Felix Lucka

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: This study suggests that improvements in the effects of transcranial direct current stimulation are achievable and introduces new methods to evaluate the effectivity in the target area specifically, where it needs higher input currents than cerebral stimulation does.

TL;DR: In this article, a 3D (2D plus time) CNN architecture was developed and trained using synthetic training data created from previously acquired breath hold cine images from 250 CHD patients.

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: 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.