Biomedical Physics

Course with Participations of Group Members

Offers for Theses in the Group

AI in Physics: Convolutional neural networks for dark-field X-ray CT reconstruction

Grating-based X-ray dark-field imaging uses scattering of X-rays to create an image of an object, rather than conventional X-ray attenuation. The combination of X-ray scattering with imaging allows us to map information about structures that are much smaller than the resolution of the imaging system over a large field of view. X-ray dark-field imaging can be combined with computed tomography (CT) to create three-dimensional images of the scattering distribution inside an object. DF-CT was recently implemented for the first time into a clinical CT here at TUM (https://www.bioengineering.tum.de/en/news/details/new-technology-for-clinical-ct-scans). The goal of this project is to use convolutional neural networks (CNNs) to remove sampling artefacts in DF-CT images. Due to the unavailability of training data from the DF-CT machine, a technologically similar experimental setup and apply transfer learning will be used. The student will acquire, process and prepare training data, as well as train and apply CNNs. Character of thesis work: mainly computational physics & image processing For more information, please contact: Dr. Florian Schaff (florian.schaff@tum.de), or Prof. Franz Pfeiffer (franz.pfeiffer@tum.de).

suitable as

• Master’s Thesis Biomedical Engineering and Medical Physics

Supervisor: Franz Pfeiffer

AI in Physics: Convolutional neural networks for dark-field X-ray CT reconstruction

Grating-based X-ray dark-field imaging uses scattering of X-rays to create an image of an object, rather than conventional X-ray attenuation. The combination of X-ray scattering with imaging allows us to map information about structures that are much smaller than the resolution of the imaging system over a large field of view. X-ray dark-field imaging can be combined with computed tomography (CT) to create three-dimensional images of the scattering distribution inside an object.  DF-CT was recently implemented for the first time into a clinical CT here at TUM

(https://www.bioengineering.tum.de/en/news/details/new-technology-for-clinical-ct-scans).

The goal of this project is to use convolutional neural networks (CNNs) to remove sampling artefacts in DF-CT images. Due to the unavailability of training data from the DF-CT machine, a technologically similar experimental setup and apply transfer learning will be used. The student will acquire, process and prepare training data, as well as train and apply CNNs.

Character of thesis work: mainly computational physics & image processing

For more information, please contact: Dr. Florian Schaff (florian.schaff@tum.de), or Prof. Franz Pfeiffer (franz.pfeiffer@tum.de).

suitable as

• Master’s Thesis Applied and Engineering Physics

Supervisor: Franz Pfeiffer

Dark-field Chest X-ray Imaging: Advanced image processing for clinical applications

Dark-field radiography exploits the scattering of X-rays to visualize structures below the resolution limit. Currently, several initial clinical patient studies are underway on a worldwide first prototype we recently realized at Klinikum rechts der Isar. Within the framework of this project, the special algorithms for image post-processing of these first clinical data will be further optimized and used together with the participating radiologists for the evaluation of better direction of lung diseases.

Character of thesis work: mainly computational physics & image processing

For more information, please contact: Rafael Schick (rafael.schick@tum.de) or Franz Pfeiffer (franz.pfeiffer@tum.de).

suitable as

• Master’s Thesis Biomedical Engineering and Medical Physics

Supervisor: Franz Pfeiffer

Dark-field Chest X-ray Imaging: Advanced image processing for clinical applications

Dark-field radiography exploits the scattering of X-rays to visualize structures below the resolution limit. Currently, several initial clinical patient studies are underway on a worldwide first prototype we recently realized at Klinikum rechts der Isar. Within the framework of this project, the special algorithms for image post-processing of these first clinical data will be further optimized and used together with the participating radiologists for the evaluation of better direction of lung diseases.

Character of thesis work: mainly computational physics & image processing

For more information, please contact: Rafael Schick (rafael.schick@tum.de) or Franz Pfeiffer (franz.pfeiffer@tum.de).

suitable as

• Master’s Thesis Applied and Engineering Physics

Supervisor: Franz Pfeiffer

Dark-field Chest X-ray Imaging: Development of registration algorithms for the analysis of functional lung images

Dark-field radiography exploits the scattering of X-rays to reveal structures in lung tissue that cannot be visualized with conventional imaging. Currently, several initial clinical patient studies are underway on a worldwide first prototype we recently realized at Klinikum rechts der Isar. Within the scope of this project, special registration algorithms are to be developed that can register thorax images in inhalation and exhalation and allow local differences between ventilation states (for example in certain lung diseases).

