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.
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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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