Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157117
Title: Photoacoustic imaging aided with deep learning: a review
Authors: Rajendran, Praveenbalaji
Sharma, Arunima
Pramanik, Manojit
Keywords: Engineering::Bioengineering
Issue Date: 2022
Source: Rajendran, P., Sharma, A. & Pramanik, M. (2022). Photoacoustic imaging aided with deep learning: a review. Biomedical Engineering Letters, 12(2), 155-173. https://dx.doi.org/10.1007/s13534-021-00210-y
Project: RG144/18
RG127/19
Journal: Biomedical Engineering Letters
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.
URI: https://hdl.handle.net/10356/157117
ISSN: 2093-9868
DOI: 10.1007/s13534-021-00210-y
Rights: © 2021 Korean Society of Medical and Biological Engineering. All rights reserved. This paper was published in Biomedical Engineering Letters and is made available with permission of Korean Society of Medical and Biological Engineering.
Fulltext Permission: embargo_20230607
Fulltext Availability: With Fulltext
Appears in Collections:SCBE Journal Articles

Files in This Item:
File Description SizeFormat 
PA_DL_review_revison_final.pdf
  Until 2023-06-07
1.25 MBAdobe PDFUnder embargo until Jun 07, 2023

Page view(s)

13
Updated on May 20, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.