no code implementations • 1 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.
no code implementations • 13 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.
no code implementations • 9 Dec 2022 • Fabian Wagner, Mareike Thies, Laura Pfaff, Noah Maul, Sabrina Pechmann, Mingxuan Gu, Jonas Utz, Oliver Aust, Daniela Weidner, Georgiana Neag, Stefan Uderhardt, Jang-Hwan Choi, Andreas Maier
We stack denoising with domain-transfer operators to utilize the independent noise realizations of different image contrasts to derive a self-supervised loss.
1 code implementation • 2 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.
1 code implementation • 25 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.