Positron Emission Tomography is a powerful medical imaging modality
for investigating human and animal biochemistry and physiology.
Detection of photon pairs produced by positron-electron annihilation
produces tomographic projections of the spatial distribution of positron-emitting nuclei.
Tomographic reconstruction methods can then be used to form volumetric images.
By labelling biochemicals with positron-emitting nuclei, we can produce images of a wide
variety of biochemical and physiological processes. PET is now widely used in detecting and
staging cancer through imaging of glucose. In the brain, receptor and transmitter ligands have
been developed to study the dopamine and other neurochemical systems. The most exciting
recent development in PET is the ability to directly image gene expression through the use of
PET tracer/reporter gene combinations. In humans this technique can be used, for example,
to monitor the efficacy of gene therapy techniques. PET gene expression imaging is also being
increasingly used to study gene expression in transgenic animals. Essential to the success of
positron tomography in these diverse applications is a combination of instrumentation-design
optimized for specific applications (e.g. humans vs. small animals) and image processing methods
that maximize image quality when forming volumetric images from PET data. After reviewing the
instrumentation and principles behind PET, I will describe statistically-based approaches to
reconstructing PET images. Using a Bayesian formulation, we combine accurate physical models
of the physics underlying PET systems with accurate statistical models for photon limited data
collected in these systems. I will illustrate the impact of this approach on image quality through
examples for clinical and animal studies. I will also describe our current work on evaluating image
quality through a combination of theoretical, Monte-Carlo and human-observer studies.
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