Open Positions

The Munich Compact Light Source (MuCLS) is a laboratory-scale synchrotron radiation facility located at the Munich Institute of Biomedical Engineering (MIBE) in Garching and operated by the Chair of Biomedical Physics. Its X-ray source uses the principle of inverse Compton scattering to produce a partially coherent, quasi-monochromatic and high-flux X-ray beam [1].

Among these techniques, propagation-based phase-contrast imaging (PBI) is a very versatile single-shot phase-sensitive X-ray imaging method. On the one hand, this enables studying dynamic processes in specimens of biomedical interest, such as in vivo small-animal X-ray imaging of airways or lungs [2]. On the other hand, rapid micro-CTs of centimeter-scale samples at resolutions better than 10 μm become feasible.

The current PBI framework at the MuCLS employs the standard Paganin single-material single-distance phase retrieval. Within the framework of this thesis, more sophisticated phase-retrieval techniques, e.g. employing convolutional neuronal networks or the Fokker-Plank equation, will be developed and implemented. Furthermore, the duration of the micro-CT scans should be reduced by exploiting (both classical and AI-based) methods for denoising and sparse sampling. The improved PBI framework will be used to measure biomedical specimens, e.g., cochleas.

Character of the thesis: theory, programming & data processing (60%), experimental (40%)

Experience in X-ray or phase-contrast imaging as well as Python programming is desirable.

For further information, please contact Prof. Franz Pfeiffer (franz.pfeiffer(at)tum.de).

[1] Günther et al. 2020. https://dx.doi.org/10.1107/s1600577520008309
[2] Gradl et al. 2018. https://dx.doi.org/10.1038/s41598-018-24763-8

The Munich Compact Light Source (MuCLS) is a laboratory-scale synchrotron radiation facility located at the Munich Institute of Biomedical Engineering (MIBE) in Garching and operated by the Chair of Biomedical Physics. Its X-ray source uses the principle of inverse Compton scattering to produce a partially coherent, quasi-monochromatic and high-flux X-ray beam [1].

X-ray absorption spectroscopy (XAS) provides element-specific information about the chemical surroundings of an atom and, therefore, has a wide range of applications in different research fields. The XAS setup implemented at MuCLS [2] has successfully been applied in different catalysis research projects in collaboration with the TUM Department of Chemistry [3].

This PhD project will focus on further developing the energy-dispersive XAS setup at MuCLS and its applications. Currently, the setup employs a flat crystal in Laue geometry to generate a spatial energy gradient (see [1] for details). This geometry also results in a polychromatic line focus, which in the past had been inaccessible for experiments due to geometrical constraints. With a recent major remodeling of the experimental area of the MuCLS, these constraints have been removed. The PhD project is expected to make use of this increased flexibility by designing, implementing, and conducting experiments with the polychromatic focus. Furthermore, dynamical bending of the crystal shall be investigated to control both the bandwidth and the focusing of the transmitted beam. Exploration of alternative setup geometries (through simulations and experiments) and combination with other techniques like, e.g., X-ray fluorescence spectroscopy, is desirable.

Character of the thesis: simulation & data processing (50%), experimental (including setup design) (50%).

Experience in Python programming and CAD is desirable. Previous experience with XAS is considered an asset but not a requirement.

For further information, please contact Prof. Franz Pfeiffer (franz.pfeiffer(at)tum.de).

[1] B. Günther et al. 2020. https://doi.org/10.1107/S1600577520008309
[2] J. Huang et al. 2020. https://doi.org/10.1038/s41598-020-65225-4
[3] J. Huang et al. 2021. https://doi.org/10.1039/D1JA00274K

Dark-field radiography of the human chest is a promising novel imaging technique with the potential of becoming a valuable tool for the early diagnosis of chronic obstructive pulmonary disease and other diseases of the lung. First clinical results with patients could recently be achieved by a prototype system installed in the TUM university hospital.

This PhD will focus on exploring advanced postprocessing and reconstruction algorithms for improving image quality in this scanning and future full-field dark-field radiography systems [1]. It will include novel motion correction, denoising, and beam hardening algorithms and also use latest machine learning approaches. The work will have immediate impact on the clinical usage of this methods and provide the basis for future commercial implementation.

Character of thesis work: Image processing & machine learning. Experience in python programming is essential, and additional experience in image processing and/or machine learning beneficial.

To apply for this position, please send a short motivation letter, CV and other relevant information to Prof. Franz Pfeiffer [franz.pfeiffer(at)tum.de].

[1] Schick et al. 2021. https://doi.org/10.1109/TMI.2021.3126492

A big challenge for translating grating-based dark-field computed tomography to medical applications lies in improving CT reconstruction and image postprocessing algorithms, specifically for the darkfield signal. This is particularly necessary, as darkfield CT is very sensitive to vibrations and also subject to noise if the visibility of the interferometer is limited.

