Maximilian Reymann
Maximilian Reymann, M. Sc.
Researcher
Martensstr. 3
91058 Erlangen
Department of Computer Science
Chair of Computer Science 5 (Pattern Recognition)
Martensstr. 3
91058 Erlangen
- Phone number: +49 9131 85-28982
- Fax number: +49 9131 85-27270
- Email: maximilian.reymann@fau.de
- Website: https://lme.tf.fau.de/person/reymann/
Office hours
Each week Fr,Academic CV
- Since 04/2019:
PhD student at the Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Germany
In cooperation with Siemens Medical Solutions, Hoffman Estates, USA and Clinic of Nuclear Medicine, University Hospital Erlangen, Germany - Until 03/2019:
Masters degree in Medical Engineering with focus on Image and Data Analytics at the FAU - Until 09/2016:
Bachelors degree in Medical Engineering with focus on Prosthetics at the FAU - 03/2016 – 09/2016:
Bachelor’s thesis at the computational Cardiology Lab, Johns Hopkins University, Baltimore, USA
PhD Topic
2019
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Improving multi-modal quantitative SPECT with Deep Learning approaches to optimize image reconstruction and extraction of medical information
(Non-FAU Project)
Term: April 1, 2019 - April 30, 2022This project aims to improve multi-modal quantitative SPECT with Deep Learning approaches to optimize image reconstruction and extraction of medical information. Such improvements include noise reduction and artifact removal from data acquired in SPECT.
Publications
2023
Journal Articles
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Method for comparison of data driven gating algorithms in emission tomography
In: Physics in Medicine and Biology (2023)
ISSN: 0031-9155
Open Access: https://iopscience.iop.org/article/10.1088/1361-6560/acf3ce
URL: http://iopscience.iop.org/article/10.1088/1361-6560/acf3ce
BibTeX: Download
Conference Contributions
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Masking for DDG in SPECT Reduces Influence of Motion Artifacts
In: IEEE (ed.): IEEE RTSD NSS-MIC 2023 2023
BibTeX: Download - , , :
Data Driven Gating in SPECT using Isomap
SNMMI Annual Meeting (Chicago, IL, USA)
In: SNMMI (ed.): Society of Nuclear Medicine and Molecular Imaging Annual Meeting 2023 2023
BibTeX: Download
2022
Conference Contributions
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Effect of High-Uptake Regions with Inconsistent Motion on Data Driven Respiratory Gating in SPECT Myocardial Perfusion Imaging using Digital Phantoms
In: EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, NEW YORK: 2022
BibTeX: Download - , , , , :
Digital Twinning of Dynamic Anthropomorphic Population for Assessment of Data Driven Respiratory Gating Methods in SPECT
In: IEEE Nuclear Science Symposium and Medical Imaging Conference, Milano: 2022
BibTeX: Download
2021
Conference Contributions
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GAN Generated Model Observer for one Class Detection in SPECT Imaging
SPIE (Online, February 15, 2021 - February 19, 2021)
In: SPIE (ed.): GAN Generated Model Observer for one Class Detection in SPECT Imaging 2021
DOI: 10.1117/12.2582113
BibTeX: Download - Gosewisch A, Reymann M, Ilhan H, Beyer L, Platsch G, Bartenstein P, Vija AH, Boening G:
Investigation of the effect of small but frequently occurring patient movements onto 3D activity quantitation in Siemens xSPECT Quant reconstruction with integrated motion correction
In: EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING 2021
BibTeX: Download - , , , , , :
Deep Learning based Model Observers for Multi - Modal Imaging
16th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Leuven, Belgium, July 19, 2021 - July 23, 2021)
BibTeX: Download
2020
Conference Contributions
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Feature Loss After Denoising of SPECT Projection Data using a U-Net
2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (Boston, Massachusetts, October 31, 2020 - November 7, 2020)
In: IEEE (ed.): Feature Loss After Denoising of SPECT Projection Data using a U-Net 2020
DOI: 10.1109/nss/mic42677.2020.9508041
BibTeX: Download - , , , , , :
U-Net for Multi-Organ Segmentation of SPECT Projection Data
IEEE (Boston, Massachuchets, October 31, 2020 - November 7, 2020)
In: IEEE (ed.): U-Net for Multi-Organ Segmentation of SPECT Projection Data 2020
DOI: 10.1109/nss/mic42677.2020.9507779
BibTeX: Download
2019
Conference Contributions
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Deep Image Denoising in SPECT
Annual Congress of the European Association of Nuclear Medicine (Barcelona, October 12, 2019 - October 16, 2019)
In: Springer-Verlag GmbH Germany, part of Springer Nature 2019 (ed.): European Journal of Nuclear Medicine and Molecular Imaging (2019) 46 (Suppl 1): S1–S952, 10.1007/s00259-019-04486-2, Berlin, Heidelberg: 2019
DOI: 10.1007/s00259-019-04486-2
URL: https://link.springer.com/article/10.1007/s00259-019-04486-2
BibTeX: Download - , , , , , , :
U-Net for SPECT Image Denoising
2019 IEEE Nuclear Science Symposium (NSS) and Medical Imaging Conference (MIC) (Manchester, October 26, 2019 - November 2, 2019)
In: IEEE (ed.): 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC) 2019
DOI: 10.1109/nss/mic42101.2019.9059879
BibTeX: Download
2016
Conference Contributions
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Blood glucose level prediction based on support vector regression using mobile platforms
38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (Orlando, Florida, USA, August 16, 2016 - August 19, 2016)
In: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2016
DOI: 10.1109/EMBC.2016.7591358
URL: https://www.mad.tf.fau.de/files/2017/06/2016-Reymann-EMBC-BGL.pdf
BibTeX: Download
Student Theses
| Type | Title | Status |
|---|---|---|
| Project | Development of a comprehensive SPECT phantom dataset using Monte Carlo Simulation | finished |
| MA thesis | Integration of Augmented Reality in SPECT-CT Workflows | finished |
| Project | Manifold Forests | running |
| Project | Deep Learning based Model Observers for Multi-modal Imaging | finished |
| Project | Analysis of NVIDIA Optix Engine for Ray Tracing in SPECT | running |
| Project | GAN Generated Model Observer for one Class Detection in SPECT Imaging | finished |
| Project | Adapting Pyro-NN with SPECT operators | finished |
| Project | Advancing the digital twin method | finished |
| BA thesis | Improved image quality of Limited Angle and Sparse View SPECT using Deep Learning | finished |
| MA thesis | Synthetic generation of CT image from non-attenuation corrected FDG-PET image using GANs and its application to whole-body PET/CT registration. | finished |
| MA thesis | Convolutional Neural Networks for multi-organ segmentation of SPECT projections | finished |