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Biomedical Physics

Prof. Franz Pfeiffer

Research Field

Our interdisciplinary research portfolio is focused on the translation of modern x-ray physics concepts to biomedical sciences and clinical applications. We are particularly interested in advancing conceptually new approaches for biomedical x-ray imaging and therapy, and work on new kinds of x-ray sources, contrast modalities, and images processing algorithms. Our activities range from fundamental research using state-of-the-art, large-scale x-ray synchrotron and laser facilities to applied research and technology transfer projects aiming at the creation of improved biomedical device technology for clinical use. From a medical perspective, our work currently targets early cancer and osteoporosis diagnostics.

Address/Contact

James-Franck-Str. 1
85748 Garching b. München
+49 89 289 12552
Fax: +49 89 289 12548

Members of the Research Group

Professors

Office

Scientists

Students

Other Staff

Teaching

Course with Participations of Group Members

Titel und Modulzuordnung
ArtSWSDozent(en)Termine
Bildgebende Verfahren
Zuordnung zu Modulen:
VO 2 Birnbacher, L. Bogdanovic, B. Burian, E. Busse, M. Ganter, C. … (insgesamt 9) Di, 11:30–13:00
Biomedical Physics 1
eLearning-Kurs
Zuordnung zu Modulen:
VO 2 Pfeiffer, F.
Mitwirkende: Schaff, F.
Mo, 12:00–14:00
Biomedical Physics 2
eLearning-Kurs
Zuordnung zu Modulen:
VO 2 Pfeiffer, F. Wilkens, J.
Mitwirkende: Schaff, F.
Mo, 10:00–12:00
Chemistry in Biomedical Imaging for Physicists
eLearning-Kurs
Zuordnung zu Modulen:
VO 2 Pfeiffer, F.
Mitwirkende: Busse, M.
Image Processing in Physics
eLearning-Kurs
Zuordnung zu Modulen:
VO 2 Achterhold, K. Herzen, J. Do, 14:00–16:00, EI-HS Garching
Modern X-Ray Physics
eLearning-Kurs
Zuordnung zu Modulen:
VO 2 Achterhold, K.
Mitwirkende: Dierolf, M.
Di, 10:00–12:00, PH II 127
Advances in X-Ray Imaging
eLearning-Kurs
Zuordnung zu Modulen:
PS 2 Herzen, J.
Mitwirkende: Busse, M.
Mi, 14:00–16:00, PH 2074
Biomedical Physics
eLearning-Kurs
Zuordnung zu Modulen:
HS 2 Pfeiffer, F.
Mitwirkende: Schaff, F.
Di, 12:15–13:45, PH 2074
sowie einzelne oder verschobene Termine
Blockseminar zu aktuellen Themen in der Biomedizinischen Physik (E17 Seminarwoche)
Zuordnung zu Modulen:
HS 2 Herzen, J. Pfeiffer, F.
Modern X-Ray Physics
eLearning-Kurs
Zuordnung zu Modulen:
HS 2 Pfeiffer, F.
Mitwirkende: Achterhold, K.Dierolf, M.
Fr, 13:00–15:00, PH 2074
Seminar zu aktuellen Themen im BioEngineering (MIBE-Seminar)
Zuordnung zu Modulen:
HS 2 Pfeiffer, F.
Mitwirkende: Schaff, F.
Exercises to Modern X-Ray Physics
eLearning-Kurs
Zuordnung zu Modulen:
UE 2
Leitung/Koordination: Achterhold, K.
Mi, 14:00–16:00, PH II 227
Exercise to Biomedical Physics 1
Zuordnung zu Modulen:
UE 2 Schaff, F.
Leitung/Koordination: Pfeiffer, F.
Mo, 12:00–14:00, PH HS3
Exercise to Biomedical Physics 2
Zuordnung zu Modulen:
UE 2 Schaff, F. Wilkens, J.
Leitung/Koordination: Pfeiffer, F.
Mo, 10:00–12:00, PH HS3
Exercise to Image Processing in Physics
eLearning-Kurs
Zuordnung zu Modulen:
UE 1
Leitung/Koordination: Achterhold, K.
Termine in Gruppen
Übungen zu Grundlagen: Algorithmen und Datenstrukturen (IN0007), Fr
Zuordnung zu Modulen:
UE 2 Lasser, T.
Mitwirkende: Cheslerean-Boghiu, T.Pekel, E.Stevens, L.
Termine in Gruppen
Übungen zu Grundlagen: Algorithmen und Datenstrukturen (IN0007), Mi, Do
Zuordnung zu Modulen:
UE 2 Lasser, T.
Mitwirkende: Cheslerean-Boghiu, T.Pekel, E.Stevens, L.
Termine in Gruppen
