Photoacoustic imaging aided with deep learning: a review

Praveenbalaji Rajendran; Arunima Sharma; Manojit Pramanik

doi:10.1007/s13534-021-00210-y

Photoacoustic imaging aided with deep learning: a review

Biomed Eng Lett. 2021 Nov 23;12(2):155-173. doi: 10.1007/s13534-021-00210-y. eCollection 2022 May.

Authors

Praveenbalaji Rajendran¹, Arunima Sharma^{1

2}, Manojit Pramanik¹

Affiliations

¹ School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore, 637459 Singapore.
² Present Address: Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD USA.

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.

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

Publication types

Review