Correcting visibility artefacts in photoacoustic imaging with a deep learning approach

Guillaume Godefroy; Bastien Arnal; Emmanuel Bossy

doi:10.1117/12.2615876

9 December 2021 Correcting visibility artefacts in photoacoustic imaging with a deep learning approach

Guillaume Godefroy, Bastien Arnal, Emmanuel Bossy

Proceedings Volume 11923, Opto-Acoustic Methods and Applications in Biophotonics V; 119230D (2021) https://doi.org/10.1117/12.2615876
Event: European Conferences on Biomedical Optics, 2021, Online Only

Abstract

In conventional photoacoustics (PA) imaging, the finite size and limited-bandwidth of ultrasound transducers often lead to visibility artifacts resulting in a degraded image quality. We propose a reconstruction algorithm based on deep learning to address theses issues. An in vitro vasculature mimicking model has been used in order to show the capability of a conventional neural network to remove these artefacts in an experimental configuration. The deep learning algorithm is trained using couples of PA images and ground truth photographs. The uncertainty of the model prediction is estimated through the Monte Carlo dropout method allowing the display of a pixel-wise degree of confidence. Finally, the interest of using simulation data through transfer learning in order to reduce the size of the experimental dataset is investigated.

Citation Download Citation

Guillaume Godefroy, Bastien Arnal, and Emmanuel Bossy "Correcting visibility artefacts in photoacoustic imaging with a deep learning approach", Proc. SPIE 11923, Opto-Acoustic Methods and Applications in Biophotonics V, 119230D (9 December 2021); https://doi.org/10.1117/12.2615876

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KEYWORDS

Monte Carlo methods

Simulation of CCA and DLA aggregates

Data modeling

Photography

Visibility

Image quality

Computer simulations

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