Document Detail


Denoising of arterial spin labeling data: wavelet-domain filtering compared with Gaussian smoothing.
MedLine Citation:
PMID:  20424885     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Adnan Bibic; Linda Knutsson; Freddy Ståhlberg; Ronnie Wirestam
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Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, Non-U.S. Gov't     Date:  2010-04-28
Journal Detail:
Title:  Magma (New York, N.Y.)     Volume:  23     ISSN:  1352-8661     ISO Abbreviation:  MAGMA     Publication Date:  2010 Jun 
Date Detail:
Created Date:  2010-06-07     Completed Date:  2011-05-03     Revised Date:  2014-01-20    
Medline Journal Info:
Nlm Unique ID:  9310752     Medline TA:  MAGMA     Country:  Germany    
Other Details:
Languages:  eng     Pagination:  125-37     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Cerebral Arteries / physiology*
Cerebrovascular Circulation / physiology
Humans
Magnetic Resonance Imaging / methods*
Microcirculation / physiology
Models, Biological
Models, Statistical
Normal Distribution
Perfusion Imaging / methods*
Spin Labels*
Chemical
Reg. No./Substance:
0/Spin Labels

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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