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Optimization of reconstruction parameters in [18F]FDG PET brain images aiming scan time reduction

Iterative image reconstruction methods are widely used in PET due to their better image quality when compared to analytical methods. However, inaccurate quantification occurs in low activity concentration regions, which leads to biased quantification of PET images. The diagnosis of some neurodegenerative diseases, such as Alzheimer’s disease, is based on identifying such low-uptake regions. Furthermore, PET imaging in these populations should be as short as possible to limit head movements and improve patient comfort. This work aims to identify optimized reconstruction parameters of [18F]FDG PET brain images aiming to reduce image acquisition time with minimal impact on quantification. For this, [18F]FDG PET images of a Hoffman 3-D brain phantom were acquired. Analytical and iterative reconstruction methods were compared utilizing image quality and quantitative accuracy metrics. OSEM reconstruction algorithm was optimized (4 iterations and 32 subsets). It resulted in remarkably similar images compared to the current clinical settings, with a 50% reduction in scan time (5 min with a post-reconstruction filter of 4 mm). Future clinical studies are needed to confirm the results presented here.