Literature DB >> 35529338

Photoacoustic imaging aided with deep learning: a review.

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


  127 in total

1.  Backward-mode multiwavelength photoacoustic scanner using a planar Fabry-Perot polymer film ultrasound sensor for high-resolution three-dimensional imaging of biological tissues.

Authors:  Edward Zhang; Jan Laufer; Paul Beard
Journal:  Appl Opt       Date:  2008-02-01       Impact factor: 1.980

2.  Least squares QR-based decomposition provides an efficient way of computing optimal regularization parameter in photoacoustic tomography.

Authors:  Calvin B Shaw; Jaya Prakash; Manojit Pramanik; Phaneendra K Yalavarthy
Journal:  J Biomed Opt       Date:  2013-08       Impact factor: 3.170

Review 3.  Optoacoustic image formation approaches-a clinical perspective.

Authors:  Xosé Luís Deán-Ben; Daniel Razansky
Journal:  Phys Med Biol       Date:  2019-09-19       Impact factor: 3.609

4.  Enabling fast and high quality LED photoacoustic imaging: a recurrent neural networks based approach.

Authors:  Emran Mohammad Abu Anas; Haichong K Zhang; Jin Kang; Emad Boctor
Journal:  Biomed Opt Express       Date:  2018-07-25       Impact factor: 3.732

5.  A Novel 2-D Synthetic Aperture Focusing Technique for Acoustic-Resolution Photoacoustic Microscopy.

Authors:  Seungwan Jeon; Jihoon Park; Ravi Managuli; Chulhong Kim
Journal:  IEEE Trans Med Imaging       Date:  2018-07-31       Impact factor: 10.048

6.  A low memory cost model based reconstruction algorithm exploiting translational symmetry for photoacustic microscopy.

Authors:  Juan Aguirre; Alexia Giannoula; Taisuke Minagawa; Lutz Funk; Pau Turon; Turgut Durduran
Journal:  Biomed Opt Express       Date:  2013-11-12       Impact factor: 3.732

7.  Vascular tree extraction for photoacoustic microscopy and imaging of cat primary visual cortex.

Authors:  Qian Li; Lin Li; Tianhao Yu; Qingliang Zhao; Chuanqing Zhou; Xinyu Chai
Journal:  J Biophotonics       Date:  2016-08-22       Impact factor: 3.207

8.  Vector extrapolation methods for accelerating iterative reconstruction methods in limited-data photoacoustic tomography.

Authors:  Navchetan Awasthi; Sandeep Kumar Kalva; Manojit Pramanik; Phaneendra K Yalavarthy
Journal:  J Biomed Opt       Date:  2018-02       Impact factor: 3.170

9.  Deep Learning-Based Spectral Unmixing for Optoacoustic Imaging of Tissue Oxygen Saturation.

Authors:  Ivan Olefir; Stratis Tzoumas; Courtney Restivo; Pouyan Mohajerani; Lei Xing; Vasilis Ntziachristos
Journal:  IEEE Trans Med Imaging       Date:  2020-10-28       Impact factor: 10.048

10.  Deep image prior for undersampling high-speed photoacoustic microscopy.

Authors:  Tri Vu; Anthony DiSpirito; Daiwei Li; Zixuan Wang; Xiaoyi Zhu; Maomao Chen; Laiming Jiang; Dong Zhang; Jianwen Luo; Yu Shrike Zhang; Qifa Zhou; Roarke Horstmeyer; Junjie Yao
Journal:  Photoacoustics       Date:  2021-03-31
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  1 in total

1.  Deep-Learning-Based Algorithm for the Removal of Electromagnetic Interference Noise in Photoacoustic Endoscopic Image Processing.

Authors:  Oleksandra Gulenko; Hyunmo Yang; KiSik Kim; Jin Young Youm; Minjae Kim; Yunho Kim; Woonggyu Jung; Joon-Mo Yang
Journal:  Sensors (Basel)       Date:  2022-05-23       Impact factor: 3.847

  1 in total

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