Noise estimation in single coil MR images

Abstract

Magnetic resonance (MR) signal is degraded by acquisition noise. Estimation and removal of noise from MR images is essential for analysis and further pre-processing. The work proposes a new method for noise variance estimation in single coil magnitude MR images. The proposed noise estimator is derived using the Maximum Á Posterior (MAP) estimator. Noise follows a Gaussian distribution at high Signal to Noise (SNR) ratio and Rician distribution at low SNR. In the MAP framework, Gaussian noise model and local statistics of the noisy image are instrumental in estimation of noise at high SNR. The proposed work also exploits the local second order moments in the MAP framework for noise estimation at low SNR. Convergence of the proposed MAP framework further highlights the optimal performance of the noise estimator. Noise estimation experiments conducted on Brainweb database exhibit encouraging performance at high as well as low SNR.

Keywords

• Noise estimation
• Maximum Á posteriori
• Bayesian estimation
• Statistical signal processing
• Magnetic resonance
• Gaussian distribution