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Conference Paper: Ki-GAN: Knowledge infusion generative adversarial network for photoacoustic image reconstruction in vivo
Title | Ki-GAN: Knowledge infusion generative adversarial network for photoacoustic image reconstruction in vivo |
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Authors | |
Keywords | Generative adversarial network Knowledge infusion Photoacoustic computed tomography Reconstruction |
Issue Date | 2019 |
Citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, v. 11764 LNCS, p. 273-281 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/344995 |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
DC Field | Value | Language |
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dc.contributor.author | Lan, Hengrong | - |
dc.contributor.author | Zhou, Kang | - |
dc.contributor.author | Yang, Changchun | - |
dc.contributor.author | Cheng, Jun | - |
dc.contributor.author | Liu, Jiang | - |
dc.contributor.author | Gao, Shenghua | - |
dc.contributor.author | Gao, Fei | - |
dc.date.accessioned | 2024-08-15T09:24:33Z | - |
dc.date.available | 2024-08-15T09:24:33Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, v. 11764 LNCS, p. 273-281 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/344995 | - |
dc.description.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. | - |
dc.language | eng | - |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.subject | Generative adversarial network | - |
dc.subject | Knowledge infusion | - |
dc.subject | Photoacoustic computed tomography | - |
dc.subject | Reconstruction | - |
dc.title | Ki-GAN: Knowledge infusion generative adversarial network for photoacoustic image reconstruction in vivo | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-030-32239-7_31 | - |
dc.identifier.scopus | eid_2-s2.0-85075640123 | - |
dc.identifier.volume | 11764 LNCS | - |
dc.identifier.spage | 273 | - |
dc.identifier.epage | 281 | - |
dc.identifier.eissn | 1611-3349 | - |