147 citations · 418 across the 25 of their papers we have counts for
29 papers
Data-driven Modeling in Metrology -- A Short Introduction, Current Developments and Future Perspectives
Linda-Sophie Schneider, Patrick Krauss, Nadine Schiering +3
Mathematical models are vital to the field of metrology, playing a key role in the derivation of measurement results and the calculation of uncertainties from measurement data, inf…
The Artificial Neural Twin -- Process Optimization and Continual Learning in Distributed Process Chains
Johannes Emmert, Ronald Mendez, Houman Mirzaalian Dastjerdi +2
Industrial process optimization and control is crucial to increase economic and ecologic efficiency. However, data sovereignty, differing goals, or the required expert knowledge fo…
Task-based Generation of Optimized Projection Sets using Differentiable Ranking
Linda-Sophie Schneider, Mareike Thies, Christopher Syben +3
We present a method for selecting valuable projections in computed tomography (CT) scans to enhance image reconstruction and diagnosis. The approach integrates two important factor…
Optimizing CT Scan Geometries With and Without Gradients
Mareike Thies, Fabian Wagner, Noah Maul +4
In computed tomography (CT), the projection geometry used for data acquisition needs to be known precisely to obtain a clear reconstructed image. Rigid patient motion is a cause fo…
Geometric Constraints Enable Self-Supervised Sinogram Inpainting in Sparse-View Tomography
Fabian Wagner, Mareike Thies, Noah Maul +5
The diagnostic quality of computed tomography (CT) scans is usually restricted by the induced patient dose, scan speed, and image quality. Sparse-angle tomographic scans reduce rad…
Deep Learning-based Patient Re-identification Is able to Exploit the Biometric Nature of Medical Chest X-ray Data
Kai Packhäuser, Sebastian Gündel, Nicolas Münster +3
With the rise and ever-increasing potential of deep learning techniques in recent years, publicly available medical datasets became a key factor to enable reproducible development…
X-ray Scatter Estimation Using Deep Splines
Philipp Roser, Annette Birkhold, Alexander Preuhs +7
Algorithmic X-ray scatter compensation is a desirable technique in flat-panel X-ray imaging and cone-beam computed tomography. State-of-the-art U-net based image translation approa…
Reconstruction of Voxels with Position- and Angle-Dependent Weightings
Lina Felsner, Tobias Würfl, Christopher Syben +4
The reconstruction problem of voxels with individual weightings can be modeled a position- and angle- dependent function in the forward-projection. This changes the system matrix a…
Cephalogram Synthesis and Landmark Detection in Dental Cone-Beam CT Systems
Yixing Huang, Fuxin Fan, Christopher Syben +3
Due to the lack of standardized 3D cephalometric analytic methodology, 2D cephalograms synthesized from 3D cone-beam computed tomography (CBCT) volumes are widely used for cephalom…
Simultaneous Estimation of X-ray Back-Scatter and Forward-Scatter using Multi-Task Learning
Philipp Roser, Xia Zhong, Annette Birkhold +7
Scattered radiation is a major concern impacting X-ray image-guided procedures in two ways. First, back-scatter significantly contributes to patient (skin) dose during complicated…
Fully-automatic CT data preparation for interventional X-ray skin dose simulation
Philipp Roser, Annette Birkhold, Alexander Preuhs +6
Recently, deep learning (DL) found its way to interventional X-ray skin dose estimation. While its performance was found to be acceptable, even more accurate results could be achie…
Deep Scatter Splines: Learning-Based Medical X-ray Scatter Estimation Using B-splines
Philipp Roser, Annette Birkhold, Alexander Preuhs +5
The idea of replacing hardware by software to compensate for scattered radiation in flat-panel X-ray imaging is well established in the literature. Recently, deep-learningbased ima…
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…
Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-beam CT
Jennifer Maier, Luis Carlos Rivera Monroy, Christopher Syben +7
Analyzing knee cartilage thickness and strain under load can help to further the understanding of the effects of diseases like Osteoarthritis. A precise segmentation of the cartila…
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…
Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging
Bernhard Stimpel, Christopher Syben, Tobias Würfl +4
Hybrid X-ray and magnetic resonance (MR) imaging promises large potential in interventional medical imaging applications due to the broad variety of contrast of MRI combined with f…
Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing
Bernhard Stimpel, Christopher Syben, Franziska Schirrmacher +3
Deep learning-based image processing is capable of creating highly appealing results. However, it is still widely considered as a "blackbox" transformation. In medical imaging, thi…
Magnetic Resonance Fingerprinting Reconstruction Using Recurrent Neural Networks
Elisabeth Hoppe, Florian Thamm, Gregor Körzdörfer +6
Magnetic Resonance Fingerprinting (MRF) is an imaging technique acquiring unique time signals for different tissues. Although the acquisition is highly accelerated, the reconstruct…
RinQ Fingerprinting: Recurrence-informed Quantile Networks for Magnetic Resonance Fingerprinting
Elisabeth Hoppe, Florian Thamm, Gregor Körzdörfer +6
Recently, Magnetic Resonance Fingerprinting (MRF) was proposed as a quantitative imaging technique for the simultaneous acquisition of tissue parameters such as relaxation times $T…
Learning with Known Operators reduces Maximum Training Error Bounds
Andreas K. Maier, Christopher Syben, Bernhard Stimpel +7
We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain op…
PYRO-NN: Python Reconstruction Operators in Neural Networks
Christopher Syben, Markus Michen, Bernhard Stimpel +3
Purpose: Recently, several attempts were conducted to transfer deep learning to medical image reconstruction. An increasingly number of publications follow the concept of embedding…
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…
A Gentle Introduction to Deep Learning in Medical Image Processing
Andreas Maier, Christopher Syben, Tobias Lasser +1
This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reaso…
User Loss -- A Forced-Choice-Inspired Approach to Train Neural Networks directly by User Interaction
Shahab Zarei, Bernhard Stimpel, Christopher Syben +1
In this paper, we investigate whether is it possible to train a neural network directly from user inputs. We consider this approach to be highly relevant for applications in which…