11 citations · 25 across the 20 of their papers we have counts for
22 papers
Faber-Krahn inequality for the first Dirichlet eigenvalues of the logarithmic Hardy operator
Ying Wang, Tao Zhang, Wanwan Wang
The purpose of this paper is to study Faber--Krahn inequality of Dirichlet eigenvalues for the logarithmic Hardy operator \[ \mathcal{L}_μ=(-Δ)^{\…
GB-SVFBP: Gaussian-Based Shift-Variant FBP neural network
Chengze Ye, Linda-Sophie Schneider, Yipeng Sun +1
This paper proposes a Gaussian-Based Shift-Variant filtered backprojection (FBP) neural network, which is designed for the efficient reconstruction of non-circular trajectory cone…
Robustness and Stability Analysis of Differentiable Shift-Variant FBP for Cone-Beam CT under Challenging Acquisition Settings
Chengze Ye, Linda-Sophie Schneider, Yipeng Sun +5
The differentiable shift-variant filtered backprojection (SV-FBP) framework enables data-driven estimation of redundancy weights for cone-beam CT reconstruction under general sourc…
Soft Tuy-Completeness for Robust Projection Selection in Cone-Beam CT
Linda-Sophie Schneider, Andreas Maier
This work introduces a continuous soft near-orthogonality score and a resolution-aware saturated coverage objective for projection selection in region-of-interest focused cone-beam…
ICDAR 2026 Competition on Writer Identification and Pen Classification from Hand-Drawn Circles
Thomas Gorges, Janne van der Loop, Lukas Hüttner +4
This paper presents CircleID, a large-scale ICDAR 2026 competition on writer identification and pen classification from scanned hand-drawn circles. The primary objective is to inve…
An update to PYRO-NN: A Python Library for Differentiable CT Operators
Linda-Sophie Schneider, Yipeng Sun, Chengze Ye +2
Deep learning has brought significant advancements to X-ray Computed Tomography (CT) reconstruction, offering solutions to challenges arising from modern imaging technologies. Thes…
Learning Wavelet-Sparse FDK for 3D Cone-Beam CT Reconstruction
Yipeng Sun, Linda-Sophie Schneider, Chengze Ye +4
Cone-Beam Computed Tomography (CBCT) is essential in medical imaging, and the Feldkamp-Davis-Kress (FDK) algorithm is a popular choice for reconstruction due to its efficiency. How…
Filter2Noise: A Framework for Interpretable and Zero-Shot Low-Dose CT Image Denoising
Yipeng Sun, Linda-Sophie Schneider, Siyuan Mei +8
Noise in low-dose computed tomography (LDCT) can obscure important diagnostic details. While deep learning offers powerful denoising, supervised methods require impractical paired…
Compressibility Analysis for the differentiable shift-variant Filtered Backprojection Model
Chengze Ye, Linda-Sophie Schneider, Yipeng Sun +2
The differentiable shift-variant filtered backprojection (FBP) model enables the reconstruction of cone-beam computed tomography (CBCT) data for any non-circular trajectories. This…
DRACO: Differentiable Reconstruction for Arbitrary CBCT Orbits
Chengze Ye, Linda-Sophie Schneider, Yipeng Sun +3
This paper introduces a novel method for reconstructing cone beam computed tomography (CBCT) images for arbitrary orbits using a differentiable shift-variant filtered backprojectio…
Modelling of measuring systems -- From white box models to cognitive approaches
Nadine Schiering, Sascha Eichstaedt, Michael Heizmann +4
Mathematical models of measuring systems and processes play an essential role in metrology and practical measurements. They form the basis for understanding and evaluating measurem…
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…
Application of Gated Recurrent Units for CT Trajectory Optimization
Yuedong Yuan, Linda-Sophie Schneider, Andreas Maier
Recent advances in computed tomography (CT) imaging, especially with dual-robot systems, have introduced new challenges for scan trajectory optimization. This paper presents a nove…
EAGLE: An Edge-Aware Gradient Localization Enhanced Loss for CT Image Reconstruction
Yipeng Sun, Yixing Huang, Linda-Sophie Schneider +5
Computed Tomography (CT) image reconstruction is crucial for accurate diagnosis and deep learning approaches have demonstrated significant potential in improving reconstruction qua…
Deep Learning Computed Tomography based on the Defrise and Clack Algorithm
Chengze Ye, Linda-Sophie Schneider, Yipeng Sun +1
This study presents a novel approach for reconstructing cone beam computed tomography (CBCT) for specific orbits using known operator learning. Unlike traditional methods, this tec…
Integer Optimization of CT Trajectories using a Discrete Data Completeness Formulation
Linda-Sophie Schneider, Gabriel Herl, Andreas Maier
X-ray computed tomography (CT) plays a key role in digitizing three-dimensional structures for a wide range of medical and industrial applications. Traditional CT systems often rel…
A 2D Sinogram-Based Approach to Defect Localization in Computed Tomography
Yuzhong Zhou, Linda-Sophie Schneider, Fuxin Fan +1
The rise of deep learning has introduced a transformative era in the field of image processing, particularly in the context of computed tomography. Deep learning has made a signifi…
Data-Driven Filter Design in FBP: Transforming CT Reconstruction with Trainable Fourier Series
Yipeng Sun, Linda-Sophie Schneider, Fuxin Fan +6
In this study, we introduce a Fourier series-based trainable filter for computed tomography (CT) reconstruction within the filtered backprojection (FBP) framework. This method over…
A gradient-based approach to fast and accurate head motion compensation in cone-beam CT
Mareike Thies, Fabian Wagner, Noah Maul +9
Cone-beam computed tomography (CBCT) systems, with their flexibility, present a promising avenue for direct point-of-care medical imaging, particularly in critical scenarios such a…
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…
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…