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Model based learning for accelerated, limited-view 3D photoacoustic tomography

Hauptmann, A; Lucka, F; Betcke, M; Huynh, N; Adler, J; Cox, B; Beard, P; ... Arridge, S; + view all (2018) Model based learning for accelerated, limited-view 3D photoacoustic tomography. IEEE Transactions on Medical Imaging , 37 (6) pp. 1382-1393. 10.1109/TMI.2018.2820382. Green open access

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Abstract

OAPA Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed-up. In this work we present a deep neural network that is specifically designed to provide high resolution 3D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artefacts. Due to the high complexity of the photoacoustic forward operator, we separate training and computation of the gradient information. A suitable prior for the desired image structures is learned as part of the training. The resulting network is trained and tested on a set of segmented vessels from lung CT scans and then applied to in-vivo photoacoustic measurement data.

Type: Article
Title: Model based learning for accelerated, limited-view 3D photoacoustic tomography
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TMI.2018.2820382
Publisher version: http://dx.doi.org/10.1109/TMI.2018.2820382
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Image reconstruction, Tomography, TV, Three-dimensional displays, Machine learning, Propagation, Computational modeling Deep learning, convolutional neural networks, photoacoustic tomography, iterative reconstruction
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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/1572851
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