Guangdong,518055,China

Lan，Hengrong,Zhou，Kang,Yang，Changchun,et al. Ki-GAN: Knowledge infusion generative adversarial network for photoacoustic image reconstruction in vivo[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2019:273-281.