Übungen zu Grundlagen: Algorithmen und Datenstrukturen (IN0007), Mo, Di
Zuordnung zu Modulen:
UE 2 Lasser, T.
Mitwirkende: Cheslerean-Boghiu, T.Pekel, E.Stevens, L.
Termine in Gruppen
Übung zu Chemie der biomedizinischen Bildgebung für Physiker
Zuordnung zu Modulen:
UE 1 Busse, M.
Leitung/Koordination: Pfeiffer, F.
BEMP Lab 01: Clinical Computed Tomography
eLearning-Kurs aktuelle Informationen
Zuordnung zu Modulen:
PR 4 Birnbacher, L. Hammel, J.
Leitung/Koordination: Pfeiffer, F.
einzelne oder verschobene Termine
BEMP Lab 02: High-Resolution Micro-Computed Tomography
eLearning-Kurs aktuelle Informationen
Zuordnung zu Modulen:
PR 4 Riedel, M.
Leitung/Koordination: Herzen, J.
einzelne oder verschobene Termine
BEMP Lab 03: Magnetic Resonance Imaging
eLearning-Kurs aktuelle Informationen
Zuordnung zu Modulen:
PR 4 Karampinos, D. Schilling, F.
Mitwirkende: Gottwald, W.Ruschke, S.
einzelne oder verschobene Termine
Current Research Topics in Biomedical Imaging (E17 Seminar)
Zuordnung zu Modulen:
SE 2 Hemmer, B. Herzen, J. Pfeiffer, F.
FOPRA-Versuch 79: Röntgencomputertomographie (AEP, BIO, KM, KTA)
LV-Unterlagen aktuelle Informationen
Zuordnung zu Modulen:
PR 1 Häusele, J. Somerkivi, V.
Leitung/Koordination: Pfeiffer, F.
Informationen zu Forschungsphase, Masterarbeit und Studienabschluss im Masterstudiengang Biomedical Engineering and Medical Physics
eLearning-Kurs
Diese Lehrveranstaltung ist keinem Modul zugeordnet.
OV 0.1 Herzen, J. einzelne oder verschobene Termine
Master's Seminar (BEMP)
Zuordnung zu Modulen:
SE 10
Leitung/Koordination: Herzen, J.
Master's Work Experience (BEMP)
Zuordnung zu Modulen:
FO 10
Leitung/Koordination: Herzen, J.
Mentoring-Programm im Bachelorstudiengang Physik
Zuordnung zu Modulen:
KO 0.2 Herzen, J. einzelne oder verschobene Termine
Mentoring-Programm im Bachelorstudiengang Physik
Zuordnung zu Modulen:
KO 0.2 Pfeiffer, F.
Repetitorium zu Biomedizinische Physik
Zuordnung zu Modulen:
RE 2
Leitung/Koordination: Pfeiffer, F.
Repetitorium zu Moderne Röntgenphysik
Zuordnung zu Modulen:
RE 2
Leitung/Koordination: Pfeiffer, F.
Repetitorium zu Neue Entwicklungen in der Röntgenbildgebung
Zuordnung zu Modulen:
RE 2
Leitung/Koordination: Herzen, J.
Repetitorium zu Seminar zu aktuellen Themen im BioEngineering (MSB-Seminar)
Zuordnung zu Modulen:
RE 2
Leitung/Koordination: Pfeiffer, F.
Vorbesprechung zum BEMP-Fortgeschrittenenpraktikum
eLearning-Kurs
Zuordnung zu Modulen:
OV 0.1 Herzen, J. einzelne oder verschobene Termine
Vorstellung der Studien- und Prüfungsordnung im Masterstudiengang Biomedical Engineering and Medical Physics
eLearning-Kurs
Diese Lehrveranstaltung ist keinem Modul zugeordnet.
OV 0.1 Block, K. Herzen, J. einzelne oder verschobene Termine

Offers for Theses in the Group

Convolutional neural networks and transfer learning for artefacts reduction in X-ray dark-field CT

Grating-based X-ray dark-field (DF) 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: experimental lab work/ data acquisition (50%) & computational/ image processing (50%)

Basic experience in image processing, CNNs, and/or Python programming are desirable.

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
Convolutional neural networks and transfer learning for artefacts reduction in X-ray dark-field CT

Grating-based X-ray dark-field (DF) 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: experimental lab work/ data acquisition (50%) & computational/ image processing (50%)

Basic experience in image processing, CNNs, and/or Python programming are desirable.

