Advertisement

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

  • Adnan BibicEmail author
  • Linda Knutsson
  • Freddy Ståhlberg
  • Ronnie Wirestam
Research Article

Abstract

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

Magnetic resonance imaging Arterial spin labeling Cerebral blood flow Perfusion Wavelets Filtering Denoising 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Buxton RB, Frank LR, Wong EC, Siewert B, Warach S, Edelman RR (1998) A general kinetic model for quantitative perfusion imaging with arterial spin labeling. Magn Reson Med 40: 383–396CrossRefPubMedGoogle Scholar
  2. 2.
    Wang J, Licht DJ, Jahng GH, Liu CS, Rubin JT, Haselgrove J, Zimmerman RA, Detre JA (2003) Pediatric perfusion imaging using pulsed arterial spin labeling. J Magn Reson Imaging 18: 404–413CrossRefPubMedGoogle Scholar
  3. 3.
    Detre JA, Zhang W, Roberts DA, Silva AC, Williams DS, Grandis DJ, Koretsky AP, Leigh JS (1994) Tissue specific perfusion imaging using arterial spin labeling. NMR Biomed 7: 75–82CrossRefPubMedGoogle Scholar
  4. 4.
    Wong EC, Buxton RB, Frank LR (1998) Quantitative imaging of perfusion using a single subtraction (QUIPSS and QUIPSS II). Magn Reson Med 39: 702–708CrossRefPubMedGoogle Scholar
  5. 5.
    Wong EC, Buxton RB, Frank LR (1997) Implementation of quantitative perfusion imaging techniques for functional brain mapping using pulsed arterial spin labeling. NMR Biomed 10: 237–249CrossRefPubMedGoogle Scholar
  6. 6.
    Golay X, Petersen ET, Hui F (2005) Pulsed star labeling of arterial regions (PULSAR): a robust regional perfusion technique for high field imaging. Magn Reson Med 53: 15–21CrossRefPubMedGoogle Scholar
  7. 7.
    Luh WM, Wong EC, Bandettini PA, Hyde JS (1999) QUIPSS II with thin-slice TI1 periodic saturation: a method for improving accuracy of quantitative perfusion imaging using pulsed arterial spin labeling. Magn Reson Med 41: 1246–1254CrossRefPubMedGoogle Scholar
  8. 8.
    Lim YM, Cho YW, Shamim S, Solomon J, Birn R, Luh WM, Gaillard WD, Ritzl EK, Theodore WH (2008) Usefulness of pulsed arterial spin labeling MR imaging in mesial temporal lobe epilepsy. Epilepsy Res 82: 183–189CrossRefPubMedGoogle Scholar
  9. 9.
    Wu WC, Mazaheri Y, Wong EC (2007) The effects of flow dispersion and cardiac pulsation in arterial spin labeling. IEEE Trans Med Imaging 26: 84–92CrossRefPubMedGoogle Scholar
  10. 10.
    Noth U, Meadows GE, Kotajima F, Deichmann R, Corfield DR, Turner R (2006) Cerebral vascular response to hypercapnia: determination with perfusion MRI at 1.5 and 3.0 Tesla using a pulsed arterial spin labeling technique. J Magn Reson Imaging 24: 1229–1235CrossRefPubMedGoogle Scholar
  11. 11.
    Ye FQ, Frank JA, Weinberger DR, McLaughlin AC (2000) Noise reduction in 3D perfusion imaging by attenuating the static signal in arterial spin tagging (ASSIST). Magn Reson Med 44: 92–100CrossRefPubMedGoogle Scholar
  12. 12.
    Wang J, Aguirre GK, Kimberg DY, Detre JA (2003) Empirical analyses of null-hypothesis perfusion FMRI data at 1.5 and 4 T. Neuroimage 19: 1449–1462CrossRefPubMedGoogle Scholar
  13. 13.
    Alexander ME, Baumgartner R, Summers AR, Windischberger C, Klarhoefer M, Moser E, Somorjai RL (2000) A wavelet-based method for improving signal-to-noise ratio and contrast in MR images. Magn Reson Imaging 18: 169–180CrossRefPubMedGoogle Scholar
  14. 14.
    Nowak RD (1999) Wavelet-based Rician noise removal for magnetic resonance imaging. IEEE Trans Image Process 8: 1408–1419CrossRefPubMedGoogle Scholar
  15. 15.
    Wirestam R, Bibic A, Lätt J, Brockstedt S, Ståhlberg F (2006) Denoising of complex MRI data by wavelet-domain filtering: application to high-b-value diffusion-weighted imaging. Magn Reson Med 56: 1114–1120CrossRefPubMedGoogle Scholar
  16. 16.
    Zaroubi S, Goelman G (2000) Complex denoising of MR data via wavelet analysis: application for functional MRI. Magn Reson Imaging 18: 59–68CrossRefPubMedGoogle Scholar
  17. 17.
    Wink AM, Roerdink JB (2004) Denoising functional MR images: a comparison of wavelet denoising and Gaussian smoothing. IEEE Trans Med Imaging 23: 374–387CrossRefPubMedGoogle Scholar
  18. 18.
    Worsley KJ, Marrett S, Neelin P, Evans AC (1996) Searching scale space for activation in PET images. Hum Brain Mapp 4: 74–90CrossRefPubMedGoogle Scholar
  19. 19.
    Percival DB, Walden AT (2000) Wavelet methods for time series analysis. Cambridge University Press, CambridgeGoogle Scholar
  20. 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
  21. 21.
    Figueiredo PM, Clare S, Jezzard P (2005) Quantitative perfusion measurements using pulsed arterial spin labeling: effects of large region-of-interest analysis. J Magn Reson Imaging 21: 676–682CrossRefPubMedGoogle Scholar
  22. 22.
    Aguirre GK, Detre JA, Zarahn E, Alsop DC (2002) Experimental design and the relative sensitivity of BOLD and perfusion fMRI. Neuroimage 15: 488–500CrossRefPubMedGoogle Scholar
  23. 23.
    Fernandez-Seara MA, Edlow BL, Hoang A, Wang J, Feinberg DA, Detre JA (2008) Minimizing acquisition time of arterial spin labeling at 3T. Magn Reson Med 59: 1467–1471CrossRefPubMedGoogle Scholar
  24. 24.
    Jones DK (2003) Determining and visualizing uncertainty in estimates of fiber orientation from diffusion tensor MRI. Magn Reson Med 49: 7–12CrossRefPubMedGoogle Scholar
  25. 25.
    Alexander ME, Baumgartner R, Windischberger C, Moser E, Somorjai RL (2000) Wavelet domain de-noising of time-courses in MR image sequences. Magn Reson Imaging 18: 1129–1134CrossRefPubMedGoogle Scholar
  26. 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
  27. 27.
    Petersen ET, Lim T, Golay X (2006) Model-free arterial spin labeling quantification approach for perfusion MRI. Magn Reson Med 55: 219–232CrossRefPubMedGoogle Scholar

Copyright information

© ESMRMB 2010

Authors and Affiliations

  • Adnan Bibic
    • 1
    Email author
  • Linda Knutsson
    • 1
  • Freddy Ståhlberg
    • 1
    • 2
  • Ronnie Wirestam
    • 1
  1. 1.Department of Medical Radiation PhysicsLund UniversityLundSweden
  2. 2.Department of Diagnostic RadiologyLund UniversityLundSweden

Personalised recommendations