Image renovation in Positron Emission Tomography using recursive algorithm

This paper explains the image reconstruction in Positron Emission Tomography using Maximum a Posterior (MAP). Till date, Diagnostic reconstruction methods offer a direct mathematical solution for the edifice of an image. This tactic requires a minimization of a convex cost function which in turn res...

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Bibliographic Details
Main Authors: Arunprasath, T., Rajasekaran, M. P., Kannan, S., Pandian, R. B. M.
Format: Conference Proceeding
Language:English
Subjects:
bayesian parameters
Gibbs distribution
Image quality
Iteration Algorithm
Markov random field
PET Brain image
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Summary:This paper explains the image reconstruction in Positron Emission Tomography using Maximum a Posterior (MAP). Till date, Diagnostic reconstruction methods offer a direct mathematical solution for the edifice of an image. This tactic requires a minimization of a convex cost function which in turn results in many problems related to the computational difficulty. Further, Iterative techniques are based on a more accurate description of the imaging process resulting in a more complicated mathematical solution requiring multiple steps to attain the image. The practical technique used here is MAP repetition method. This statistical technique offers better and lowest normalized root mean square error (NRMSE) in the PET Brain replica. Various image quality constraints make it painstaking and time consuming to analyze the PET brain image in this procedure. The PET brain image is fabricated and pretend in MATLAB/Simulink package.
DOI:10.1109/ICCIC.2012.6510210