Search Results for author: Sabrina Pechmann

Found 5 papers, 2 papers with code

Exploring Epipolar Consistency Conditions for Rigid Motion Compensation in In-vivo X-ray Microscopy

no code implementations1 Mar 2023 Mareike Thies, Fabian Wagner, Mingxuan Gu, Siyuan Mei, Yixing Huang, Sabrina Pechmann, Oliver Aust, Daniela Weidner, Georgiana Neag, Stefan Uderhardt, Georg Schett, Silke Christiansen, Andreas Maier

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 characteristic of osteoporosis.

Motion Compensation

Geometric Constraints Enable Self-Supervised Sinogram Inpainting in Sparse-View Tomography

no code implementations13 Feb 2023 Fabian Wagner, Mareike Thies, Noah Maul, Laura Pfaff, Oliver Aust, Sabrina Pechmann, Christopher Syben, Andreas Maier

By reconstructing independent stacks of projection data, a self-supervised loss is calculated in the CT image domain and used to directly optimize projection image intensities to match the missing tomographic views constrained by the projection geometry.

Computed Tomography (CT) CT Reconstruction +1

On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT Setting

1 code implementation2 Nov 2022 Fabian Wagner, Mareike Thies, Laura Pfaff, Oliver Aust, Sabrina Pechmann, Daniela Weidner, Noah Maul, Maximilian Rohleder, Mingxuan Gu, Jonas Utz, Felix Denzinger, Andreas Maier

In this work, we present an end-to-end trainable CT reconstruction pipeline that contains denoising operators in both the projection and the image domain and that are optimized simultaneously without requiring ground-truth high-dose CT data.

Computed Tomography (CT) CT Reconstruction +2

Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomography

1 code implementation25 Jan 2022 Fabian Wagner, Mareike Thies, Mingxuan Gu, Yixing Huang, Sabrina Pechmann, Mayank Patwari, Stefan Ploner, Oliver Aust, Stefan Uderhardt, Georg Schett, Silke Christiansen, Andreas Maier

Due to the extremely low number of trainable parameters with well-defined effect, prediction reliance and data integrity is guaranteed at any time in the proposed pipelines, in contrast to most other deep learning-based denoising architectures.

Denoising SSIM

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