| Literature DB >> 35529338 |
Praveenbalaji Rajendran1, Arunima Sharma1,2, Manojit Pramanik1.
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. © Korean Society of Medical and Biological Engineering 2021.Entities:
Keywords: Convolutional neural network; Deep learning; Machine learning; Photoacoustic microscopy; Photoacoustic tomography
Year: 2021 PMID: 35529338 PMCID: PMC9046497 DOI: 10.1007/s13534-021-00210-y
Source DB: PubMed Journal: Biomed Eng Lett ISSN: 2093-9868