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Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography
UCL, Dept Comp Sci, London WC1 6BT, England..
UCL, Dept Comp Sci, London WC1 6BT, England.;Ctr Wiskunde & Informat, NL-1098 XG Amsterdam, Netherlands..ORCID iD: 0000-0002-8763-5177
UCL, Dept Comp Sci, London WC1 6BT, England..
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2018 (English)In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 37, no 6, p. 1382-1393Article in journal (Refereed) Published
Abstract [en]

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 paper, we present a deep neural network that is specifically designed to provide high resolution 3-D 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 artifacts. 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 computed tomography scans and then applied to in-vivo photoacoustic measurement data.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2018. Vol. 37, no 6, p. 1382-1393
Keywords [en]
Deep learning, convolutional neural networks, photoacoustic tomography, iterative reconstruction
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:kth:diva-231207DOI: 10.1109/TMI.2018.2820382ISI: 000434302700009PubMedID: 29870367Scopus ID: 2-s2.0-8504473367OAI: oai:DiVA.org:kth-231207DiVA, id: diva2:1229072
Note

QC 20180629

Available from: 2018-06-29 Created: 2018-06-29 Last updated: 2018-06-29Bibliographically approved

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Adler, Jonas

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