PET Image Reconstruction
Motivation
Positron-Emission-Tomography is a functional medical imaging technique with proven utility in fields as oncology, cardiology, and neurology. By using radioactive tracers, PET allows for the detailed visualization of physiological processes at the molecular level. Our research focuses on developing new reconstruction methods and improving image quality by improving scanner calibration and normalization.
Research Topics
PET scanners are highly complex systems in which image quality critically depends on both accurate system calibration and robust image reconstruction. In practice, deviations from ideal system behaviour directly propagate into reconstruction artifacts and quantification errors. Our research treats calibration and reconstruction as a single, coupled problem with the goal of improving image quality under realistic imaging conditions.
In-System Calibration
Following assembly, PET systems exhibit deviations from their ideal configuration due to detector positioning errors, orientation mismatches, and variations in detector response. These effects influence gamma interaction positioning, timing, and energy estimation, and must be corrected to ensure consistent system behaviour.
We focus on in-system calibration of the complete scanner, where all relevant parameters are estimated directly from measured data after assembly. This enables a coherent and physically motivated system model, which forms the basis for reliable image reconstruction.
By shifting complexity from hardware precision to data-driven calibration, we aim to enable faster and more cost-efficient scanner manufacturing with relaxed hardware constraints, while maintaining high imaging performance.
Figure 1: Lightspreads and positioned coincidences visualized for a PET system build at the institute.
Novel Reconstruction Techniques
Tomographic image reconstruction is formulated as the solution of an inverse problem governed by the system’s forward projection model. In practice, the accuracy of this model is limited by residual calibration uncertainties and measurement imperfections.
We develop reconstruction approaches that improve robustness, quantitative accuracy, and computational efficiency in realistic acquisition scenarios. This includes the integration of prior information, the handling of incomplete or limited-angle data, and the development of data-driven and physics-informed models for image formation.
The goal is to enable consistent and robust image reconstruction that accounts for systematic uncertainties and improves diagnostic accuracy.
Hybrid Imaging
In PET/MR systems, complementary structural and functional information from MRI can be directly integrated into PET reconstruction.
We investigate multimodal reconstruction approaches that leverage MR information to guide image formation, incorporate anatomical priors, account for motion, and estimate physical effects such as attenuation in a data-driven, physics-consistent manner.
Figure 2: Schematic illustration of the tomographic imaging modalities investigated at the institute.
Thesis Topics
We offer various thesis topics on this project. We are looking for highly motivated and creative people with a strong background in physics, mathematics, electrical engineering, computer science or related fields. We offer an interdisciplinary working environment where you can gather hands-on experience to the field of PET imaging and image reconstruction.