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
Distortion correction for high-resolution quantitative X-ray imaging detectors

X-ray imaging detectors - in particular for high-resolution microscopy applications - may suffer from distortions, which degrade the image quality. This can have severe negative effects for quantitative applications, such as 3D micro-computed tomography. This project focuses on the characterisation of distortions of several X-ray imaging detectors at the Munich Compact Light Source, and the subsequent development of suitable correction methods.

Character of thesis work: mainly computational (image processing)  

For more information, please contact: Martin Dierolf (martin.dierolf@tum.de), Johannes Melcher (johannes.melcher@tum.de), or Franz Pfeiffer (franz.pfeiffer@tum.de)

suitable as
  • Master’s Thesis Applied and Engineering Physics
Supervisor: Franz Pfeiffer
Distortion correction for high-resolution quantitative X-ray imaging detectors

X-ray imaging detectors - in particular for high-resolution microscopy applications - may suffer from distortions, which degrade the image quality. This can have severe negative effects for quantitative applications, such as 3D micro-computed tomography. This project focuses on the characterisation of distortions of several X-ray imaging detectors at the Munich Compact Light Source, and the subsequent development of suitable correction methods.

Character of thesis work: mainly computational (image processing)  

For more information, please contact: Martin Dierolf (martin.dierolf@tum.de), Johannes Melcher (johannes.melcher@tum.de), or Franz Pfeiffer (franz.pfeiffer@tum.de)

suitable as
  • Master’s Thesis Biomedical Engineering and Medical Physics
Supervisor: Franz Pfeiffer
Estimation of Compton scattering in X-ray imaging using neural networks

Compton scattering is one of the two primary interactions of X-rays with matter in X-ray imaging (next to photoelectric absorption). In contrast to photoelectric absorption, Compton scattering is an inelastic scattering process during which X-ray photons are deflected and transfer some of their energy to the interaction partner, typically an electron. As a consequence, photons may still reach the detector after Compton interaction. In X-ray imaging, this leads to a smoothly varying background, which reduces contrast and is detrimental to quantitative imaging. Furthermore, the Compton scatter background is not uniform, but instead depends on the materials and their distribution within an imaged object. This makes a straight-forward analytical correction difficult, and existing tools to estimate the Compton background are limited in their accuracy and applicability.

 

The goal of this thesis is to develop methods to a) estimate the Compton scattering background from simple radiographs, and b) correct these images for it. This will be done using machine learning, in particular convolutional neural networks. The student will generate Monte-Carlo simulations based on the Geant4 platform, design and train neural networks, apply them for Compton scatter correction on clinical radiography images, and compare the results to existing approaches.

 

The project involves mostly data preparation, and computational work (85%, primarily Python, with potentially some C++ for Geant4), as well as experimental data collection (15%). The project will involve collaboration with the Radiology department at the TUM Hospital Klinikum rechts der Isar.

 

Basic experience in scientific programming, Monte-Carlo simulations, neural networks, and/or X-ray imaging are desirable.

 

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
Estimation of Compton scattering in X-ray imaging using neural networks

Compton scattering is one of the two primary interactions of X-rays with matter in X-ray imaging (next to photoelectric absorption). In contrast to photoelectric absorption, Compton scattering is an inelastic scattering process during which X-ray photons are deflected and transfer some of their energy to the interaction partner, typically an electron. As a consequence, photons may still reach the detector after Compton interaction. In X-ray imaging, this leads to a smoothly varying background, which reduces contrast and is detrimental to quantitative imaging. Furthermore, the Compton scatter background is not uniform, but instead depends on the materials and their distribution within an imaged object. This makes a straight-forward analytical correction difficult, and existing tools to estimate the Compton background are limited in their accuracy and applicability.

 

The goal of this thesis is to develop methods to a) estimate the Compton scattering background from simple radiographs, and b) correct these images for it. This will be done using machine learning, in particular convolutional neural networks. The student will generate Monte-Carlo simulations based on the Geant4 platform, design and train neural networks, apply them for Compton scatter correction on clinical radiography images, and compare the results to existing approaches.

 

The project involves mostly data preparation, and computational work (85%, primarily Python, with potentially some C++ for Geant4), as well as experimental data collection (15%). The project will involve collaboration with the Radiology department at the TUM Hospital Klinikum rechts der Isar.

 

Basic experience in scientific programming, Monte-Carlo simulations, neural networks, and/or X-ray imaging are desirable.

