Literature DB >> 18381247

MRI denoising using non-local means.

José V Manjón1, José Carbonell-Caballero, Juan J Lull, Gracián García-Martí, Luís Martí-Bonmatí, Montserrat Robles.   

Abstract

Magnetic Resonance (MR) images are affected by random noise which limits the accuracy of any quantitative measurements from the data. In the present work, a recently proposed filter for random noise removal is analyzed and adapted to reduce this noise in MR magnitude images. This parametric filter, named Non-Local Means (NLM), is highly dependent on the setting of its parameters. The aim of this paper is to find the optimal parameter selection for MR magnitude image denoising. For this purpose, experiments have been conducted to find the optimum parameters for different noise levels. Besides, the filter has been adapted to fit with specific characteristics of the noise in MR image magnitude images (i.e. Rician noise). From the results over synthetic and real images we can conclude that this filter can be successfully used for automatic MR denoising.

Mesh:

Year:  2008        PMID: 18381247     DOI: 10.1016/j.media.2008.02.004

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  62 in total

1.  The non-local bootstrap--estimation of uncertainty in diffusion MRI.

Authors:  Pew-Thian Yap; Hongyu An; Yasheng Chen; Dinggang Shen
Journal:  Inf Process Med Imaging       Date:  2013

2.  Rician noise removal by non-Local Means filtering for low signal-to-noise ratio MRI: applications to DT-MRI.

Authors:  Nicolas Wiest-Daesslé; Sylvain Prima; Pierrick Coupé; Sean Patrick Morrissey; Christian Barillot
Journal:  Med Image Comput Comput Assist Interv       Date:  2008

3.  Usability of unbiased nonlocal means for de-noising intraoperative magnetic resonance images in neurosurgery.

Authors:  Takashi Mizukuchi; Masazumi Fujii; Yuichiro Hayashi; Masatoshi Tsuzaka
Journal:  Int J Comput Assist Radiol Surg       Date:  2014-01-07       Impact factor: 2.924

4.  Least squares for diffusion tensor estimation revisited: propagation of uncertainty with Rician and non-Rician signals.

Authors:  Antonio Tristán-Vega; Santiago Aja-Fernández; Carl-Fredrik Westin
Journal:  Neuroimage       Date:  2011-10-08       Impact factor: 6.556

5.  MRI upsampling using feature-based nonlocal means approach.

Authors:  Kourosh Jafari-Khouzani
Journal:  IEEE Trans Med Imaging       Date:  2014-06-12       Impact factor: 10.048

6.  Efficient and robust nonlocal means denoising of MR data based on salient features matching.

Authors:  Antonio Tristán-Vega; Verónica García-Pérez; Santiago Aja-Fernández; Carl-Fredrik Westin
Journal:  Comput Methods Programs Biomed       Date:  2011-09-08       Impact factor: 5.428

7.  Free-breathing liver fat and R 2 quantification using motion-corrected averaging based on a nonlocal means algorithm.

Authors:  Huiwen Luo; Ante Zhu; Curtis N Wiens; Jitka Starekova; Ann Shimakawa; Scott B Reeder; Kevin M Johnson; Diego Hernando
Journal:  Magn Reson Med       Date:  2020-08-01       Impact factor: 4.668

8.  Adapting non-local means of de-noising in intraoperative magnetic resonance imaging for brain tumor surgery.

Authors:  Takashi Mizukuchi; Masazumi Fujii; Yuichiro Hayashi; Masatoshi Tsuzaka
Journal:  Radiol Phys Technol       Date:  2013-11-27

9.  Denoising of diffusion MRI using random matrix theory.

Authors:  Jelle Veraart; Dmitry S Novikov; Daan Christiaens; Benjamin Ades-Aron; Jan Sijbers; Els Fieremans
Journal:  Neuroimage       Date:  2016-08-11       Impact factor: 6.556

10.  Uncertainty estimation in diffusion MRI using the nonlocal bootstrap.

Authors:  Pew-Thian Yap; Hongyu An; Yasheng Chen; Dinggang Shen
Journal:  IEEE Trans Med Imaging       Date:  2014-04-29       Impact factor: 10.048

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.