Photoacoustic image reconstruction via deep learning

Stephan Antholzer; Markus Haltmeier; Robert Nuster; Johannes Schwab

doi:10.1117/12.2290676

19 February 2018 Photoacoustic image reconstruction via deep learning

Stephan Antholzer, Markus Haltmeier, Robert Nuster, Johannes Schwab

Proceedings Volume 10494, Photons Plus Ultrasound: Imaging and Sensing 2018; 104944U (2018) https://doi.org/10.1117/12.2290676
Event: SPIE BiOS, 2018, San Francisco, California, United States

Abstract

Applying standard algorithms to sparse data problems in photoacoustic tomography (PAT) yields low-quality images containing severe under-sampling artifacts. To some extent, these artifacts can be reduced by iterative image reconstruction algorithms which allow to include prior knowledge such as smoothness, total variation (TV) or sparsity constraints. These algorithms tend to be time consuming as the forward and adjoint problems have to be solved repeatedly. Further, iterative algorithms have additional drawbacks. For example, the reconstruction quality strongly depends on a-priori model assumptions about the objects to be recovered, which are often not strictly satisfied in practical applications. To overcome these issues, in this paper, we develop direct and efficient reconstruction algorithms based on deep learning. As opposed to iterative algorithms, we apply a convolutional neural network, whose parameters are trained before the reconstruction process based on a set of training data. For actual image reconstruction, a single evaluation of the trained network yields the desired result. Our presented numerical results (using two different network architectures) demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative reconstruction methods.

Citation Download Citation

Stephan Antholzer, Markus Haltmeier, Robert Nuster, and Johannes Schwab "Photoacoustic image reconstruction via deep learning", Proc. SPIE 10494, Photons Plus Ultrasound: Imaging and Sensing 2018, 104944U (19 February 2018); https://doi.org/10.1117/12.2290676

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