Deep Learning Enhanced Volumetric Photoacoustic Imaging of Vasculature in Human

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Abstract: The development of high‐performance imaging processing algorithms is a core area of photoacoustic tomography. While various deep learning based image processing techniques have been developed in the area, their applications in 3D imaging are still limited due to challenges in computational cost and memory allocation. To address those limitations, this work implements a 3D fully‐dense (3DFD) U‐net to linear array based photoacoustic tomography and utilizes volumetric simulation and mixed precision training to increase efficiency and training size. Through numerical simulation, phantom imaging, and in vivo experiments, this work demonstrates that the trained network restores the true object size, reduces the noise level and artifacts, improves the contrast at deep regions, and reveals vessels subject to limited view distortion. With these enhancements, 3DFD U‐net successfully produces clear 3D vascular images of the palm, arms, breasts, and feet of human subjects. These enhanced vascular images offer improved capabilities for biometric identification, foot ulcer evaluation, and breast cancer imaging. These results indicate that the new algorithm will have a significant impact on preclinical and clinical photoacoustic tomography.

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: Deep Learning Enhanced Volumetric Photoacoustic Imaging of Vasculature in Human ; day:02 ; month:08 ; year:2023 ; extent:12
Advanced science ; (02.08.2023) (gesamt 12)

Creator: Zheng, Wenhan
Zhang, Huijuan
Huang, Chuqin
Shijo, Varun
Xu, Chenhan
Xu, Wenyao
Xia, Jun

DOI: 10.1002/advs.202301277

URN: urn:nbn:de:101:1-2023080215213288524963

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:55 AM CEST

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Zheng, Wenhan
Zhang, Huijuan
Huang, Chuqin
Shijo, Varun
Xu, Chenhan
Xu, Wenyao
Xia, Jun

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