TRUNCATION ARTIFACT REDUCTION IN MAGNETIC-RESONANCE-IMAGING BY MARKOVRANDOM-FIELD METHODS

A new statistical method is proposed for reduction of truncation artif acts when reconstructing a function by a finite number of its Fourier series coefficients, Following the Bayesian approach, it is possible t o take into account both the errors induced by the truncation of the F ourier series and some specific characteristics of the function. A sui table Markov random field is used for modeling these characteristics. Furthermore, in applications like Magnetic Resonance Imaging, where th ese coefficients are the measured data, the experimental random noise in the data can also be taken into account, Monte Carlo Markov chain m ethods are used to make statistical inference. Parameter selection in the Bayesian model is also addressed and a solution for selecting the parameters automatically is proposed, The method is applied successful ly to both simulated and real magnetic resonance images.