Quantitative applications in positron emission tomography achieved through signal modelling.

Positron emission tomography (PET) is a valuable tool in medical imaging, but it provides limited quantitative utility in a number of important applications, such as mapping of tracer accumulation in small tissues and quantitative assessment of factors affecting tracer uptake. We aimed to develop a quantification approach based on signal modelling, to address the above limitations. Our signal modelling approach allows for a comprehensive description of target and background signals. We used in silico simulations to exemplify the quantitative utility of signal modelling in a number of applications and conducted scans of standardized PET phantoms to validate our computer simulation algorithms. The simulations showed that the modelling approach allows applications not supported by current techniques, such as estimation of activity fractions of sub-resolution small tissues and accurate quantification of the effect of biological factors, such as hypoxia, on tracer accumulation. There was strong agreement between the simulation data and actual scans of phantoms, providing support for the validity of the simulation algorithms. We conclude that the presented signal modelling approach may provide a framework for image analysis that can improve and expand the quantitative capacity of PET imaging.