 

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
High-sensitivity grating-based phase-contrast imaging at the Munich Compact Light Source - Computational Part

Using phase-contrast as alternative imaging contrast for X-rays can considerably improve the imaging results for biomedical specimens. This project will focus on the development of an algorithmic framework for a high-sensitivity and high-resolution grating-based phase-contrast micro-tomography setup at the Munich Compact Light Source for investigating soft-tissue biomedical samples, such biopsies. 

Character of thesis work: mainly computational (image processing/ reconstruction)  

For more information, please contact: Martin Dierolf (martin.dierolf@tum.de), Johannes Brantl (johannes.brantl@tum.de), or Franz Pfeiffer (franz.pfeiffer@tum.de)

suitable as
  • Master’s Thesis Biomedical Engineering and Medical Physics
Supervisor: Franz Pfeiffer
High-sensitivity grating-based phase-contrast imaging at the Munich Compact Light Source - Computational Part

Using phase-contrast as alternative imaging contrast for X-rays can considerably improve the imaging results for biomedical specimens. This project will focus on the development of an algorithmic framework for a high-sensitivity and high-resolution grating-based phase-contrast micro-tomography setup at the Munich Compact Light Source for investigating soft-tissue biomedical samples, such biopsies. 

Character of thesis work: mainly computational (image processing/ reconstruction)  

For more information, please contact: Martin Dierolf (martin.dierolf@tum.de), Johannes Brantl (johannes.brantl@tum.de), or Franz Pfeiffer (franz.pfeiffer@tum.de)

suitable as
  • Master’s Thesis Applied and Engineering Physics
Supervisor: Franz Pfeiffer
High-sensitivity grating-based phase-contrast imaging at the Munich Compact Light Source - Experimental Part
Using phase-contrast as alternative imaging contrast for X-rays can considerably improve the imaging results for biomedical specimens. This project will focus on the experimental construction of a high-sensitivity and high-resolution grating-based phase-contrast micro-tomography setup at the Munich Compact Light Source for investigating soft-tissue biomedical samples, such as biopsies. Character of thesis work: mainly experimental For more information, please contact: Martin Dierolf (martin.dierolf@tum.de), Johannes Brantl (johannes.brantl@tum.de), or Franz Pfeiffer (franz.pfeiffer@tum.de)
suitable as
  • Master’s Thesis Applied and Engineering Physics
Supervisor: Franz Pfeiffer
High-sensitivity grating-based phase-contrast imaging at the Munich Compact Light Source - Experimental Part
Using phase-contrast as alternative imaging contrast for X-rays can considerably improve the imaging results for biomedical specimens. This project will focus on the experimental construction of a high-sensitivity and high-resolution grating-based phase-contrast micro-tomography setup at the Munich Compact Light Source for investigating soft-tissue biomedical samples, such as biopsies. Character of thesis work: mainly experimental For more information, please contact: Martin Dierolf (martin.dierolf@tum.de), Johannes Brantl (johannes.brantl@tum.de), or Franz Pfeiffer (franz.pfeiffer@tum.de)
suitable as
  • Master’s Thesis Biomedical Engineering and Medical Physics
Supervisor: Franz Pfeiffer
Implementation of a grating-based interferometer for X-ray vector radiography at the Munich Compact Light Source

Grating-based X-ray dark-field (DF) 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. The fact that the used gratings typically are one-dimensional can be leveraged to obtain an orientation dependent dark-field signal in a technique called X-ray vector radiography (XVR). Applications of XVR include the determination of the fibre orientation in reinforced composite materials, or characterization of the anisotropic structure in trabecular bones.

The goal of this project is to implement an experimental XVR setup at the Munich Compact Light Source (MuCLS - https://www.bioengineering.tum.de/en/central-building/munich-compact-light-source). The student will help with the design, implementation, and characterization of the X-ray grating interferometer setup, and conduct their own XVR experiments.

Character of thesis work: experimental lab work/ controls/ data acquisition (50%) & computational/ simulation/image processing (50%)

Basic experience in X-ray imaging, and/or Python programming are desirable.

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
Implementation of a grating-based interferometer for X-ray vector radiography at the Munich Compact Light Source

Grating-based X-ray dark-field (DF) 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. The fact that the used gratings typically are one-dimensional can be leveraged to obtain an orientation dependent dark-field signal in a technique called X-ray vector radiography (XVR). Applications of XVR include the determination of the fibre orientation in reinforced composite materials, or characterization of the anisotropic structure in trabecular bones.

The goal of this project is to implement an experimental XVR setup at the Munich Compact Light Source (MuCLS - https://www.bioengineering.tum.de/en/central-building/munich-compact-light-source). The student will help with the design, implementation, and characterization of the X-ray grating interferometer setup, and conduct their own XVR experiments.

