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A multigrid expectation maximization reconstruction algorithm for positron emission tomography

Journal Article · · IEEE Trans. Med. Imag.; (United States)
DOI:https://doi.org/10.1109/42.14509· OSTI ID:5812943
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The problem of reconstruction in positron emission tomography (PET) is basically estimating the number of photon pairs emitted from the source. Using the concept of the maximum likelihood (ML) algorithm, the problem of reconstruction is reduced to determining an estimate of the emitter density that maximizes the probability of observing the actual detector count data over all possible emitter density distributions. A solution using this type of expectation maximization (EM) algorithm with a fixed grid size is severely handicapped by the slow convergence rate, the large computation time, and the non-uniform correction efficiency of each iteration making the algorithm very sensitive to the image pattern. An efficient knowledge-based multigrid reconstruction algorithm based on the ML approach is presented to overcome these problems.

Research Organization:
9508070; 9507448; 9508352
OSTI ID:
5812943
Journal Information:
IEEE Trans. Med. Imag.; (United States), Journal Name: IEEE Trans. Med. Imag.; (United States) Vol. 7:4; ISSN ITMID
Country of Publication:
United States
Language:
English

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Related Subjects

550602* -- Medicine-- External Radiation in Diagnostics-- (1980-)
62 RADIOLOGY AND NUCLEAR MEDICINE
99 GENERAL AND MISCELLANEOUS
990230 -- Mathematics & Mathematical Models-- (1987-1989)
ALGORITHMS
CALCULATION METHODS
COMPUTERIZED TOMOGRAPHY
DIAGNOSTIC TECHNIQUES
EMISSION COMPUTED TOMOGRAPHY
IMAGE PROCESSING
ITERATIVE METHODS
KNOWLEDGE BASE
MATHEMATICAL LOGIC
MAXIMUM-LIKELIHOOD FIT
NUMERICAL SOLUTION
POSITRON COMPUTED TOMOGRAPHY
PROCESSING
TOMOGRAPHY