CROSS-REFERENCE MAXIMUM LIKELIHOOD ESTIMATE RECONSTRUCTION FOR POSITRON EMISSION TOMOGRAPHY

Cross-Reference Maximum Likelihood Estimates for Positron Emission Tomography

Maximum likelihood estimate (MLE) is a widely-used approach for PET image reconstruction. However, it has been shown that reconstructing emission tomography based on MLE without regularization would result in noise and edge artifacts. In the attempt to regularize the maximum likelihood estimate , in this paper, we propose a new and eecient method to incorporate the correlated but possibly incom...

Accelerated Cross-Reference Maximum Likelihood Estimates for Positron Emission Tomography

The state of art of positron emission tomography (PET) takes into account the accidental coincidence events and attenuation. The maximum likelihood estimator can handle this kind of random variation in the reconstruction of a PET image. However, the reconstruction is ill-posed and needs regularization. The boundary information, either from an expert or from the other medical modality of the sam...

Maximum Likelihood Algorithms for Image Reconstruction in Positron Emission Tomography

Simultaneous Reconstruction in Dynamic Positron Emission Tomography

In this paper we study the problem of reconstructing simultaneously a sequence of images in a dynamic PET study. In our algorithm we will make use of the fact that the morphology does not change by much between adjacent frames. Together with a total variation prior this will be shown to reduce the noise level considerably, without corrupting the individuality of the frames. Simulation results a...

A New Algorithm for Image Reconstruction for Positron Emission Tomography

In Positron Emission Tomography, penalized iterative algorithms like MAP often results in over smooth reconstructions due to over penalizing nature of the assumed interacting potential. These algorithms fail to determine the density class of the estimate and hence penalize the pixels irrespective of the density class. In this work, a fuzzy logic based approach is proposed to model the prior whi...