Noise Quantification in MR Experiments

• Activity Number: 587
• Type: Topic Contributed
• Date/Time: Wednesday, August 12, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Joerg Polzehl* and Karsten Tabelow
• Companies: Weierstrass Institute for Applied Analysis and Stochastics and Weierstrass Institute for Applied Analysis and Stochastics
• Keywords: Neuroscience ; Diffusion weighted MR ; Modeling ; High-resolution ; Low SNR

Abstract

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 neighbourhoods 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 modelling 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. The results are compared to other noise estimation methods. We illustrate the gain from using the method in data enhancement and modelling of a high-resolution diffusion dataset.