Deep Learning for Photoacoustic Tomography from Sparse Data

@article{Antholzer2017DeepLF,
  title={Deep Learning for Photoacoustic Tomography from Sparse Data},
  author={Stephan Antholzer and Markus Haltmeier and Johannes Schwab},
  journal={CoRR},
  year={2017},
  volume={abs/1704.04587}
}
The development of fast and accurate image reconstruction algorithms is a central aspect of computed tomography. In this paper, we investigate this issue for the sparse data problem in photoacoustic tomography (PAT). We develop a direct and highly efficient reconstruction algorithm based on deep learning. In our approach image reconstruction is performed with a deep convolutional neural network (CNN), whose weights are adjusted prior to the actual image reconstruction based on a set of training… CONTINUE READING
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