Positron emission tomography – computed tomography (PET-CT) is a non-invasive diagnostic tool that is widely used in oncology imaging. High quality diagnostic images and quantitative accuracy are often restricted by image noise, adequate spatial resolution and contrast ratio.

Ordered Subset Expectation Maximisation (OSEM) is a widely used statistical iterative reconstruction algorithm in PET-CT due to its dependability, reconstruction quality and adequate signal-to-noise ratio. However, OSEM requires a large number of iterations to achieve high quantitative accuracy which results in increasing image noise. A novel algorithm, HYPER DPR (developed by United Imaging Healthcare) is an artificial intelligence-based reconstruction method that aims to provide increased sensitivity, higher spatial resolution and less noise.

This study evaluates the accuracy and sensitivity of HYPER DPR against OSEM using reconstructed images from analog and digital PET-CT. Results demonstrate that both OSEM and HYPER DPR reconstruction algorithms in digital PET-CT has greater spatial resolution, increased detection sensitivity and less image noise when compared to analog PET-CT. Digital PET-CT and HYPER DPR enables better small lesion detection and increased resolution, thus resulting in better disease detection and improved patient management. Increased sensitivity of digital PET-CT results in low dose scans from reduced radiotracer injections, therefore having higher patient output.

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