MLE reconstruction of a brain phantom using a Monte Carlo transition matrix and a statistical stopping rule
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 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 with computer-simulated Poisson data.
- Research Organization:
- Lawrence Berkeley Lab., Univ. of California, Berkeley, CA (US)
- OSTI ID:
- 7057004
- Report Number(s):
- CONF-871006-; TRN: 88-024829
- Journal Information:
- IEEE Trans. Nucl. Sci.; (United States), Vol. 35:1; 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
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Related Subjects
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
BRAIN
PHANTOMS
IMAGE PROCESSING
POSITRON COMPUTED TOMOGRAPHY
ALGORITHMS
CALCULATION METHODS
COINCIDENCE METHODS
COMPTON EFFECT
COMPUTERIZED SIMULATION
IMAGE SCANNERS
ITERATIVE METHODS
MATRICES
MAXIMUM-LIKELIHOOD FIT
MONTE CARLO METHOD
POISSON EQUATION
RADIATION DETECTORS
BASIC INTERACTIONS
BODY
CENTRAL NERVOUS SYSTEM
COMPUTERIZED TOMOGRAPHY
COUNTING TECHNIQUES
DIAGNOSTIC TECHNIQUES
DIFFERENTIAL EQUATIONS
ELASTIC SCATTERING
ELECTROMAGNETIC INTERACTIONS
EMISSION COMPUTED TOMOGRAPHY
EQUATIONS
INTERACTIONS
MATHEMATICAL LOGIC
MEASURING INSTRUMENTS
MOCKUP
NERVOUS SYSTEM
NUMERICAL SOLUTION
ORGANS
PARTIAL DIFFERENTIAL EQUATIONS
PROCESSING
SCATTERING
SIMULATION
STRUCTURAL MODELS
TOMOGRAPHY
655003* - Medical Physics- Dosimetry
440101 - Radiation Instrumentation- General Detectors or Monitors & Radiometric Instruments
990230 - Mathematics & Mathematical Models- (1987-1989)