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Approximate k-space models and deep learning for fast photoacoustic reconstruction

Hauptmann, A; Cox, B; Lucka, F; Huynh, N; Betcke, M; Beard, P; Arridge, S; (2018) Approximate k-space models and deep learning for fast photoacoustic reconstruction. In: MLMIR 2018: Machine Learning for Medical Image Reconstruction. (pp. pp. 103-111). Springer: Cham, Switzerland. Green open access

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Abstract

We present a framework for accelerated iterative reconstructions using a fast and approximate forward model that is based on k-space methods for photoacoustic tomography. The approximate model introduces aliasing artefacts in the gradient information for the iterative reconstruction, but these artefacts are highly structured and we can train a CNN that can use the approximate information to perform an iterative reconstruction. We show feasibility of the method for human in-vivo measurements in a limited-view geometry. The proposed method is able to produce superior results to total variation reconstructions with a speed-up of 32 times.

Type: Proceedings paper
Title: Approximate k-space models and deep learning for fast photoacoustic reconstruction
Event: International Workshop on Machine Learning for Medical Image Reconstruction
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-00129-2_12
Publisher version: http://dx.doi.org/10.1007/978-3-030-00129-2_12
Language: English
Additional information: © 2018, Springer Nature Switzerland AG. This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Learned image reconstruction · Photoacoustic tomography · Fast Fourier methods · Compressed sensing
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/10058919
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