Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Doctor of Philosophy

Program

Medical Biophysics

Collaborative Specialization

Molecular Imaging

Supervisor

Lee, Ting-Yim

2nd Supervisor

Koropatnick, James

Joint Supervisor

Abstract

PET with targeted probes may better elucidate the molecular and functional basis of diseases. The widely used standardized uptake value from static imaging, however, cannot quantify the probe uptake processes like perfusion, permeability, binding to and disassociation (k4) from target. The overarching thesis goal is to develop a model to enable kinetic analysis of dynamic imaging to separate these processes.

As perfusion delivery is not modelled in the current standard two tissue compartment (S2TC) model, I developed a flow modified two tissue compartment (F2TC) model that incorporates the blood flow effect. The model’s performances were investigated with simulation. It was applied to derive kinetic parameters of [18F]FAZA binding to highly hypoxic pancreatic cancer. As a validation, the distribution volume (DV) of [18F]FAZA determined with the F2TC and S2TC model were compared with graphical analysis (GA). Kinetic analysis requires arterial concentration of the native probe to model the observed tissue uptake over time, therefore, a method was developed to correct for the metabolite contamination of arterial plasma.

Based on fractional Euclidean distance of estimated and simulated parameters, F2TC model performed better than S2TC model, particularly with longer mean transit time due to the neglect of perfusion effect in the latter model. Also, dynamic acquisition longer than 45 minutes did not improve the accuracy of estimated F2TC model parameters. In the pancreatic cancer study: (a) GA showed that [18F]FAZA was reversibly bound to hypoxic cells; (b) DV estimated by the F2TC and S2TC model was not and was significantly different from GA respectively; (c) k4 and DV estimated by F2TC model could distinguish normal and cancerous tissue with 95% sensitivity. TLC-autoradiography identified metabolites in 2µL of arterial plasma with radioactivity as low as 17Bq. This high sensitivity and the ability to measure multiple (8-12) samples simultaneously could allow metabolite correction of arterial plasma to be performed in individual studies.

Finally, the reversible binding of [18F]FAZA in hypoxic pancreatic tumor cells could be due to efflux of reduced products by the multidrug resistance protein. Therefore, kinetic analysis of dynamic [18F]FAZA PET could monitor both hypoxia and drug resistance for individualized treatment.

Summary for Lay Audience

PET is an imaging technique that uses targeted molecules (tracers) to monitor disease processes in the body. Currently, static “snapshot” imaging is used to image the tracer uptake at a single time following injection. Static imaging cannot differentiate the different dynamic processes involved in tracer uptake over time. Dynamic imaging acquired at multiple times post injection are required for the analysis of these dynamic processes, elucidation of which can improve our mechanistic understanding of disease. The overarching goal of my PhD research is to develop a mathematical model for the analysis of dynamic images. This analysis, also called kinetic analysis, requires measurement of the fraction of native (unmodified) tracer in blood plasma, therefore, I also developed a technique to measure such fraction in blood plasma.

The current mathematical model, standard two tissue compartment model (S2TCM), neglects the delivery of tracer by blood flow. I developed a flow modified two tissue compartment model (F2TCM) to explicitly take into account of this delivery effect. Computer simulation showed the F2TCM is better than S2TCM in more accurately measuring the processes involved in the uptake of the targeted tracer, therefore may be better in characterizing disease mechanisms. Furthermore, this improved analysis was achieved with 45 min of dynamic image acquisition.

The developed F2TCM was applied to pancreatic cancer to investigate the uptake of [18F]FAZA, a targeted tracer that binds to tumor cells deprived of oxygen (hypoxic), making them resistant to treatment. It was found that the tracer is not trapped in hypoxic cells as commonly believed and it could be pumped out of hypoxic tumor cells via the multidrug resistance protein on cell surface. Furthermore two parameters estimated with the F2TCM can identify pancreatic cancer with 95% sensitivity.

The developed technique can measured the fraction of native tracer in blood plasma using very small volume of very low radioactivity. Metabolite contamination of blood plasma has been plaguing the accuracy of kinetic analysis and calls for measurement of this contamination in individual patients. The high sensitivity and convenience of my technique opens up the possibility of measuring the plasma metabolite fraction for individual patients.

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