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

Author:
  • Adnan Bibic
  • Linda Knutsson
  • Freddy Ståhlberg
  • Ronnie Wirestam
Publishing year: 2010
Language: English
Pages: 125-137
Publication/Series: Magma
Volume: 23
Issue: 3
Document type: Journal article
Publisher: Springer

Abstract 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.

Keywords

  • Radiology, Nuclear Medicine and Medical Imaging

Other

Published
  • ISSN: 1352-8661
Freddy Ståhlberg
E-mail: freddy.stahlberg [at] med.lu.se

Professor

Medical Radiation Physics, Lund

+46 46 17 31 19

+46 70 688 31 19

32

Professor

Diagnostic Radiology, (Lund)

+46 46 17 70 30

32