Nowadays, Positron Emission Tomography (PET) plays an important role in medical diagnostics. This medical imaging method helps doctors to perceive cancer in its early stage. During the examination, the patient is injected with a contrast substance, which emits positrons during its decay process. A positron collides with an electron in the patient's body, which produces a photon pair. These photon pairs are registered by the detector. After the measurement, our task is to reconstruct the emission density distribution of the contrast substance from the registered detector hits.
By monitoring the changes in the density distribution of the contrast substance, we can exploit the potential in PET technology more profoundly. Using dynamic PET, we are able to analyze the mechanism of action and absorption of a newly developed drug. It is also useful in other areas, such as the examination of metabolic processes of the body.
In my thesis, I present the basics of PET technology and generalize the existing static reconstruction algorithm taking the time dimension into account. I implement this algorithm for the 2 dimension case, analyze the results, and make propositions for a 3 dimension solution in GPU environment.