Abstract

In this work a closed-form, maximum-likelihood (ML) estimator for the variance of the thermal noise in magnetic resonance imaging (MRI) systems has been developed. The ML estimator was, in turn, used as a priori information for devising a single dimensional noise-cancellation-based image restoration algorithm. The performance of the estimator was assessed theoretically by means of the Crámer-Rao lower bound, and the effect of selecting an appropriate set of no-signal pixels on estimating the noise variance was also investigated. The effectivity of the noise-cancellation-based image restoration algorithm in compensating for the thermal noise in MRI was also evaluated. Actual MRI data from the LONI database was employed to assess the performance of both the ML estimator and the image restoration algorithm. © 2011 Springer-Verlag.