A portable PET image reconstruction system for parallel machines

With its unique diagnostic capabilities for imaging metabolic body functions, positron emission tomography (PET) has become the superior system among all noninvasive modalities for quantitatively evaluating physiology. Commonly-used reconstruction algorithms such as filtered backprojection (FBP) have been adopted due to their rapid computation but may compromise the resulting image quantitatively with inherent noise amplification. Expectation maximization (EM), an algorithm that includes the stochastic nature of the count data, has been proposed and proved to generate better images, but remains hard to use because of the high complexity of the algorithm. The EM reconstruction algorithm is becoming more feasible with parallel computers which are widely available in research organizations. Different schemes and high-cost hardware platforms have been introduced to speed up the reconstruction process but often incur high costs with their ad-hoc approaches. We introduce a hardware-independent parallel PET image reconstruction system with portable software implementation. To reduce the cost that comes with most parallel platforms, commercial off-the-shelf hardware and software components can be used to build a system with a low cost/performance ratio. The system architecture is also shown to be scalable with increasing image size to meet future needs.