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Deep Learning Enhanced Volumetric Photoacoustic Imaging of Vasculature in Human
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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582405/ https://www.ncbi.nlm.nih.gov/pubmed/37530209 http://dx.doi.org/10.1002/advs.202301277 |
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author | Zheng, Wenhan Zhang, Huijuan Huang, Chuqin Shijo, Varun Xu, Chenhan Xu, Wenyao Xia, Jun |
author_facet | Zheng, Wenhan Zhang, Huijuan Huang, Chuqin Shijo, Varun Xu, Chenhan Xu, Wenyao Xia, Jun |
author_sort | Zheng, Wenhan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10582405 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105824052023-10-19 Deep Learning Enhanced Volumetric Photoacoustic Imaging of Vasculature in Human Zheng, Wenhan Zhang, Huijuan Huang, Chuqin Shijo, Varun Xu, Chenhan Xu, Wenyao Xia, Jun Adv Sci (Weinh) Research Articles 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. John Wiley and Sons Inc. 2023-08-02 /pmc/articles/PMC10582405/ /pubmed/37530209 http://dx.doi.org/10.1002/advs.202301277 Text en © 2023 The Authors. Advanced Science published by Wiley‐VCH GmbH https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Zheng, Wenhan Zhang, Huijuan Huang, Chuqin Shijo, Varun Xu, Chenhan Xu, Wenyao Xia, Jun Deep Learning Enhanced Volumetric Photoacoustic Imaging of Vasculature in Human |
title | Deep Learning Enhanced Volumetric Photoacoustic Imaging of Vasculature in Human |
title_full | Deep Learning Enhanced Volumetric Photoacoustic Imaging of Vasculature in Human |
title_fullStr | Deep Learning Enhanced Volumetric Photoacoustic Imaging of Vasculature in Human |
title_full_unstemmed | Deep Learning Enhanced Volumetric Photoacoustic Imaging of Vasculature in Human |
title_short | Deep Learning Enhanced Volumetric Photoacoustic Imaging of Vasculature in Human |
title_sort | deep learning enhanced volumetric photoacoustic imaging of vasculature in human |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582405/ https://www.ncbi.nlm.nih.gov/pubmed/37530209 http://dx.doi.org/10.1002/advs.202301277 |
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