6 citations · 13 across the 19 of their papers we have counts for
11 papers · 1 filter
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…
Attri-Net: A Globally and Locally Inherently Interpretable Model for Multi-Label Classification Using Class-Specific Counterfactuals
Susu Sun, Stefano Woerner, Andreas Maier +2
Interpretability is crucial for machine learning algorithms in high-stakes medical applications. However, high-performing neural networks typically cannot explain their predictions…
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…
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…
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…
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…