In this paper we formulate a new approach to medical image reconstruction from projections in emission tomography. This approach differs from traditional methods such as filtered back projection, maximum likelihood or maximum penalized likelihood. Our method is developed directly from the Bayes formula and the final result is an iterative algorithm, for which the maximum likelihood expectation-maximization is a special case.4 page(s
The use of anatomical information to improve the quality of reconstructed images in Positron Emissio...
The maximum-likelihood (ML) expectation-maximization (EM) [ML-EM] algorithm is being widely used for...
Region of interest (ROI) quantitation is an important task in emission tomography (e.g., positron em...
In this paper we formulate a new approach to medical image reconstruction from projections in emissi...
The development and tests of an iterative reconstruction algorithm for emission tomography based on ...
The maximum-likelihood (ML) approach in emission tomography provides images with superior noise char...
The maximum-likelihood (ML) approach in emission tomography provides images with superior noise char...
This article outlines the statistical developments that have taken place in the use of the EM algori...
The maximum a-posteriori (MAP) and maximum likelihood (ML) algorithm produces good reconstruction fo...
This paper proposes a new method for region of interest reconstruction in emission tomography assumi...
Stochastic methods based on Maximum Likelihood Estimation (MLE) provide accurate tomographic reconst...
Presented in this thesis are multiresolution image models and Bayesian algorithms for statistical im...
We present a new reconstruction algorithm for emission and transmission tomography. The algorithm pe...
In emission tomography maximum likelihood expectation maximization reconstruction technique has repl...
Abstract—In emission tomography, the Poisson statistics of the observations make penalized–likelihoo...
The use of anatomical information to improve the quality of reconstructed images in Positron Emissio...
The maximum-likelihood (ML) expectation-maximization (EM) [ML-EM] algorithm is being widely used for...
Region of interest (ROI) quantitation is an important task in emission tomography (e.g., positron em...
In this paper we formulate a new approach to medical image reconstruction from projections in emissi...
The development and tests of an iterative reconstruction algorithm for emission tomography based on ...
The maximum-likelihood (ML) approach in emission tomography provides images with superior noise char...
The maximum-likelihood (ML) approach in emission tomography provides images with superior noise char...
This article outlines the statistical developments that have taken place in the use of the EM algori...
The maximum a-posteriori (MAP) and maximum likelihood (ML) algorithm produces good reconstruction fo...
This paper proposes a new method for region of interest reconstruction in emission tomography assumi...
Stochastic methods based on Maximum Likelihood Estimation (MLE) provide accurate tomographic reconst...
Presented in this thesis are multiresolution image models and Bayesian algorithms for statistical im...
We present a new reconstruction algorithm for emission and transmission tomography. The algorithm pe...
In emission tomography maximum likelihood expectation maximization reconstruction technique has repl...
Abstract—In emission tomography, the Poisson statistics of the observations make penalized–likelihoo...
The use of anatomical information to improve the quality of reconstructed images in Positron Emissio...
The maximum-likelihood (ML) expectation-maximization (EM) [ML-EM] algorithm is being widely used for...
Region of interest (ROI) quantitation is an important task in emission tomography (e.g., positron em...