Denoising of arterial spin labeling data: wavelet-domain filtering compared with Gaussian smoothing.
(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:
https://lup.lub.lu.se/record/1594714
- author
- Bibic, Adnan
LU
; Knutsson, Linda
LU
; Ståhlberg, Freddy LU and Wirestam, Ronnie LU
- organization
- publishing date
- 2010
- 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
- pmid:20424885
- ISSN
- 1352-8661
- DOI
- 10.1007/s10334-010-0209-8
- project
- MRI brain perfusion quantification at 3 tesla using arterial spin labeling
- 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
- 2016-04-01 13:09:16
- date last changed
- 2022-03-29 05:50:55
@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 = {{https://lup.lub.lu.se/search/files/3194729/1653281.pdf}}, doi = {{10.1007/s10334-010-0209-8}}, volume = {{23}}, year = {{2010}}, }