Compensating for visibility artefacts in photoacoustic imaging with a deep learning approach providing prediction uncertainties

Compensating for visibility artefacts in photoacoustic imaging with a deep learning approach providing prediction uncertainties

Conventional photoacoustic imaging may suffer from the limited view and bandwidth of ultrasound transducers. A deep learning approach is proposed to handle these problems and is demonstrated both in simulations and in experiments on a multi-scale model of leaf skeleton. We employed an experimental a...

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Bibliographic Details
Main Authors: Guillaume Godefroy, Bastien Arnal, Emmanuel Bossy
Format: Article
Language:English
Published: Elsevier 2021-03-01
Series:Photoacoustics
Subjects:
Photoacoustic imaging
Deep learning
Visibility artefacts
Monte Carlo dropout
Bayesian neural network
Online Access:http://www.sciencedirect.com/science/article/pii/S2213597920300574

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http://www.sciencedirect.com/science/article/pii/S2213597920300574

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