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.
Preview |
Text
Hauptmann_Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography_VoR.pdf Download (1MB) | Preview |
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 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 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