Character of thesis work: mainly computational physics & image processing

For more information, please contact: Rafael Schick (rafael.schick@tum.de) or Franz Pfeiffer (franz.pfeiffer@tum.de).

suitable as

• Master’s Thesis Applied and Engineering Physics

Supervisor: Franz Pfeiffer

Dark-field Chest X-ray Imaging: Development of registration algorithms for the analysis of functional lung images

Dark-field radiography exploits the scattering of X-rays to reveal structures in lung tissue that cannot be visualized with conventional imaging. Currently, several initial clinical patient studies are underway on a worldwide first prototype we recently realized at Klinikum rechts der Isar. Within the scope of this project, special registration algorithms are to be developed that can register thorax images in inhalation and exhalation and allow local differences between ventilation states (for example in certain lung diseases).Character of thesis work: mainly computational physics & image processing

For more information, please contact: Rafael Schick (rafael.schick@tum.de) or Franz Pfeiffer (franz.pfeiffer@tum.de).

suitable as

• Master’s Thesis Biomedical Engineering and Medical Physics

Supervisor: Franz Pfeiffer

Dark-field Chest X-ray Imaging: Monte Carlo based simulation of Compton scattering

Dark-field radiography is a novel X-ray imaging technique that is being tested for the first time in clinical patient studies on a worldwide first prototype recently completed by us at the TUM Klinikum rechts der Isar. Within the scope of this project, Monte Carlo based Compton simulations will be developed, which will allow an exact modelling of the Compton scattering and thus a better correction of the image artifacts.

Character of thesis work: experimental physics (50%) & computational physics (50%).

For more information, please contact: Henriette Bast (henriette.bast@tum.de) or Franz Pfeiffer (franz.pfeiffer@tum.de).

suitable as

• Master’s Thesis Biomedical Engineering and Medical Physics

Supervisor: Franz Pfeiffer

Dark-field Chest X-ray Imaging: Monte Carlo based simulation of Compton scattering

Dark-field radiography is a novel X-ray imaging technique that is being tested for the first time in clinical patient studies on a worldwide first prototype recently completed by us at the TUM Klinikum rechts der Isar. Within the scope of this project, Monte Carlo based Compton simulations will be developed, which will allow an exact modelling of the Compton scattering and thus a better correction of the image artifacts.

Character of thesis work: experimental physics (50%) & computational physics (50%).

For more information, please contact: Henriette Bast (henriette.bast@tum.de) or Franz Pfeiffer (franz.pfeiffer@tum.de).

suitable as

• Master’s Thesis Applied and Engineering Physics

Supervisor: Franz Pfeiffer

Dark-field X-ray microCT: Pre-clinical research on improved lung disease detection

Dark-field computed tomography uses the wave property of X-rays to provide complementary contrasts in X-ray imaging. In this project, an existing prototype for dark-field CT in mice will be used to explore the use of dark-field contrast in pre-clinical research for improved detection of lung diseases in collaboration with the Helmholtz Center for Health. In addition to experimental work to support the conduct of the preclinical studies, algorithmic research to reduce image noise and dose is planned.

Character of thesis work: experimental medical physics (60%) & image processing (40%).

For more information, please contact: Benedikt Guenther (benedikt.guenther@mytum.de), Simon Zandarco (simon.zandarco@tum.de) or Franz Pfeiffer (franz.pfeiffer@tum.de).

suitable as

• Master’s Thesis Biomedical Engineering and Medical Physics

Supervisor: Franz Pfeiffer

Dark-field X-ray microCT: Pre-clinical research on improved lung disease detection

Dark-field computed tomography uses the wave property of X-rays to provide complementary contrasts in X-ray imaging. In this project, an existing prototype for dark-field CT in mice will be used to explore the use of dark-field contrast in pre-clinical research for improved detection of lung diseases in collaboration with the Helmholtz Center for Health. In addition to experimental work to support the conduct of the preclinical studies, algorithmic research to reduce image noise and dose is planned.

Character of thesis work: experimental medical physics (60%) & image processing (40%).

For more information, please contact: Benedikt Guenther (benedikt.guenther@mytum.de), Simon Zandarco (simon.zandarco@tum.de) or Franz Pfeiffer (franz.pfeiffer@tum.de).

suitable as

• Master’s Thesis Applied and Engineering Physics

Supervisor: Franz Pfeiffer

Current and Finished Theses in the Group