Deep Learning for Photoacoustic Tomography from Sparse Data

Understand

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 data.