COmpton Scatter based Imaging for Nuclear Emissions

forthcoming

The main goal of my proposal is to exploit Compton scattering in PET imaging, a nuclear imaging technique well-established in clinical routine, mainly in oncology as a tool for tumor diagnosis and monitoring, but also through preclinical PET as an essential tool for pharmacology and drug development. By considering signal events undergoing Compton scattering, I expect to increase the sensitivity of PET systems, and so improve the attainable image quality. Sensitivity in PET systems is a key parameter to be optimized, as it could allow for dose exposure and/or scan time reduction, in addition to being critical in low-count regime scenarios, for pediatric patients, and in research applications involving small-animal imaging. Aside from increasing the sensitivity, the purposeful detection of Compton scattering in PET imaging opens the door for the use of this modality for new applications (e.g. immunoPET, theranostics...). However, incorrect/simplified modeling of these events can degrade PET image quality. My proposal relies on developing algorithms and models based on solid mathematical and physical foundations, able to include events involving Compton scattering into dedicated reconstruction frameworks. To this end, I intend to use the knowledge I have acquired during my PhD developing similar models and algorithms for imaging with Compton cameras. Specifically, I intend to address three questions: first, the inclusion of inter-crystal scatter events with particular focus on small-animal PET imaging; second, the modeling of object scattered events and their use in low-count regime scenarios; and third, the treatment of coincidence events that involve an additional emission of photons, either correlated or uncorrelated to the positron emission. The evaluation of the proposed methods will be carried out using both Monte-Carlo simulations and experimental PET data, for which the facilities and technologies developed by the host team in Lübeck will be employed.