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Photoacoustic imaging aided with deep learning: a review

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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.

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Acknowledgements

The author would like to acknowledge the support by Tier 1 Grant funded by the Ministry of Education in Singapore (RG144/18, RG127/19).

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  1. Arunima Sharma

    Present address: Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA

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    Praveenbalaji Rajendran, Arunima Sharma & Manojit Pramanik

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Rajendran, P., Sharma, A. & Pramanik, M. Photoacoustic imaging aided with deep learning: a review. Biomed. Eng. Lett. 12, 155–173 (2022). https://doi.org/10.1007/s13534-021-00210-y

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