Development of high resolution, high sensitivity PET capability for awake and freely moving small animals
Access status:
Open Access
Type
ThesisThesis type
Doctor of PhilosophyAbstract
Positron emission tomography (PET) imaging in small animals, such as mice and rats, holds great potential for advancing our understanding of neurological disorders and evaluating new treatments. However, translating findings from animal models to clinical applications faces two ...
See morePositron emission tomography (PET) imaging in small animals, such as mice and rats, holds great potential for advancing our understanding of neurological disorders and evaluating new treatments. However, translating findings from animal models to clinical applications faces two main challenges: quantitative inaccuracies due to limited PET system performance and the small anatomical structures of these animals, and the need for anaesthesia. Accurate tracer quantification in brain regions requires PET scanners that provide both sub-millimetre resolution and high sensitivity. Furthermore, to avoid the confounding effects of anaesthesia, scanners capable of imaging awake, freely moving rodents are essential—yet no such system currently exists. The Open-Field Mouse Brain PET (mousePET) scanner aims to overcome these limitations. This thesis addresses three main challenges: (i) developing cost-effective solutions for scalable detector data acquisition, (ii) improving event positioning in the PET detectors, and (iii) creating efficient methods for depth-of-interaction (DOI) image reconstruction. The use of a low-cost application-specific integrated circuit (ASIC) for multiplexed detector data acquisition was investigated. The optimised mousePET detector achieved mean energy, DOI, and coincidence time resolutions of 10.5%, 2.3 mm, and 1.3 ns, respectively, demonstrating strong performance. Deep learning techniques were applied to further enhance event positioning accuracy. A deep neural network (DNN), trained via Monte Carlo simulations, improved positioning accuracy by 28% compared to traditional Anger logic. Finally, virtual cylindrical detector geometries were tested for efficient DOI image reconstruction, showing that optimising virtual geometry balances computational efficiency and spatial resolution. In conclusion, this thesis develops and rigorously evaluates critical components of the mousePET scanner, laying a strong foundation for its final development.
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See morePositron emission tomography (PET) imaging in small animals, such as mice and rats, holds great potential for advancing our understanding of neurological disorders and evaluating new treatments. However, translating findings from animal models to clinical applications faces two main challenges: quantitative inaccuracies due to limited PET system performance and the small anatomical structures of these animals, and the need for anaesthesia. Accurate tracer quantification in brain regions requires PET scanners that provide both sub-millimetre resolution and high sensitivity. Furthermore, to avoid the confounding effects of anaesthesia, scanners capable of imaging awake, freely moving rodents are essential—yet no such system currently exists. The Open-Field Mouse Brain PET (mousePET) scanner aims to overcome these limitations. This thesis addresses three main challenges: (i) developing cost-effective solutions for scalable detector data acquisition, (ii) improving event positioning in the PET detectors, and (iii) creating efficient methods for depth-of-interaction (DOI) image reconstruction. The use of a low-cost application-specific integrated circuit (ASIC) for multiplexed detector data acquisition was investigated. The optimised mousePET detector achieved mean energy, DOI, and coincidence time resolutions of 10.5%, 2.3 mm, and 1.3 ns, respectively, demonstrating strong performance. Deep learning techniques were applied to further enhance event positioning accuracy. A deep neural network (DNN), trained via Monte Carlo simulations, improved positioning accuracy by 28% compared to traditional Anger logic. Finally, virtual cylindrical detector geometries were tested for efficient DOI image reconstruction, showing that optimising virtual geometry balances computational efficiency and spatial resolution. In conclusion, this thesis develops and rigorously evaluates critical components of the mousePET scanner, laying a strong foundation for its final development.
See less
Date
2024Rights statement
The author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.Faculty/School
Faculty of Engineering, School of Biomedical EngineeringAwarding institution
The University of SydneyShare