MLE (Maximum Likelihood Estimator) reconstruction of a brain phantom using a Monte Carlo transition matrix and a statistical stopping rule

Title: MLE (Maximum Likelihood Estimator) reconstruction of a brain phantom using a Monte Carlo transition matrix and a statistical stopping rule

Full Record
Other Related Research

Abstract

In order to study properties of the Maximum Likelihood Estimator (MLE) algorithm for image reconstruction in Positron Emission Tomographyy (PET), the algorithm is applied to data obtained by the ECAT-III tomograph from a brain phantom. The procedure for subtracting accidental coincidences from the data stream generated by this physical phantom is such that he resultant data are not Poisson distributed. This makes the present investigation different from other investigations based on computer-simulated phantoms. It is shown that the MLE algorithm is robust enough to yield comparatively good images, especially when the phantom is in the periphery of the field of view, even though the underlying assumption of the algorithm is violated. Two transition matrices are utilized. The first uses geometric considerations only. The second is derived by a Monte Carlo simulation which takes into account Compton scattering in the detectors, positron range, etc. in the detectors. It is demonstrated that the images obtained from the Monte Carlo matrix are superior in some specific ways. A stopping rule derived earlier and allowing the user to stop the iterative process before the images begin to deteriorate is tested. Since the rule is based on the Poisson assumption, it does not work wellmore »« less

Authors:: Veklerov, E; Llacer, J; Hoffman, E J

Publication Date:: 1987-10-01

Research Org.:: Lawrence Berkeley Lab., CA (USA); California Univ., Los Angeles (USA). Dept. of Radiological Sciences

OSTI Identifier:: 5205389

Report Number(s):: LBL-23357; CONF-871006-51
ON: DE88008219

DOE Contract Number:: AC03-76SF00098

Resource Type:: Conference

Resource Relation:: Conference: 34. nuclear science symposium and 19. nuclear power systems symposium, San Francisco, CA, USA, 21 Oct 1987

Country of Publication:: United States

Language:: English

Subject:: 62 RADIOLOGY AND NUCLEAR MEDICINE; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; BRAIN; IMAGES; POSITRON COMPUTED TOMOGRAPHY; ALGORITHMS; COMPUTERIZED SIMULATION; IMAGE PROCESSING; MATRICES; MONTE CARLO METHOD; PHANTOMS; STATISTICS; BODY; CENTRAL NERVOUS SYSTEM; COMPUTERIZED TOMOGRAPHY; DIAGNOSTIC TECHNIQUES; EMISSION COMPUTED TOMOGRAPHY; MATHEMATICAL LOGIC; MATHEMATICS; MOCKUP; NERVOUS SYSTEM; ORGANS; PROCESSING; SIMULATION; STRUCTURAL MODELS; TOMOGRAPHY; 550602* - Medicine- External Radiation in Diagnostics- (1980-); 990220 - Computers, Computerized Models, & Computer Programs- (1987-1989)

Citation Formats


                    Veklerov, E, Llacer, J, and Hoffman, E J. MLE (Maximum Likelihood Estimator) reconstruction of a brain phantom using a Monte Carlo transition matrix and a statistical stopping rule.  United States: N. p., 1987. 
        Web.

Copy to clipboard


                    Veklerov, E, Llacer, J, & Hoffman, E J. MLE (Maximum Likelihood Estimator) reconstruction of a brain phantom using a Monte Carlo transition matrix and a statistical stopping rule.  United States.

Copy to clipboard


                    Veklerov, E, Llacer, J, and Hoffman, E J. Thu .  
        "MLE (Maximum Likelihood Estimator) reconstruction of a brain phantom using a Monte Carlo transition matrix and a statistical stopping rule".  United States.  https://www.osti.gov/servlets/purl/5205389.

Copy to clipboard


                    
@article{osti_5205389,

  title        = {MLE (Maximum Likelihood Estimator) reconstruction of a brain phantom using a Monte Carlo transition matrix and a statistical stopping rule},

