Positron Emission Tomography (PET) is an imaging technique that uses biologically relevant molecules labelled with positron emitting radioisotopes to measure regional tissue function in living organisms. To maximise the detection efficiency, data are acquired in 3D, that is, all possible detector combinations in a scanner without inter-ring shielding (septa). The gain in sensitivity afforded by 3D PET is offset by the increase in random coincidences, scattered coincidences and deadtime. These problems must be overcome for the gain in sensitivity to be fully realised. The aim of this research project was to investigate strategies to increase the signal to noise ratio of the 3D PET data. Additional side shielding, both in neuro and body scanning, has been implemented and assessed. Large gains were achieved using the neuro shields in experimental and clinical studies. The potential of the body shields was tested in experimental and in-vivo studies which showed that they were scan dependent. For example, no gain was found for a cardiac blood flow (H2 IS0) study. A model-based scatter correction was assessed by companng compartment ratios within the 'Utah' phantom with radioactivity outside the field of view, with and without neuroshielding. Recovered ratios were within 6% of their actual values. The integration time was reduced in an effort to decrease the system deadtime. A peak increase of 150/0 in noise equivalent count rate was measured for a uniform cylinder inside the field of view. A random coincidence variance reduction technique was implemented and assessed to reduce the noise contained in the delayed window random coincidence estimate. The algorithm was evaluated using phantoms and tested on clinical data. A mean 16% reduction in coefficient of variation was measured for a C15O torso study.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:322456 |
| Date | January 2000 |
| Creators | Miller, Matthew P. |
| Publisher | University of Surrey |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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