Dynamic PET Image Reconstruction Incorporating Multiscale Superpixel Clusters
نویسندگان
چکیده
Dynamic positron emission tomography (PET) image reconstruction is challenging due to the low-count statistics of individual frames. This study proposes a novel framework aiming enhance quantitative accuracy dynamic frames via introduction priors based on multiscale superpixel clusters. The clusters are derived from pre-reconstruction composite images using clustering followed by fuzzy c-means (FCM) clustering. A aggregation exploited during generate Then, maximum posteriori (MAP) PET with different-scale separately applied frame and fused final result. Using realistic simulated brain data, performance proposed method investigated compared maximum-likelihood expectation-maximization (MLEM), Bowsher method, kernelized (the kernel method). achieves substantial improvements in both visual (in terms signal-to-noise ratio contrast versus noise performances). also tested 60 min 18 F-FDG rat performed an Inveon small animal scanner. shown outperform other methods mean intensity region interest).
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2021
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2021.3058807