no code implementations • 23 Apr 2024 • Mareike Thies, Noah Maul, Siyuan Mei, Laura Pfaff, Nastassia Vysotskaya, Mingxuan Gu, Jonas Utz, Dennis Possart, Lukas Folle, Fabian Wagner, Andreas Maier
Motion artifacts can compromise the diagnostic value of computed tomography (CT) images.
no code implementations • 23 Apr 2024 • Siyuan Mei, Fuxin Fan, Mareike Thies, Mingxuan Gu, Fabian Wagner, Oliver Aust, Ina Erceg, Zeynab Mirzaei, Georgiana Neag, Yipeng Sun, Yixing Huang, Andreas Maier
Recently, X-ray microscopy (XRM) and light-sheet fluorescence microscopy (LSFM) have emerged as two pivotal imaging tools in preclinical research on bone remodeling diseases, offering micrometer-level resolution.
no code implementations • 4 Apr 2024 • Siyuan Mei, Fuxin Fan, Fabian Wagner, Mareike Thies, Mingxuan Gu, Yipeng Sun, Andreas Maier
Deep learning-based medical image processing algorithms require representative data during development.
no code implementations • 4 Mar 2024 • Noah Maul, Annette Birkhold, Fabian Wagner, Mareike Thies, Maximilian Rohleder, Philipp Berg, Markus Kowarschik, Andreas Maier
In our work, we implicitly include this information in a neural network-based model that is trained on a dataset of image-based blood flow simulations.
no code implementations • 17 Jan 2024 • Mareike Thies, Fabian Wagner, Noah Maul, Haijun Yu, Manuela Meier, Linda-Sophie Schneider, Mingxuan Gu, Siyuan Mei, Lukas Folle, Andreas Maier
The analytic Jacobian for the backprojection operation, which is at the core of the proposed method, is made publicly available.
no code implementations • 3 Aug 2023 • Jonas Utz, Tobias Weise, Maja Schlereth, Fabian Wagner, Mareike Thies, Mingxuan Gu, Stefan Uderhardt, Katharina Breininger
We show that this increases coherence between generated images and cycled masks and evaluate synthetic datasets on a downstream nuclei segmentation task.
no code implementations • 22 May 2023 • Felix Denzinger, Michael Wels, Oliver Taubmann, Florian Kordon, Fabian Wagner, Stephanie Mehltretter, Mehmet A. Gülsün, Max Schöbinger, Florian André, Sebastian Buss, Johannes Görich, Michael Sühling, Andreas Maier
If a patient exhibits a Shepherd's Crook (SC) Right Coronary Artery (RCA) - an anatomical norm variant of the coronary vasculature - the complexity of this procedure is increased.
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 • Mareike Thies, Fabian Wagner, Noah Maul, Laura Pfaff, Linda-Sophie Schneider, Christopher Syben, Andreas Maier
In computed tomography (CT), the projection geometry used for data acquisition needs to be known precisely to obtain a clear reconstructed image.
no code implementations • 13 Feb 2023 • Noah Maul, Katharina Zinn, Fabian Wagner, Mareike Thies, Maximilian Rohleder, Laura Pfaff, Markus Kowarschik, Annette Birkhold, Andreas Maier
Nevertheless, the prediction of high-resolution transient CFD simulations for complex vascular geometries poses a challenge to conventional deep learning models.
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 • 5 Dec 2022 • Mareike Thies, Fabian Wagner, Noah Maul, Lukas Folle, Manuela Meier, Maximilian Rohleder, Linda-Sophie Schneider, Laura Pfaff, Mingxuan Gu, Jonas Utz, Felix Denzinger, Michael Manhart, Andreas Maier
The cost function is parameterized by a trained neural network which regresses an image quality metric from the motion affected reconstruction alone.
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.
no code implementations • 15 Jul 2022 • Fabian Wagner, Mareike Thies, Felix Denzinger, Mingxuan Gu, Mayank Patwari, Stefan Ploner, Noah Maul, Laura Pfaff, Yixing Huang, Andreas Maier
Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality.
no code implementations • 8 Jun 2022 • Mingxuan Gu, Sulaiman Vesal, Mareike Thies, Zhaoya Pan, Fabian Wagner, Mirabela Rusu, Andreas Maier, Ronak Kosti
Then, to align the source and target features and tackle the memory issue of the traditional contrastive loss, we propose the centroid-based contrastive learning (CCL) and a centroid norm regularizer (CNR) to optimize the contrastive pairs in both direction and magnitude.
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.
no code implementations • 19 Jan 2022 • Mareike Thies, Fabian Wagner, Mingxuan Gu, Lukas Folle, Lina Felsner, Andreas Maier
Learned iterative reconstruction algorithms for inverse problems offer the flexibility to combine analytical knowledge about the problem with modules learned from data.
no code implementations • 14 Jan 2021 • Luciano Petruzziello, Fabian Wagner
This paper aims at investigating the influence of space-time curvature on the uncertainty relation.
General Relativity and Quantum Cosmology Quantum Physics