Learning iterative reconstruction for high resolution photoacoustic tomography

In this talk I will give an introduction to photoacoustic tomography, its mathematical background and applications. Then I will concentrate on recent work on learned iterative reconstructions, where we train a deep neural network that to provide high resolution 3D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artefacts. Due to the high complexity of the photoacoustic forward operator, we separate training and computation of the gradient information. A suitable prior for the desired image structures is learned as part of the training. The resulting network is trained and tested on a set of segmented vessels from lung CT scans and then applied to human in-vivo photoacoustic measurement data.