  author       = {Veklerov, E and Llacer, J and Hoffman, E J},

  abstractNote = {In order to study properties of the Maximum Likelihood Estimator (MLE) algorithm for image reconstruction in Positron Emission Tomographyy (PET), the algorithm is applied to data obtained by the ECAT-III tomograph from a brain phantom. The procedure for subtracting accidental coincidences from the data stream generated by this physical phantom is such that he resultant data are not Poisson distributed. This makes the present investigation different from other investigations based on computer-simulated phantoms. It is shown that the MLE algorithm is robust enough to yield comparatively good images, especially when the phantom is in the periphery of the field of view, even though the underlying assumption of the algorithm is violated. Two transition matrices are utilized. The first uses geometric considerations only. The second is derived by a Monte Carlo simulation which takes into account Compton scattering in the detectors, positron range, etc. in the detectors. It is demonstrated that the images obtained from the Monte Carlo matrix are superior in some specific ways. A stopping rule derived earlier and allowing the user to stop the iterative process before the images begin to deteriorate is tested. Since the rule is based on the Poisson assumption, it does not work well with the presently available data, although it is successful wit computer-simulated Poisson data.},

  doi          = {},

  url          = {https://www.osti.gov/biblio/5205389},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {1987},

  month        = {10}

}

Copy to clipboard

Conference:

View Conference (0.22 MB)

Other availability

Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:

Export Metadata

Save to My Library

Similar records in OSTI.GOV collections:

MLE reconstruction of a brain phantom using a Monte Carlo transition matrix and a statistical stopping rule

Conference Veklerov, E ; Llacer, J ; Hoffman, E J - IEEE Trans. Nucl. Sci.; (United States)

In order to study properties of the Maximum Likelihood Estimator (MLE) algorithm for image reconstruction in Positron Emission Tomography (PET), the algorithm is applied to data obtained by the ECAT-III tomograph from a brain phantom. The procedure for subtracting accidental coincidences from the data stream generated by this physical phantom is such that the resultant data are not Poisson distributed. This makes the present investigation different from other investigations based on computer-simulated phantoms. It is shown that the MLE algorithm is robust enough to yield comparatively good images, especially when the phantom is in the periphery of the field ofmore »« less
The maximum likelihood estimator method of image reconstruction: Its fundamental characteristics and their origin

Conference Llacer, J ; Veklerov, E

We review our recent work characterizing the image reconstruction properties of the MLE algorithm. We studied its convergence properties and confirmed the onset of image deterioration, which is a function of the number of counts in the source. By modulating the weight given to projection tubes with high numbers of counts with respect to those with low numbers of counts in the reconstruction process, we have confirmed that image deterioration is due to an attempt by the algorithm to match projection data tubes with high numbers of counts too closely to the iterative image projections. We developed a stopping rulemore »« less
Full Text Available
Towards a practical implementation of the MLE algorithm for Positron Emission Tomography

Conference Llacer, J ; Andreae, S ; Veklerov, E ; ...

Recognizing that the quality of images obtained by application of the Maximum Likelihood Estimator (MLE) to Positron Emission Tomography (PET) and Single Photon Emission Tomography (SPECT) appears to be substantially better than those obtained by conventional methods, we have started to develop methods that will facilitate the necessary research for a good evaluation of the algorithm and may lead to its practical application for research and routine tomography. We have found that the non-linear MLE algorithm can be used with pixel sizes which are smaller than the sampling distance, without interpolation, obtaining excellent resolution and no noticeable increase in noise.more »« less
Towards a practical implementation of the MLE algorithm for positron emission tomography

Conference Llacer, J ; Andreae, S ; Veklerov, E ; ... - IEEE Trans. Nucl. Sci.; (United States)

Recognizing that the quality of images obtained by application of the Maximum Likelihood Estimator (MLE) to Positron Emission Tomography (PET) and Single Photon Emission Tomography (SPECT) appears to be substantially better than those obtained by conventional methods, the authors have started to develop methods that will facilitate the necessary research for a good evaluation of the algorithm and may lead to its practical application for research and routine tomography. They have found that the non-linear MLE algorithm can be used with pixel sizes which are smaller than the sampling distance, without interpolation, obtaining excellent resolution and no noticeable increase inmore »« less
Maximum-likelihood reconstruction of transmission images in emission computed tomography via the EM algorithm

Journal Article Ollinger, J M - IEEE Transactions on Medical Imaging (Institute of Electrical and Electronics Engineers); (United States)

The expectation-maximization (EM) algorithm for computing maximum-likelihood estimates of transmission images in positron-emission tomography (PET) is extended to include measurement error, accidental coincidences and Compton scatter. A method for accomplishing the maximization step using one step of Newton's method is proposed. The algorithm is regularized with the method of sieves. Evaluations using both Monte Carlo simulations and phantom studies on the Siemens 953B scanner suggest that the algorithm yields unbiased images with significantly lower variances than filtered-backprojection when the images are reconstructed to the intrinsic resolution. Large features in the images converge in under 200 iterations while the smallest featuresmore »« less
https://doi.org/10.1109/42.276147

Similar Records