Marta M. Betcke

TL;DR: A deep neural network is presented that is specifically designed to provide high resolution 3-D images from restricted photoacoustic measurements to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artifacts.

TL;DR: The results show that images with good spatial resolution and contrast can be obtained from highly sub-sampled PAT data if variational image reconstruction techniques that describe the tissues structures with suitable sparsity-constraints are used.

TL;DR: Two modifications of total variation are discussed that take structural a priori knowledge into account and reduce to total variation in the degenerate case when no structural knowledge is available and exploiting the two dimensional directional information results in images with well defined edges.

TL;DR: In this paper, a simple mathematical derivation of the adjoint of the photoacoustic tomography (PAT) forward operator in the continuous framework is presented, and an efficient numerical implementation of this adjoint using a k-space time domain wave propagation model is described and illustrated in the context of variational PAT image reconstruction, on both 2D and 3D examples including inhomogeneous sound speed.

TL;DR: In this article, a deep neural network is designed to provide high resolution 3D images from restricted photoacoustic measurements, and the resulting network is trained and tested on a set of segmented vessels from lung CT scans and then applied to in-vivo photo-acoustic measurement data.