30 citations · 96 across the 28 of their papers we have counts for
28 papers
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…
FIND-Net -- Fourier-Integrated Network with Dictionary Kernels for Metal Artifact Reduction
Farid Tasharofi, Fuxin Fan, Melika Qahqaie +2
Metal artifacts, caused by high-density metallic implants in computed tomography (CT) imaging, severely degrade image quality, complicating diagnosis and treatment planning. While…
CyclePose -- Leveraging Cycle-Consistency for Annotation-Free Nuclei Segmentation in Fluorescence Microscopy
Jonas Utz, Stefan Vocht, Anne Tjorven Buessen +6
In recent years, numerous neural network architectures specifically designed for the instance segmentation of nuclei in microscopic images have been released. These models embed nu…
DiffRenderGAN: Addressing Training Data Scarcity in Deep Segmentation Networks for Quantitative Nanomaterial Analysis through Differentiable Rendering and Generative Modelling
Dennis Possart, Leonid Mill, Florian Vollnhals +11
Nanomaterials exhibit distinctive properties governed by parameters such as size, shape, and surface characteristics, which critically influence their applications and interactions…
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…
On the Influence of Smoothness Constraints in Computed Tomography Motion Compensation
Mareike Thies, Fabian Wagner, Noah Maul +6
Computed tomography (CT) relies on precise patient immobilization during image acquisition. Nevertheless, motion artifacts in the reconstructed images can persist. Motion compensat…
Differentiable Score-Based Likelihoods: Learning CT Motion Compensation From Clean Images
Mareike Thies, Noah Maul, Siyuan Mei +8
Motion artifacts can compromise the diagnostic value of computed tomography (CT) images. Motion correction approaches require a per-scan estimation of patient-specific motion patte…
Segmentation-Guided Knee Radiograph Generation using Conditional Diffusion Models
Siyuan Mei, Fuxin Fan, Fabian Wagner +4
Deep learning-based medical image processing algorithms require representative data during development. In particular, surgical data might be difficult to obtain, and high-quality…
BigReg: An Efficient Registration Pipeline for High-Resolution X-Ray and Light-Sheet Fluorescence Microscopy
Siyuan Mei, Fuxin Fan, Mareike Thies +9
Recently, X-ray microscopy (XRM) and light-sheet fluorescence microscopy (LSFM) have emerged as pivotal tools in preclinical research, particularly for studying bone remodeling dis…
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…
Physics-Informed Learning for Time-Resolved Angiographic Contrast Agent Concentration Reconstruction
Noah Maul, Annette Birkhold, Fabian Wagner +5
Three-dimensional Digital Subtraction Angiography (3D-DSA) is a well-established X-ray-based technique for visualizing vascular anatomy. Recently, four-dimensional DSA (4D-DSA) rec…
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…
Focus on Content not Noise: Improving Image Generation for Nuclei Segmentation by Suppressing Steganography in CycleGAN
Jonas Utz, Tobias Weise, Maja Schlereth +5
Annotating nuclei in microscopy images for the training of neural networks is a laborious task that requires expert knowledge and suffers from inter- and intra-rater variability, e…
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…
Exploring Epipolar Consistency Conditions for Rigid Motion Compensation in In-vivo X-ray Microscopy
Mareike Thies, Fabian Wagner, Mingxuan Gu +10
Intravital X-ray microscopy (XRM) in preclinical mouse models is of vital importance for the identification of microscopic structural pathological changes in the bone which are cha…
Transient Hemodynamics Prediction Using an Efficient Octree-Based Deep Learning Model
Noah Maul, Katharina Zinn, Fabian Wagner +6
Patient-specific hemodynamics assessment could support diagnosis and treatment of neurovascular diseases. Currently, conventional medical imaging modalities are not able to accurat…
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…
Noise2Contrast: Multi-Contrast Fusion Enables Self-Supervised Tomographic Image Denoising
Fabian Wagner, Mareike Thies, Laura Pfaff +10
Self-supervised image denoising techniques emerged as convenient methods that allow training denoising models without requiring ground-truth noise-free data. Existing methods usual…
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…
On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT Setting
Fabian Wagner, Mareike Thies, Laura Pfaff +9
Computed tomography (CT) is routinely used for three-dimensional non-invasive imaging. Numerous data-driven image denoising algorithms were proposed to restore image quality in low…
Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT
Fabian Wagner, Mareike Thies, Felix Denzinger +7
Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality. Recently, deep learning~(…