Denoising of arterial spin labeling data: wavelet-domain filtering compared with Gaussian smoothing
- 274 Downloads
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.
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.
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.
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.
KeywordsMagnetic resonance imaging Arterial spin labeling Cerebral blood flow Perfusion Wavelets Filtering Denoising
Unable to display preview. Download preview PDF.
- 19.Percival DB, Walden AT (2000) Wavelet methods for time series analysis. Cambridge University Press, CambridgeGoogle Scholar
- 20.Leenders KL, Perani D, Lammertsma AA, Heather JD, Buckingham P, Healy MJ, Gibbs JM, Wise RJ, Hatazawa J, Herold S, Beaney RP, Brooks DJ, Spinks T, Rhodes C, Frackowiak RSJ, Jones T (1990) Cerebral blood flow, blood volume and oxygen utilization. Normal values and effect of age. Brain 113: 27–47CrossRefPubMedGoogle Scholar
- 26.Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1992) Numerical recipes in C: the art of scientific computing. 2. Cambridge University Press, CambridgeGoogle Scholar