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Photoacoustic imaging aided with deep learning: a review

Photoacoustic imaging aided with deep learning: a review Photoacoustic imaging (PAI) is an emerging hybrid imaging modality integrating the benefits of both optical and ultrasound imaging. Although PAI exhibits superior imaging capabilities, its translation into clinics is still hindered by various limitations. In recent years, deeplearning (DL), a new paradigm of machine learning, is gaining a lot of attention due to its ability to improve medical images. Likewise, DL is also widely being used in PAI to overcome some of the limitations of PAI. In this review, we provide a comprehensive overview on the various DL techniques employed in PAI along with its promising advantages. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biomedical Engineering Letters Springer Journals

Photoacoustic imaging aided with deep learning: a review

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References (187)

Publisher
Springer Journals
Copyright
Copyright © Korean Society of Medical and Biological Engineering 2021
ISSN
2093-9868
eISSN
2093-985X
DOI
10.1007/s13534-021-00210-y
Publisher site
See Article on Publisher Site

Abstract

Photoacoustic imaging (PAI) is an emerging hybrid imaging modality integrating the benefits of both optical and ultrasound imaging. Although PAI exhibits superior imaging capabilities, its translation into clinics is still hindered by various limitations. In recent years, deeplearning (DL), a new paradigm of machine learning, is gaining a lot of attention due to its ability to improve medical images. Likewise, DL is also widely being used in PAI to overcome some of the limitations of PAI. In this review, we provide a comprehensive overview on the various DL techniques employed in PAI along with its promising advantages.

Journal

Biomedical Engineering LettersSpringer Journals

Published: May 1, 2022

Keywords: Photoacoustic tomography; Photoacoustic microscopy; Machine learning; Deep learning; Convolutional neural network

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