Maximilian Rohleder
at Friedrich-Alexander-Univ. Erlangen-Nürnberg
SPIE Involvement:
Area of Expertise:
Cone Beam CT , Deep Learning , Operator Learning , Metal Artifact Reduction
Profile Summary

Maximilian Rohleder is a doctoral researcher from Munich, Germany. He is conducting his doctoral research in a collaboration between Friedrich-Alexander-University (FAU) and Siemens Healthineers with roles both in Mobile C-Arms Systems pre-development and at the university. In summer 2022, he visited the I-Star Laboratory at Johns Hopkins University to further investigate the topic of Metal Artifact Avoidance. Max studied Medical Engineering at FAU and the University of Lisbon (Instituto Superior Técnico) and conducted his Bachelor levels research at Stanford University. His research interests include new 2D and 3D imaging workflows for intraoperative Cone-Beam CT, the simulation of realistic X-Ray data, and integration of reconstruction operators in Neural Network architectures.
Publications (4)

Proceedings Article | 3 April 2024 Poster + Paper
Proceedings Volume 12927, 129273B (2024)
KEYWORDS: Cone beam computed tomography, Voxels, Image segmentation, Lung, Computed tomography, Medical imaging

Proceedings Article | 1 April 2024 Poster + Paper
Maximilian Rohleder, Holger Kunze, Andreas Maier, Bjoern Kreher
Proceedings Volume 12925, 129253C (2024)
KEYWORDS: Image segmentation, Metals, Data modeling, Cone beam computed tomography, 3D projection, Matrices, X-rays, X-ray imaging, Deep learning

Proceedings Article | 7 April 2023 Presentation + Paper
M. Rohleder, L. Mekki, A. Uneri, A. Sisniega, H. Kunze, G. Kleinszig, A. Maier, B. Kreher, J. Siewerdsen
Proceedings Volume 12463, 1246312 (2023)
KEYWORDS: Metals, 3D modeling, Image restoration, Cone beam computed tomography, Signal attenuation, Image segmentation, 3D projection, Prior knowledge, Expectation maximization algorithms, X-ray imaging

Proceedings Article | 18 October 2022 Open Access Paper
Maximilian Rohleder, Tristan Gottschalk, Andreas Maier, Bjoern Kreher
Proceedings Volume 12304, 123040K (2022)
KEYWORDS: Metals, Image segmentation, X-ray computed tomography, X-ray imaging, Network architectures, Neural networks, 3D image reconstruction, Machine learning

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