We replace part of a model-based iterative algorithm with a convolutional neural network in order to improve the quality of tomography reconstructions. We analyse its robustness against uncertainties in the image and uncertainties in system settings. Results are presented for the application of photoacoustic tomography in a limited angle setup.
|Title of host publication||89th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM)|
|Number of pages||2|
|Publication status||Published - 17 Dec 2018|
|Name||Proceedings in Applied Mathematics and Mechanics|
- inverse problems
- deep learning