This PhD will focus on advancing the presently existing reconstruction framework by implementing latest advances in the field, e.g. including also AI-based improvements in the reconstruction pipeline and in image postprocessing [1,2,3]. The algorithms will be implemented in a human darkfield CT prototype and enable first patient applications.

Character of the thesis: Mainly theoretical and numerical (80%), with some experimental activities (20%) possible. Experience in python and/or machine learning is desirable.

To apply for this position, please send a short motivation letter, CV and other relevant information to Prof. Franz Pfeiffer [franz.pfeiffer(at)tum.de].

[1] Schmid et al. 2022 . https://doi.org/10.1109/TMI.2022.3217662
[2] Haeusele et al. 2023. https://doi.org/10.1109/TMI.2023.3271413
[3] Haeusele et al. 2024. https://doi.org/10.1109/TMI.2024.3400593

Ring artifacts are a common issue in X-ray computed tomography (CT), caused by variations in detector pixel sensitivity. These artifacts manifest as circular patterns in reconstructed images and vary in shape and intensity. While traditional post-reconstruction image processing techniques are frequently employed to mitigate them, they often fall short under challenging conditions.

This project aims to develop machine learning–based methods for the effective removal of ring artifacts in CT images. Tasks include simulating ring artifacts on existing micro-CT datasets, training machine learning models for artifact correction, and benchmarking their performance against classical image processing techniques.

Character of thesis work: Numerical Simulation (33%), Image Processing (33%), Machine Learning (33%)

Experience in X-ray imaging and Python programming are desirable.

For more information, please contact: Sebastian Peterhansl [sebastian.peterhansl(at)tum.de], Dr. Florian Schaff [florian.schaff(at)tum.de], or Prof. Franz Pfeiffer [franz.pfeiffer(at)tum.de].

Noise in X-ray imaging degrades image quality and can obscure fine structural details. It is primarily caused by shot noise due to a limited number of photons, common in low-dose or rapid imaging, and electronic noise from detector hardware. A distinct characteristic in X-ray imaging with flat-panel detectors is the spatial correlation of shot noise across neighboring pixels. Traditional denoising techniques, often developed for uncorrelated noise, typically do not account for this.

The aim of this project is to develop machine learning–based methods to denoise X-ray images and enhance image quality. The work involves simulating realistic noise in existing micro-CT datasets, training machine learning models for noise removal, and benchmarking their performance against classical image processing techniques.

Character of thesis work: Numerical Simulation (33%), Image Processing (33%), Machine Learning (33%)

Experience in X-ray imaging and Python programming are desirable.

For more information, please contact: Sebastian Peterhansl [sebastian.peterhansl(at)tum.de], Dr. Florian Schaff [florian.schaff(at)tum.de], or Prof. Franz Pfeiffer [franz.pfeiffer(at)tum.de].

Grating-based X-ray dark-field Computed Tomography uses scattering of X-rays to create an image of an object, rather than conventional X-ray attenuation. This technique enables mapping of microstructural features from structures much smaller than the resolution of the imaging system over a large field of view. Since dark-field imaging obtains structural information indirectly via scattering, image resolution is less critical than in attenuation imaging and can be traded in favour of image quality.

The goal of this project is to develop and apply upscaling techniques that generate dark-field images matched in resolution to the corresponding full-resolution attenuation image. This enables combined data representation, essential for simultaneous visualization of morphological (attenuation-based) and microstructural (dark-field-based) information.

Character of thesis work: CT reconstruction (33%), Image Processing (33%), Machine Learning (33%)

Experience in X-ray imaging and Python programming are desirable.

For more information, please contact: Daniel Frey  [daniel.frey(at)tum.de], Dr. Florian Schaff [florian.schaff(at)tum.de], or Prof. Franz Pfeiffer [franz.pfeiffer(at)tum.de].

Grating-based X-ray dark-field Computed Tomography uses scattering of X-rays to create an image of an object, rather than conventional X-ray attenuation. This technique enables mapping of microstructural features from structures much smaller than the resolution of the imaging system over a large field of view. Currently, X-ray Dark-Field images suffer from increased image noise and more image artifacts compared to standard attenuation CT images.

The goal of this project is to develop machine-learning-based image enhancement methods to improve image quality in X-ray dark-field CT images. The focus will be on self-supervised learning techniques that have shown strong performance in denoising tasks for attenuation-based CT.

Character of thesis work: CT reconstruction (33%), Image Processing (33%), Machine Learning (33%)

Experience in X-ray imaging and Python programming are desirable.

For more information, please contact: Daniel Frey  [daniel.frey(at)tum.de], Dr. Florian Schaff [florian.schaff(at)tum.de], or Prof. Franz Pfeiffer [franz.pfeiffer(at)tum.de].