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

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Author

Summary, in English

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.

Publishing year

2010

Language

English

Pages

125-137

Publication/Series

Magma

Volume

23

Issue

3

Document type

Journal article

Publisher

Springer

Topic

  • Radiology, Nuclear Medicine and Medical Imaging

Status

Published

Project

  • MRI brain perfusion quantification at 3 tesla using arterial spin labeling

Research group

  • MR Physics

ISBN/ISSN/Other

  • ISSN: 1352-8661