PDEs on Graphs for Image Reconstruction on Positron Emission Tomography
A better quality of an image can be achieved through iterative image reconstruction for positron emission tomography (PET) as it employs spatial regularization that minimizes the difference of image intensity among adjacent pixels. In our previous works, we have proposed a simple method to solve PDEs on general images using the framework of PdEs (Partial difference Equations) on graphs. In this paper, we propose to apply morphological-based operators on graphs for processing of 2D PET images. We apply this approach for to remove noise from the raw projections data. The quality measurements and visual inspections show a significant improvement in image quality compared to conventional Algebraic Reconstruction Technique (ART).
KeywordsImage reconstruction PET Post-reconstruction ART algorithm PdE framework
A. Emoataz is supported by the ANR SUMUM (ANR-17-CE38-0004).
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