Exploiting photon energy information in Positron Emission Tomography

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Positron Emission Tomography (PET) imaging forms a crucial part of managing the patient's treatment pathway in cancer and many other diseases. However, due to limitations on radioactive dose to the patient, PET images can be noisy and with relatively low image resolution. Modern PET scanners have experimental capabilities to measure gamma photon energy information in much more detail than before, which has the potential to increase signal to noise (SNR) ratio, as shown in preliminary scientific papers. However, further work is needed before this can be progressed to commercial systems.This project aims to evaluate the impact of various methods to exploit additional photon energy information on image quality, as well as optimise the most promising candidates. Aspects to be included are scatter rejection by using energy window tuning, photon scatter estimation, accidental coincidences, as well as image reconstruction strategies. We envisage combining established PET data processing with novel techniques using Machine Learning.This project will extend previous work on using photon energy in PET and SPECT. Previous publications have investigated the potential for a decrease in noise or the joint estimation of attenuation and emission images. We intend to combine this by investigating optimal energy windows providing a trade-off between accuracy and precision in the resulting estimates.This project fits primarily in the second Challenge of the EPSRC health technologies theme : Transforming early prediction and diagnosis.