Diffusion driven reconstruction for positron emission tomography

Reconstruction map of the radionucleus distribution in positron emission tomography imaging is an ill-posed inverse problem. Existing reconstruction methodologies based on maximum a posteriori principle reconstruct images that suffer from artifacts. A method is proposed for generating better map of radionucleus distribution primarily based on surface diffusion model. The mean curvature diffusion has resulted in efficient noise suppression when applied to an isolated edge embedded in noise. Experimental results show that the proposed approach has better image resolution and edge-preservation capability. The reconstruction methodology will therefore find applications in biomedical imaging and bioengineering.