Advanced

Wavelet-based noise reduction for improved deconvolution of time-series data in dynamic susceptibility-contrast MRI.

Wirestam, Ronnie LU and Ståhlberg, Freddy LU (2005) In Magma 18(3). p.113-118
Abstract
Dynamic susceptibility-contrast (DSC) MRI requires deconvolution to retrieve the tissue residue function R(t) and the cerebral blood flow (CBF). In this study, deconvolution of time-series data was performed by wavelet-transform-based denoising combined with the Fourier transform (FT). Traditional FT-based deconvolution of noisy data requires frequency-domain filtering, often leading to excessive smoothing of the recovered signal. In the present approach, only a low degree of regularisation was employed while the major noise reduction was accomplished by wavelet transformation of data and Wiener-like filtering in the wavelet space. After inverse wavelet transform, the estimate of CBF.R(t) was obtained. DSC-MRI signal-versus-time curves... (More)
Dynamic susceptibility-contrast (DSC) MRI requires deconvolution to retrieve the tissue residue function R(t) and the cerebral blood flow (CBF). In this study, deconvolution of time-series data was performed by wavelet-transform-based denoising combined with the Fourier transform (FT). Traditional FT-based deconvolution of noisy data requires frequency-domain filtering, often leading to excessive smoothing of the recovered signal. In the present approach, only a low degree of regularisation was employed while the major noise reduction was accomplished by wavelet transformation of data and Wiener-like filtering in the wavelet space. After inverse wavelet transform, the estimate of CBF.R(t) was obtained. DSC-MRI signal-versus-time curves (signal-to-noise ratios 40 and 100) were simulated, corresponding to CBF values in the range 10-60 ml/(min 100 g). Three shapes of the tissue residue function were investigated. The technique was also applied to six volunteers. Simulations showed CBF estimates with acceptable accuracy and precision, as well as independence of any time shift between the arterial input function and the tissue concentration curve. The grey-matter to white-matter CBF ratio in volunteers was 2.4 +/- 0.2. The proposed wavelet/FT deconvolution is robust and can be implemented into existing perfusion software. CBF maps from healthy volunteers showed high quality. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
wavelets, noise, cerebral blood flow, imaging, perfusion, magnetic resonance, deconvolution, dynamic susceptibility contrast
in
Magma
volume
18
issue
3
pages
113 - 118
publisher
Springer
external identifiers
  • wos:000231721400003
  • pmid:15887036
  • scopus:23844534512
ISSN
1352-8661
DOI
10.1007/s10334-005-0102-z
language
English
LU publication?
yes
id
f1aaa32c-53f2-437b-a351-786605591d15 (old id 138038)
alternative location
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=15887036&dopt=Abstract
date added to LUP
2007-07-24 12:30:20
date last changed
2017-03-12 04:13:05
@article{f1aaa32c-53f2-437b-a351-786605591d15,
  abstract     = {Dynamic susceptibility-contrast (DSC) MRI requires deconvolution to retrieve the tissue residue function R(t) and the cerebral blood flow (CBF). In this study, deconvolution of time-series data was performed by wavelet-transform-based denoising combined with the Fourier transform (FT). Traditional FT-based deconvolution of noisy data requires frequency-domain filtering, often leading to excessive smoothing of the recovered signal. In the present approach, only a low degree of regularisation was employed while the major noise reduction was accomplished by wavelet transformation of data and Wiener-like filtering in the wavelet space. After inverse wavelet transform, the estimate of CBF.R(t) was obtained. DSC-MRI signal-versus-time curves (signal-to-noise ratios 40 and 100) were simulated, corresponding to CBF values in the range 10-60 ml/(min 100 g). Three shapes of the tissue residue function were investigated. The technique was also applied to six volunteers. Simulations showed CBF estimates with acceptable accuracy and precision, as well as independence of any time shift between the arterial input function and the tissue concentration curve. The grey-matter to white-matter CBF ratio in volunteers was 2.4 +/- 0.2. The proposed wavelet/FT deconvolution is robust and can be implemented into existing perfusion software. CBF maps from healthy volunteers showed high quality.},
  author       = {Wirestam, Ronnie and Ståhlberg, Freddy},
  issn         = {1352-8661},
  keyword      = {wavelets,noise,cerebral blood flow,imaging,perfusion,magnetic resonance,deconvolution,dynamic susceptibility contrast},
  language     = {eng},
  number       = {3},
  pages        = {113--118},
  publisher    = {Springer},
  series       = {Magma},
  title        = {Wavelet-based noise reduction for improved deconvolution of time-series data in dynamic susceptibility-contrast MRI.},
  url          = {http://dx.doi.org/10.1007/s10334-005-0102-z},
  volume       = {18},
  year         = {2005},
}