Description of the research project:
Positron Emission Tomography (PET) is a non-invasive imaging technique that uses radioactive tracers to visualize and measure metabolic activity in the body. By detecting gamma rays from tracer interactions, PET creates detailed 3D images of biological processes. Dynamic PET goes a step further by capturing a series of images over time, providing both spatial and temporal insights into how tracers move and interact, revealing metabolic rates and mechanisms. However, PET imaging faces significant challenges: Reconstructiong a PET image from corresponding measurements requires to solve an inverse problem with a highly ill-posed forward model. Further, PET raw data is usually corrupted by particularly strong noise, which is even more pronounced in dynamic brain PET.
Our research group works on improving PET imaging in several aspects: In some of our works, such as [SHRVKBBN2017, SH2022, SHKBKBN2017, KHKOBS2017] we deal with modelling- and algorithmic aspects of PET image reconstruction, developing efficient algorithms and MR-prior based approaches to improve the quality of the reconstructed images. In other works, such as [HM2024, HM2025], we address more fundamental questions regarding the unique identifiability of physiological parameters in PET tracer kinetic modeling.
Associated publications:
[KHKOBS2017]
Florian Knoll and Martin Holler and Thomas Koesters and Richardo Otazo and Kristian Bredies and Daniel K Sodickson. Joint MR-PET reconstruction using a multi-channel image regularizer.
IEEE Transactions on Medical Imaging, 36(1):1-16,
2017
[SHRVKBBN2017]
Georg Schramm and Martin Holler and Ahmadreza Rezaei and Kathleen Vunckx and Florian Knoll and Kristian Bredies and Fernando Boada and Johan Nuyts. Evaluation of Parallel Level Sets and Bowsher's Method as Segmentation-Free Anatomical Priors for Time-of-Flight PET Reconstruction.
IEEE Transaction on Medical Imaging, 37(2):590 - 603,
2017
[SH2022]
Georg Schramm and Martin Holler. Fast and memory-efficient reconstruction of sparse Poisson data in listmode with non-smooth priors with application to time-of-flight PET.
Physics in Medicine \& Biology, 67(15):155020,
2022
[HM2024]
Holler, Martin and Morina, Erion and Schramm, Georg. Exact parameter identification in PET pharmacokinetic modeling using the irreversible two tissue compartment model.
Physics in Medicine & Biology, 69(16):165008,
IOP Publishing,
2024
[HM2025]
Holler, Martin and Morina, Erion and Schramm, Georg. Exact Parameter Identification in PET Pharmacokinetic Modeling - Extension to the Reversible Two Tissue Compartment Model.
Inverse Problems, 41(9):095009,
2025
[SHKBKBN2017]
Georg Schramm and Martin Holler and Thomas Koesters and Fernando Boada and Florian Knoll and Kristian Bredies and Johann Nuyts. PET reconstruction with non-smooth gradient-based priors.
Proceedings of the 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD),
2017
