20 citations · 36 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…
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~(…