Publication: Restoration of DWI Data Using a Rician LMMSE Estimator
Open/View Files
Date
2008
Published Version
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical & Electronics Engineers (IEEE)
The Harvard community has made this article openly available. Please share how this access benefits you.
Citation
Aja-Fernandez, S., M. Niethammer, M. Kubicki, M.E. Shenton, and C.-F. Westin. 2008. Restoration of DWI Data Using a Rician LMMSE Estimator. IEEE Trans. Med. Imaging 27, no. 10: 1389–1403. doi:10.1109/tmi.2008.920609.
Research Data
Abstract
This paper introduces and analyzes a linear minimum mean square error (LMMSE) estimator using a Rician noise model and its recursive version (RLMMSE) for the restoration of diffusion weighted images. A method to estimate the noise level based on local estimations of mean or variance is used to automatically parametrize the estimator. The restoration performance is evaluated using quality indexes and compared to alternative estimation schemes. The overall scheme is simple, robust, fast, and improves estimations. Filtering diffusion weighted magnetic resonance imaging (DW-MRI) with the proposed methodology leads to more accurate tensor estimations. Real and synthetic datasets are analyzed.
Description
Other Available Sources
Keywords
Diffusion-weighted imaging (DWI) restoration, linear minimum mean square error (LMMSE) estimator, magnetic resonance imaging (MRI), noise filtering, Rician distribution
Terms of Use
This article is made available under the terms and conditions applicable to Other Posted Material (LAA), as set forth at Terms of Service