Pan, Bolin;
Betcke, Marta M;
(2023)
On Learning the Invisible in Photoacoustic Tomography with Flat Directionally Sensitive Detector.
SIAM Journal on Imaging Sciences
, 16
(2)
pp. 770-801.
10.1137/22M148793X.
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Abstract
In photoacoustic tomography (PAT) with a flat sensor, we routinely encounter two types of limited data. The first is due to using a finite sensor and is especially perceptible if the region of interest is large relative to the sensor or located farther away from the sensor. In this paper, we focus on the second type caused by a varying sensitivity of the sensor to the incoming wavefront direction, which can be modelled as binary, i.e., by a cone of sensitivity. Such visibility conditions result, in the Fourier domain, in a restriction of both the image and the data to a bowtie, akin to the one corresponding to the range of the forward operator. The visible wavefrontsets in image and data domains, are related by the wavefront direction mapping. We adapt the wedge restricted curvelet decomposition, we previously proposed for the representation of the full PAT data, to separate the visible and invisible wavefronts in the image. We optimally combine fast approximate operators with tailored deep neural network architectures into efficient learned reconstruction methods which perform reconstruction of the visible coefficients, and the invisible coefficients are learned from a training set of similar data.
Type: | Article |
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Title: | On Learning the Invisible in Photoacoustic Tomography with Flat Directionally Sensitive Detector |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1137/22M148793X |
Publisher version: | https://doi.org/10.1137/22M148793X |
Language: | English |
Additional information: | © The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/ |
Keywords: | learned image reconstruction, compressed sensing, curvelet transform, photoacoustic tomography, fast Fourier methods, limited view |
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 |
URI: | https://discovery.ucl.ac.uk/id/eprint/10165514 |
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