Character of thesis work: experimental lab work/ controls/ data acquisition (50%) & computational/ simulation/image processing (50%)

Basic experience in X-ray imaging, and/or Python programming are desirable.

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
Radioluminescence microscopy for organoid specimens

Radioluminescence microscopy (RLM) is a novel approach for high-resolution imaging of the radionuclide uptake in living cells, particularly in organoid systems. This project will focus on the development of an experimental setup, which allows imaging the radionuclide distribution with a few micro-meter resolution, using a scintillator-lens CCD system. This project will be carried out in collaboration with the department of nuclear medicine at the TUM university hospital Klinikum rechts der Isar.

Character of thesis work: experimental (50%) / computational (50%)

For more information, please contact: Martin Dierolf (martin.dierolf@tum.de), Franz Pfeiffer (franz.pfeiffer@tum.de)

suitable as
  • Master’s Thesis Applied and Engineering Physics
Supervisor: Franz Pfeiffer
Radioluminescence microscopy for organoid specimens

Radioluminescence microscopy (RLM) is a novel approach for high-resolution imaging of the radionuclide uptake in living cells, particularly in organoid systems. This project will focus on the development of an experimental setup, which allows imaging the radionuclide distribution with a few micro-meter resolution, using a scintillator-lens CCD system. This project will be carried out in collaboration with the department of nuclear medicine at the TUM university hospital Klinikum rechts der Isar.

Character of thesis work: experimental (50%) / computational (50%)

For more information, please contact: Martin Dierolf (martin.dierolf@tum.de), Franz Pfeiffer (franz.pfeiffer@tum.de)

suitable as
  • Master’s Thesis Biomedical Engineering and Medical Physics
Supervisor: Franz Pfeiffer
Spectral material decomposition with dual-energy, photon-counting X-ray detectors for industrial applications

Despite being meanwhile well established and broadly available in medical imaging applications, spectral material decomposition using photon-counting hybrid pixel detectors is presently still underused in industrial imaging tasks. The main goal of this project is therefore to translate the existing theoretical and experimental knowledge from photon-counting biomedical imaging applications to industrial inspection applications. The work includes numerical programming tasks, such as implementing and refining decomposition algorithms,  and experimental tasks, such as taking several best practice measurement to classify different material separation applications for industrial end-users.

This master thesis will be carried out in collaboration with an external industrial collaborator located in the Munich area.

Character of thesis work: experimental physics (50%) & image processing (50%)

For more information, please contact: Franz Pfeiffer (franz.pfeiffer@tum.de)

suitable as
  • Master’s Thesis Applied and Engineering Physics
Supervisor: Franz Pfeiffer
Spectral material decomposition with dual-energy, photon-counting X-ray detectors for industrial applications

Despite being meanwhile well established and broadly available in medical imaging applications, spectral material decomposition using photon-counting hybrid pixel detectors is presently still underused in industrial imaging tasks. The main goal of this project is therefore to translate the existing theoretical and experimental knowledge from photon-counting biomedical imaging applications to industrial inspection applications. The work includes numerical programming tasks, such as implementing and refining decomposition algorithms,  and experimental tasks, such as taking several best practice measurement to classify different material separation applications for industrial end-users.

This master thesis will be carried out in collaboration with an external industrial collaborator located in the Munich area.

Character of thesis work: experimental physics (50%) & image processing (50%)

For more information, please contact: 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

Biomedical Applications using Fast-Switching Silver-KES Imaging at the Munich Compact Light Source
Abschlussarbeit im Masterstudiengang Biomedical Engineering and Medical Physics
Themensteller(in): Franz Pfeiffer
Deep Learning Based Denoising for Sparse Sampling Lung CT
Abschlussarbeit im Masterstudiengang Physics (Applied and Engineering Physics)
Themensteller(in): Franz Pfeiffer
Exploring the degeneration of intervertebral discs using staining based X-ray CT imaging
Abschlussarbeit im Masterstudiengang Physics (Applied and Engineering Physics)
Themensteller(in): Franz Pfeiffer
Quantitative dual-energy dental CT and Panoramic Imaging
Abschlussarbeit im Masterstudiengang Physics (Applied and Engineering Physics)
Themensteller(in): Franz Pfeiffer
Quantitative myocardial perfusion Imaging using Dual-Layer CT
Abschlussarbeit im Masterstudiengang Biomedical Engineering and Medical Physics
Themensteller(in): Franz Pfeiffer
X-ray diffraction imaging at a compact synchrotron source
Abschlussarbeit im Masterstudiengang Biomedical Engineering and Medical Physics
Themensteller(in): Franz Pfeiffer
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