Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5406851 | Journal of Magnetic Resonance | 2009 | 10 Pages |
Abstract
Data analysis in MRI usually entails a series of processing procedures. One of these procedures is noise assessment, which in the context of this work, includes both the identification of noise-only pixels and the estimation of noise variance (standard deviation). Although noise assessment is critical to many MRI processing techniques, the identification of noise-only pixels has received less attention than has the estimation of noise variance. The main objectives of this paper are, therefore, to demonstrate (a) that the identification of noise-only pixels has an important role to play in the analysis of MRI data, (b) that the identification of noise-only pixels and the estimation of noise variance can be combined into a coherent framework, and (c) that this framework can be made self-consistent. To this end, we propose a novel iterative approach to simultaneously identify noise-only pixels and estimate the noise standard deviation from these identified pixels in a commonly used data structure in MRI. Experimental and simulated data were used to investigate the feasibility, the accuracy and the stability of the proposed technique.
Keywords
Related Topics
Physical Sciences and Engineering
Chemistry
Physical and Theoretical Chemistry
Authors
Cheng Guan Koay, Evren Ãzarslan, Carlo Pierpaoli,