Publications (9)
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~(…
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…
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…
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…
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…
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…