Abstract
Non-invasive measurement of Cerebral Blood Flow (CBF) is now feasible thanks to the introduction of Arterial Spin Labeling (ASL) Magnetic Resonance Imaging (MRI) techniques. To date, the low signal-to-noise ratio of ASL gives us no option but to repeat the acquisition in order to accumulate enough data to get a reliable signal. Perfusion signal is usually extracted by averaging across the repetitions. However, due to its zero breakdown point, the sample mean is very sensitive to outliers. A single outlier can thus have strong detrimental effects on the sample mean estimate.
In this paper, we propose to estimate robust ASL CBF maps by means of M-estimators to overcome the deleterious effects of outliers. The behavior of this method is compared to z-score thresholding as recommended in [8]. Validation on simulated and real data is provided. Quantitative validation is undertaken by measuring the correlation with the most widespread technique to measure perfusion with MRI: Dynamic Susceptibility weighted Contrast (DSC).
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References
Buxton, R.B., Frank, L.R., Wong, E.C., Siewert, B., Warach, S., Edelman, R.R.: A general kinetic model for quantitative perfusion imaging with arterial spin labeling. Magnetic Resonance in Medicine 40(3), 383–396 (1998)
Detre, J.A., Leigh, J.S., Williams, D.S., Koretsky, A.P.: Perfusion imaging. Magnetic Resonance in Medicine 23, 37–45 (1992)
Huber, P.: Robust estimation of a location parameter. The Annals of Mathematical Statistics (1964)
Krasker, W.S., Welsch, R.E.: Efficient bounded-influence regression estimation. Journal of the American Statistical Association 77(379), 595–604 (1982)
Rousseeuw, P., LeRoy, A.: Robust Regression and Outlier Detection. Wiley series in probability and mathematical statistics. Probability and mathematical statistics. Wiley-Interscience (2003)
Shiffler, R.: Maximum Z scores and outliers. The American Statistician (1988)
Sidaros, K., Olofsson, K., Miranda, M.J., Paulson, O.B.: Arterial spin labeling in the presence of severe motion. Journal of Cerebral Blood Flow and Metabolism (2005)
Tan, H., Maldjian, J.A., Pollock, J.M., Burdette, J.H., Yang, L.Y., Deibler, A.R., Kraft, R.A.: A fast, effective filtering method for improving clinical pulsed arterial spin labeling MRI. Journal of Magnetic Resonance Imaging 29(5), 1134–1139 (2009)
Warmuth, C., Günther, M., Zimmer, C.: Quantification of Blood Flow in Brain Tumors: Comparison of Arterial Spin Labeling and Dynamic Susceptibility-weighted Contrast-enhanced MR Imaging. Radiology 228(4), 523–532 (2003)
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Maumet, C., Maurel, P., Ferré, JC., Barillot, C. (2012). Robust Cerebral Blood Flow Map Estimation in Arterial Spin Labeling. In: Yap, PT., Liu, T., Shen, D., Westin, CF., Shen, L. (eds) Multimodal Brain Image Analysis. MBIA 2012. Lecture Notes in Computer Science, vol 7509. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33530-3_19
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DOI: https://doi.org/10.1007/978-3-642-33530-3_19
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