Аннотация:
We present a novel method for local estimation of the noise level in magnetic resonance images in the presence of a signal. The procedure uses a multi-scale approach to adaptively infer on local neighborhoods with similar data distribution. It exploits a maximum-likelihood estimator for the local noise level. Information assessed by this method is essential in a correct modeling in diffusion magnetic resonance experiments as well as in adequate preprocessing. The validity of the method is evaluated on repeated diffusion data of a phantom and simulated data. We illustrate the gain from using the method in data enhancement and modeling of a high-resolution diffusion data set.
