Algorithm

Set (abstract data type)

Iterative reconstruction

Reconstruction algorithm

Computer science

Tomography

Physics

Pattern recognition (psychology)

Optics

Image (mathematics)

Data set

Artificial intelligence

Photoacoustic tomography

Programming language

Deep learning

Artificial neural network

Convolutional neural network

Computer vision

Inverse Problems in Science and Engineering

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. The proposed reconstruction approach can be interpreted as a network that uses the PAT filtered backprojection algorithm for the first layer, followed by the U-net architecture for the remaining layers. Actual image reconstruction with deep learning consists in one evaluation of the trained CNN, which does not require time-consuming solution of the forward and adjoint problems. At the same time, our numerical results demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative approaches for PAT from sparse data.

Deep learning for photoacoustic tomography from sparse data