Advanced

Denoising of arterial spin labeling data: wavelet-domain filtering compared with Gaussian smoothing.

Bibic, Adnan LU ; Knutsson, Linda LU ; Ståhlberg, Freddy LU and Wirestam, Ronnie LU (2010) In Magma 23(3). p.125-137
Abstract
PURPOSE: To investigate a wavelet-based filtering scheme for denoising of arterial spin labeling (ASL) data, potentially enabling reduction of the required number of averages and the acquisition time. METHODS: ASL magnetic resonance imaging (MRI) provides quantitative perfusion maps by using arterial water as an endogenous tracer. The signal difference between a labeled image, where inflowing arterial spins are inverted, and a control image is proportional to blood perfusion. ASL perfusion maps suffer from low SNR, and the experiment must be repeated a number of times (typically more than 40) to achieve adequate image quality. In this study, systematic errors introduced by the proposed wavelet-domain filtering approach were investigated in... (More)
PURPOSE: To investigate a wavelet-based filtering scheme for denoising of arterial spin labeling (ASL) data, potentially enabling reduction of the required number of averages and the acquisition time. METHODS: ASL magnetic resonance imaging (MRI) provides quantitative perfusion maps by using arterial water as an endogenous tracer. The signal difference between a labeled image, where inflowing arterial spins are inverted, and a control image is proportional to blood perfusion. ASL perfusion maps suffer from low SNR, and the experiment must be repeated a number of times (typically more than 40) to achieve adequate image quality. In this study, systematic errors introduced by the proposed wavelet-domain filtering approach were investigated in simulated and experimental image datasets and compared with conventional Gaussian smoothing. RESULTS: Application of the proposed method enabled a reduction of the number of averages and the acquisition time by at least 50% with retained standard deviation, but with effects on absolute CBF values close to borders and edges. CONCLUSIONS: When the ASL perfusion maps showed moderate-to-high SNRs, wavelet-domain filtering was superior to Gaussian smoothing in the vicinity of borders between gray and white matter, while Gaussian smoothing was a better choice for larger homogeneous areas, irrespective of SNR. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Magma
volume
23
issue
3
pages
125 - 137
publisher
Springer
external identifiers
  • wos:000278470100001
  • pmid:20424885
  • scopus:77956395565
ISSN
1352-8661
DOI
10.1007/s10334-010-0209-8
language
English
LU publication?
yes
id
1ab86eb8-2ea8-4f0c-8843-15e4e7ee4ca0 (old id 1594714)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/20424885?dopt=Abstract
date added to LUP
2010-05-04 23:13:26
date last changed
2018-06-03 03:50:37
@article{1ab86eb8-2ea8-4f0c-8843-15e4e7ee4ca0,
  abstract     = {PURPOSE: To investigate a wavelet-based filtering scheme for denoising of arterial spin labeling (ASL) data, potentially enabling reduction of the required number of averages and the acquisition time. METHODS: ASL magnetic resonance imaging (MRI) provides quantitative perfusion maps by using arterial water as an endogenous tracer. The signal difference between a labeled image, where inflowing arterial spins are inverted, and a control image is proportional to blood perfusion. ASL perfusion maps suffer from low SNR, and the experiment must be repeated a number of times (typically more than 40) to achieve adequate image quality. In this study, systematic errors introduced by the proposed wavelet-domain filtering approach were investigated in simulated and experimental image datasets and compared with conventional Gaussian smoothing. RESULTS: Application of the proposed method enabled a reduction of the number of averages and the acquisition time by at least 50% with retained standard deviation, but with effects on absolute CBF values close to borders and edges. CONCLUSIONS: When the ASL perfusion maps showed moderate-to-high SNRs, wavelet-domain filtering was superior to Gaussian smoothing in the vicinity of borders between gray and white matter, while Gaussian smoothing was a better choice for larger homogeneous areas, irrespective of SNR.},
  author       = {Bibic, Adnan and Knutsson, Linda and Ståhlberg, Freddy and Wirestam, Ronnie},
  issn         = {1352-8661},
  language     = {eng},
  number       = {3},
  pages        = {125--137},
  publisher    = {Springer},
  series       = {Magma},
  title        = {Denoising of arterial spin labeling data: wavelet-domain filtering compared with Gaussian smoothing.},
  url          = {http://dx.doi.org/10.1007/s10334-010-0209-8},
  volume       = {23},
  year         = {2010},
}