Fabian Wagner
Fabian Wagner, M. Sc.
You are working with noisy image data? Try out our open-source, fully Trainable Bilateral Filter Layer (PyTorch) to get rid of noise. It only adds FOUR trainable parameters to your network and you can integrate it in your model the same way as any convolutional layer.
Academic CV
- Since 10/2020:
Ph.D. Student at Pattern Recognition Lab, FAU Erlangen-Nürnberg - 01/2019 – 01/2020:
Master’s thesis at Northwestern University, Illinois and University of Wisconsin-Madison - 10/2014 – 09/2020:
B. Sc. and M. Sc. Physics, FAU Erlangen-Nürnberg
Projects
Robust and interpretable low-dose CT denoising in context of the 4D+nanoSCOPE:
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Advancing osteoporosis medicine by observing bone microstructure and remodelling using a four-dimensional nanoscope
(Third Party Funds Single)
Term: April 1, 2019 - March 31, 2025
Funding source: European Research Council (ERC)
URL: https://cordis.europa.eu/project/id/810316Due to Europe's ageing society, there has been a dramatic increase in the occurrence of osteoporosis (OP) and related diseases. Sufferers have an impaired quality of life, and there is a considerable cost to society associated with the consequent loss of productivity and injuries. The current understanding of this disease needs to be revolutionized, but study has been hampered by a lack of means to properly characterize bone structure, remodeling dynamics and vascular activity. This project, 4D nanoSCOPE, will develop tools and techniques to permit time-resolved imaging and characterization of bone in three spatial dimensions (both in vitro and in vivo), thereby permitting monitoring of bone remodeling and revolutionizing the understanding of bone morphology and its function.
To advance the field, in vivo high-resolution studies of living bone are essential, but existing techniques are not capable of this. By combining state-of-the art image processing software with innovative 'precision learning' software methods to compensate for artefacts (due e.g. to the subject breathing or twitching), and innovative X-ray microscope hardware which together will greatly speed up image acquisition (aim is a factor of 100), the project will enable in vivo X-ray microscopy studies of small animals (mice) for the first time. The time series of three-dimensional X-ray images will be complemented by correlative microscopy and spectroscopy techniques (with new software) to thoroughly characterize (serial) bone sections ex vivo.
The resulting three-dimensional datasets combining structure, chemical composition, transport velocities and local strength will be used by the PIs and international collaborators to study the dynamics of bone microstructure. This will be the first time that this has been possible in living creatures, enabling an assessment of the effects on bone of age, hormones, inflammation and treatment.
Publications
2023
Journal Articles
Gradient-based geometry learning for fan-beam CT reconstruction
In: Physics in Medicine and Biology 68 (2023), Article No.: 205004
ISSN: 0031-9155
DOI: 10.1088/1361-6560/acf90e
URL: https://iopscience.iop.org/article/10.1088/1361-6560/acf90e
BibTeX: Download
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Conference Contributions
Geometric Constraints Enable Self-Supervised Sinogram Inpainting in Sparse-View Tomography
17th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D) (Stony Brook University, NY, USA, July 16, 2023 - July 21, 2023)
DOI: 10.48550/arXiv.2302.06436
URL: https://arxiv.org/pdf/2302.06436.pdf
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Unsupervised Super Resolution in X-ray Microscopy using a Cycle-consistent Generative Model
BVM Workshop Bildverarbeitung für die Medizin 2023 (Braunschweig, July 2, 2023 - July 4, 2023)
In: Bildverarbeitung für die Medizin 2023 2023
DOI: 10.1007/978-3-658-41657-7_19
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Noise2Contrast: Multi-contrast Fusion Enables Self-supervised Tomographic Image Denoising
28th International Conference on Information Processing in Medical Imaging, IPMI 2023 (San Carlos de Bariloche, ARG, June 18, 2023 - June 23, 2023)
In: Alejandro Frangi, Marleen de Bruijne, Demian Wassermann, Nassir Navab (ed.): Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2023
DOI: 10.1007/978-3-031-34048-2_59
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Optimizing CT Scan Geometries With and Without Gradients
17th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D) (Stony Brook University, New York, July 16, 2023 - July 21, 2023)
Open Access: https://arxiv.org/abs/2302.06251
BibTeX: Download
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Quantitative evaluation of denoising algorithms without noise-free ground-truth data
ISMRM
BibTeX: Download
