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Toward accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in three dimensions

Bench, C; Hauptmann, A; Cox, B; (2020) Toward accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in three dimensions. Journal of Biomedical Optics , 25 (8) , Article 085003. 10.1117/1.JBO.25.8.085003. Green open access

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Abstract

Significance: Two-dimensional (2-D) fully convolutional neural networks have been shown capable of producing maps of sO2 from 2-D simulated images of simple tissue models. However, their potential to produce accurate estimates in vivo is uncertain as they are limited by the 2-D nature of the training data when the problem is inherently three-dimensional (3-D), and they have not been tested with realistic images. Aim: To demonstrate the capability of deep neural networks to process whole 3-D images and output 3-D maps of vascular sO2 from realistic tissue models/images. Approach: Two separate fully convolutional neural networks were trained to produce 3-D maps of vascular blood oxygen saturation and vessel positions from multiwavelength simulated images of tissue models. Results: The mean of the absolute difference between the true mean vessel sO2 and the network output for 40 examples was 4.4% and the standard deviation was 4.5%. Conclusions: 3-D fully convolutional networks were shown capable of producing accurate sO2 maps using the full extent of spatial information contained within 3-D images generated under conditions mimicking real imaging scenarios. We demonstrate that networks can cope with some of the confounding effects present in real images such as limited-view artifacts and have the potential to produce accurate estimates in vivo.

Type: Article
Title: Toward accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in three dimensions
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1117/1.JBO.25.8.085003
Publisher version: https://doi.org/10.1117/1.JBO.25.8.085003
Language: English
Additional information: Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI
Keywords: photoacoustics; deep learning; oxygen saturation; sO2; machine learning; quantitative photoacoustics.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10108541
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