Denoising of arterial spin labeling data: wavelet-domain filtering compared with Gaussian smoothing.
OBJECTIVE
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.
Bibic
Adnan
A
Department of Medical Radiation Physics, Lund University, Lund, Sweden. adnan.bibic@med.lu.se
Knutsson
Linda
L
Ståhlberg
Freddy
F
Wirestam
Ronnie
R
D002536:Cerebral Arteries / Q000502:physiology*; D002560:Cerebrovascular Circulation / Q000502:physiology; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008833:Microcirculation / Q000502:physiology; D008954:Models, Biological; D015233:Models, Statistical; D016011:Normal Distribution; D055420:Perfusion Imaging / Q000379:methods*; D013113:Spin Labels
D013113:Spin Labels