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Robust multi-contrast MRI denoising using trainable bilateral filters without noise-free targets
International Symposium on Biomedical Imaging (ISBI)
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Transient Hemodynamics Prediction Using an Efficient Octree-Based Deep Learning Model
28th International Conference on Information Processing in Medical Imaging, IPMI 2023 (San Carlos de Bariloche, June 18, 2023 - June 23, 2023)
In: Alejandro Frangi, Marleen de Bruijne, Demian Wassermann, Nassir Navab (ed.): Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2023
DOI: 10.1007/978-3-031-34048-2_15
BibTeX: Download
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Unsupervised denoising of prostate DWI
ISMRM
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2022
Journal Articles
Deep learning for terahertz image denoising in nondestructive historical document analysis
In: Scientific Reports 12 (2022), p. 1-11
ISSN: 2045-2322
DOI: 10.1038/s41598-022-26957-7
URL: https://www.nature.com/articles/s41598-022-26957-7
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Trainable joint bilateral filters for enhanced prediction stability in low-dose CT
In: Scientific Reports 12 (2022), Article No.: 17540
ISSN: 2045-2322
DOI: 10.1038/s41598-022-22530-4
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Calibration by differentiation – Self-supervised calibration for X-ray microscopy using a differentiable cone-beam reconstruction operator
In: Journal of Microscopy (2022)
ISSN: 0022-2720
DOI: 10.1111/jmi.13125
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Ultra low‐parameter denoising: Trainable bilateral filter layers in computed tomography
In: Medical Physics (2022)
ISSN: 0094-2405
DOI: 10.1002/mp.15718
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Conference Contributions
Learned Cone-Beam CT Reconstruction Using Neural Ordinary Differential Equations
7th International Conference on Image Formation in X-Ray Computed Tomography (Baltimore, June 12, 2022 - June 16, 2022)
In: Proceedings Volume 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography; 1230409 (2022) 2022
DOI: 10.1117/12.2646442
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Monte Carlo Dose Simulation for In-Vivo X-Ray Nanoscopy
German Workshop on Medical Image Computing, 2022 (Heidelberg, DEU, June 26, 2022 - June 28, 2022)
In: Klaus Maier-Hein, Thomas M. Deserno, Heinz Handels, Andreas Maier, Christoph Palm, Thomas Tolxdorff (ed.): Informatik aktuell 2022
DOI: 10.1007/978-3-658-36932-3_22
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Tibia Cortical Bone Segmentation in Micro-CT and X-ray Microscopy Data Using a Single Neural Network
German Workshop on Medical Image Computing, 2022 (Heidelberg, June 26, 2022 - June 28, 2022)
In: Klaus Maier-Hein, Thomas M. Deserno, Heinz Handels, Andreas Maier, Christoph Palm, Thomas Tolxdorff (ed.): Informatik aktuell 2022
DOI: 10.1007/978-3-658-36932-3_68
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2021
Journal Articles
Intensity interferometry-based 3D imaging
In: Optics Express 29 (2021), p. 4733-4745
ISSN: 1094-4087
DOI: 10.1364/OE.412688
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Conference Contributions
Semi-Permeable Filters for Interior Region of Interest Dose Reduction in X-Ray Microscopy
BVM 2021 (Regensburg, March 7, 2021 - March 9, 2021)
In: Bildverarbeitung für die Medizin 2021
DOI: 10.1007/978-3-658-33198-6_16
URL: https://link.springer.com/chapter/10.1007/978-3-658-33198-6_16
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2020
Journal Articles
Length-dependence of light-induced currents in graphene
In: Journal of Physics B-Atomic Molecular and Optical Physics (2020)
ISSN: 0953-4075
DOI: 10.1088/1361-6455/ab9075
URL: https://iopscience.iop.org/article/10.1088/1361-6455/ab9075
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Conference Contributions
Intensity interferometry-based depth ranging
Computational Optical Sensing and Imaging 2020
DOI: 10.1364/COSI.2020.CTh3C.5
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Lectures
- Projekt Flat-Panel CT Reconstruction (ProjFCR): Do 10:00 – 12:00, 2 SWS (5 ECTS)
Student theses
Type | Title | Status |
---|---|---|
MA thesis | Diffusion-based Super Resolution for X-ray Microscopy | finished |
MA thesis | Classification of Detector Artifacts in Angiographic Imaging using Neural Networks | finished |
Project | Super-short Scans in Bone XRM Acquisitions | finished |
Project | Sparse-angle CT Super Resolution using Known Operators | running |
Project | Deep Learning-based XRM Projection Super Resolution | finished |
Project | Unsupervised Super Resolution in X-ray Microscopy Using a Cycle-Consistent Generative Model | finished |
MA thesis | Learning-based reduction of non-significant changes in subtraction volumes | finished |
MA thesis | Micro CT Denoising Using Low Parameter Models | finished |