180 citations · 1.3k across the 95 of their papers we have counts for
9 papers · 2 filters
Photoacoustic Tomography with Direction Dependent Data: An Exact Series Reconstruction Approach
Gerhard Zangerl, Sunghwan Moon, Markus Haltmeier
Photoacoustic image reconstruction often assumes that the restriction of the acoustic pressure on the detection surface is given. However, commonly used detectors often have a cert…
Big in Japan: Regularizing networks for solving inverse problems
Johannes Schwab, Stephan Antholzer, Markus Haltmeier
Deep learning and (deep) neural networks are emerging tools to address inverse problems and image reconstruction tasks. Despite outstanding performance, the mathematical analysis f…
Variational regularization of the weighted conical Radon transform
Markus Haltmeier, Daniela Schiefeneder
Recovering a function from integrals over conical surfaces recently got significant interest. It is relevant for emission tomography with Compton cameras and other imaging applicat…
Full field inversion in photoacoustic tomography with variable sound speed
Gerhard Zangerl, Markus Haltmeier, Linh V. Nguyen +1
Recently, a novel measurement setup has been introduced to photoacoustic tomography, that collects data in the form of projections of the full 3D acoustic pressure distribution at…
Deep Null Space Learning for Inverse Problems: Convergence Analysis and Rates
Johannes Schwab, Stephan Antholzer, Markus Haltmeier
Recently, deep learning based methods appeared as a new paradigm for solving inverse problems. These methods empirically show excellent performance but lack of theoretical justific…
Douglas-Rachford Algorithm for Magnetorelaxometry Imaging using Random and Deterministic Activations
Markus Haltmeier, Gerhard Zangerl, Peter Schier +1
Magnetorelaxometry imaging is a novel tool for quantitative determination of the spatial distribution of magnetic nanoparticle inside an organism. The use of multiple excitation pa…
NETT: Solving Inverse Problems with Deep Neural Networks
Housen Li, Johannes Schwab, Stephan Antholzer +1
Recovering a function or high-dimensional parameter vector from indirect measurements is a central task in various scientific areas. Several methods for solving such inverse proble…
Stochastic Proximal Gradient Algorithms for Multi-Source Quantitative Photoacoustic Tomography
Simon Rabanser, Lukas Neumann, Markus Haltmeier
The development of accurate and efficient image reconstruction algorithms is a central aspect of quantitative photoacoustic tomography (QPAT). In this paper, we address this issues…
Real-time photoacoustic projection imaging using deep learning
Johannes Schwab, Stephan Antholzer, Robert Nuster +1
Photoacoustic tomography (PAT) is an emerging and non-invasive hybrid imaging modality for visualizing light absorbing structures in biological tissue. The recently invented PAT sy…