2018-09-12
Approximate k-space models and deep learning for fast photoacoustic reconstruction
Publication
Publication
Presented at the
International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR (September 2018), Granada, Spain
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.
| Additional Metadata | |
|---|---|
| , , , | |
| doi.org/10.1007/978-3-030-00129-2_12 | |
| Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence | |
| International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR | |
| Organisation | Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands |
|
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 Machine Learning for Medical Image Reconstruction (pp. 103–111). doi:10.1007/978-3-030-00129-2_12 |
|
