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Denoising of complex MRI data by wavelet-domain filtering: Application to high-b-value diffusion-weighted imaging.

Wirestam, Ronnie LU ; Bibic, Adnan LU ; Lätt, Jimmy LU ; Brockstedt, Sara LU and Ståhlberg, Freddy LU (2006) In Magnetic Resonance in Medicine 56(5). p.1114-1120
Abstract
The Rician distribution of noise in magnitude magnetic resonance (MR) images is particularly problematic in low signal-to-noise ratio (SNR) regions. The Rician noise distribution causes a nonzero minimum signal in the image, which is often referred to as the rectified noise floor. True low signal is likely to be concealed in the noise, and quantification is severely hampered in low-SNR regions. To address this problem we performed noise reduction (or denoising) by Wiener-like filtering in the wavelet domain. The filtering was applied to complex MRI data before construction of the magnitude image. The noise-reduction algorithm was applied to simulated and experimental diffusion-weighted (DW) images. Denoising considerably reduced the signal... (More)
The Rician distribution of noise in magnitude magnetic resonance (MR) images is particularly problematic in low signal-to-noise ratio (SNR) regions. The Rician noise distribution causes a nonzero minimum signal in the image, which is often referred to as the rectified noise floor. True low signal is likely to be concealed in the noise, and quantification is severely hampered in low-SNR regions. To address this problem we performed noise reduction (or denoising) by Wiener-like filtering in the wavelet domain. The filtering was applied to complex MRI data before construction of the magnitude image. The noise-reduction algorithm was applied to simulated and experimental diffusion-weighted (DW) images. Denoising considerably reduced the signal standard deviation (SD, by up to 87% in simulated images) and decreased the background noise floor (by approximately a factor of 6 in simulated and experimental images). (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
magnetic resonance imaging, diffusion, wavelet, noise, filtering
in
Magnetic Resonance in Medicine
volume
56
issue
5
pages
1114 - 1120
publisher
Wiley Online Library
external identifiers
  • wos:000241761900021
  • scopus:33750621209
ISSN
1522-2594
DOI
10.1002/mrm.21036
language
English
LU publication?
yes
id
b5892090-08ba-4f0d-9d79-756384b50728 (old id 160927)
alternative location
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=16986108&dopt=Abstract
date added to LUP
2007-07-24 12:32:25
date last changed
2019-07-14 03:35:34
@article{b5892090-08ba-4f0d-9d79-756384b50728,
  abstract     = {The Rician distribution of noise in magnitude magnetic resonance (MR) images is particularly problematic in low signal-to-noise ratio (SNR) regions. The Rician noise distribution causes a nonzero minimum signal in the image, which is often referred to as the rectified noise floor. True low signal is likely to be concealed in the noise, and quantification is severely hampered in low-SNR regions. To address this problem we performed noise reduction (or denoising) by Wiener-like filtering in the wavelet domain. The filtering was applied to complex MRI data before construction of the magnitude image. The noise-reduction algorithm was applied to simulated and experimental diffusion-weighted (DW) images. Denoising considerably reduced the signal standard deviation (SD, by up to 87% in simulated images) and decreased the background noise floor (by approximately a factor of 6 in simulated and experimental images).},
  author       = {Wirestam, Ronnie and Bibic, Adnan and Lätt, Jimmy and Brockstedt, Sara and Ståhlberg, Freddy},
  issn         = {1522-2594},
  keyword      = {magnetic resonance imaging,diffusion,wavelet,noise,filtering},
  language     = {eng},
  number       = {5},
  pages        = {1114--1120},
  publisher    = {Wiley Online Library},
  series       = {Magnetic Resonance in Medicine},
  title        = {Denoising of complex MRI data by wavelet-domain filtering: Application to high-b-value diffusion-weighted imaging.},
  url          = {http://dx.doi.org/10.1002/mrm.21036},
  volume       = {56},
  year         = {2006},
}