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Technical Note: Emission expectation maximization look‐alike algorithms for x‐ray CT and other applications
Author(s) -
Zeng Gengsheng L.
Publication year - 2018
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13077
Subject(s) - algorithm , expectation–maximization algorithm , weighting , tomography , transmission (telecommunications) , multiplicative function , rate of convergence , computer science , convergence (economics) , noise (video) , mathematical optimization , mathematics , maximum likelihood , physics , optics , key (lock) , artificial intelligence , telecommunications , statistics , mathematical analysis , computer security , acoustics , economics , image (mathematics) , economic growth
Purpose In emission tomography, the expectation maximization ( EM ) algorithm is easy to use with only one parameter to adjust ― the number of iterations. On the other hand, the EM algorithms for transmission tomography are not so user‐friendly and have many problems. This paper develops a new transmission algorithm similar to the emission EM algorithm. Methods This paper develops a family of emission‐ EM ‐look‐alike algorithms by expressing the emission EM algorithm in the additive form and changing the weighting factor. One of the family members can be applied to transmission tomography such as the x‐ray computed tomography ( CT ). Results Computer simulations are performed and compared with a similar algorithm by a different group using the transmission CT noise model. Our algorithm has the same convergence rate as theirs, and our algorithm provides better contrast‐to‐noise ratio for lesion detection. Conclusions For any noise variance function, an emission‐ EM ‐look‐alike algorithm can be derived. This algorithm preserves many properties of the emission EM algorithm such as multiplicative update, non‐negativity, faster convergence rate for the bright objects, and ease of implementation.