77 citations · 298 across the 18 of their papers we have counts for
19 papers
Gradient-Based Geometry Learning for Fan-Beam CT Reconstruction
Mareike Thies, Fabian Wagner, Noah Maul +10
Incorporating computed tomography (CT) reconstruction operators into differentiable pipelines has proven beneficial in many applications. Such approaches usually focus on the proje…
Appearance Learning for Image-based Motion Estimation in Tomography
Alexander Preuhs, Michael Manhart, Philipp Roser +5
In tomographic imaging, anatomical structures are reconstructed by applying a pseudo-inverse forward model to acquired signals. Geometric information within this process is usually…
Data Consistent CT Reconstruction from Insufficient Data with Learned Prior Images
Yixing Huang, Alexander Preuhs, Michael Manhart +2
Image reconstruction from insufficient data is common in computed tomography (CT), e.g., image reconstruction from truncated data, limited-angle data and sparse-view data. Deep lea…
Deep autofocus with cone-beam CT consistency constraint
Alexander Preuhs, Michael Manhart, Philipp Roser +5
High quality reconstruction with interventional C-arm cone-beam computed tomography (CBCT) requires exact geometry information. If the geometry information is corrupted, e. g., by…
Image Quality Assessment for Rigid Motion Compensation
Alexander Preuhs, Michael Manhart, Philipp Roser +5
Diagnostic stroke imaging with C-arm cone-beam computed tomography (CBCT) enables reduction of time-to-therapy for endovascular procedures. However, the prolonged acquisition time…
Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior
Yixing Huang, Alexander Preuhs, Guenter Lauritsch +3
Robustness of deep learning methods for limited angle tomography is challenged by two major factors: a) due to insufficient training data the network may not generalize well to uns…
Maximum Likelihood Estimation of Head Motion using Epipolar Consistency
Alexander Preuhs, Nishant Ravikumar, Michael Manhart +5
Open gantry C-arm systems that are placed within the interventional room enable 3-D imaging and guidance for stroke therapy without patient transfer. This can profit in drastically…
Fast Epipolar Consistency without the Need for Pseudo Matrix Inverses
Alexander Preuhs, Michael Manhart, Andreas Maier
Interventional C-arm systems allow flexible 2-D imaging of a 3-D scene while being capable of cone beam computed tomography. Due to the flexible structure of the C-arm, the rotatio…
Numerical simulation of transport in porous media: some problems from micro to macro scale
Quanji Cai, Sheema Kooshapur, Michael Manhart +4
This paper deals with simulation of flow and transport in porous media such as transport of groundwater contaminants. We first discuss how macro scale equations are derived and whi…
Double Your Views - Exploiting Symmetry in Transmission Imaging
Alexander Preuhs, Andreas Maier, Michael Manhart +3
For a plane symmetric object we can find two views - mirrored at the plane of symmetry - that will yield the exact same image of that object. In consequence, having one image of a…
Growth tradeoffs produce complex microbial communities on a single limiting resource
Michael Manhart, Eugene I. Shakhnovich
The relationship between the dynamics of a community and its constituent pairwise interactions is a fundamental problem in ecology. Higher-order ecological effects beyond pairwise…
Pore-scale lattice Boltzmann simulation of laminar and turbulent flow through a sphere pack
Ehsan Fattahia, Christian Waluga, Barbara Wohlmuth +3
The lattice Boltzmann method can be used to simulate flow through porous media with full geometrical resolution. With such a direct numerical simulation, it becomes possible to stu…
Path statistics, memory, and coarse-graining of continuous-time random walks on networks
Michael Manhart, Willow Kion-Crosby, Alexandre V. Morozov
Continuous-time random walks (CTRWs) on discrete state spaces, ranging from regular lattices to complex networks, are ubiquitous across physics, chemistry, and biology. Models with…
Scaling properties of evolutionary paths in a biophysical model of protein adaptation
Michael Manhart, Alexandre V. Morozov
The enormous size and complexity of genotypic sequence space frequently requires consideration of coarse-grained sequences in empirical models. We develop scaling relations to quan…
Protein folding and binding can emerge as evolutionary spandrels through structural coupling
Michael Manhart, Alexandre V. Morozov
Binding interactions between proteins and other molecules mediate numerous cellular processes, including metabolism, signaling, and regulation of gene expression. These interaction…
Biophysical Fitness Landscapes for Transcription Factor Binding Sites
Allan Haldane, Michael Manhart, Alexandre V. Morozov
Evolutionary trajectories and phenotypic states available to cell populations are ultimately dictated by intermolecular interactions between DNA, RNA, proteins, and other molecular…
Statistical Physics of Evolutionary Trajectories on Fitness Landscapes
Michael Manhart, Alexandre V. Morozov
Random walks on multidimensional nonlinear landscapes are of interest in many areas of science and engineering. In particular, properties of adaptive trajectories on fitness landsc…
A path-based approach to random walks on networks characterizes how proteins evolve new function
Michael Manhart, Alexandre V. Morozov
We develop a path-based approach to continuous-time random walks on networks with arbitrarily weighted edges. We describe an efficient numerical algorithm for calculating statistic…
A priori and a posteriori analysis of models for Large-Eddy simulation of particle-laden flow
Ch. Gobert, M. Manhart
In Large-Eddy simulation of particle-laden flow, the effect of the unresolved scales on the particles needs to be modelled. In this work we analyse three very promising models, nam…