Robust Cerebral Blood Flow Map Estimation in Arterial Spin Labeling
Résumé
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).
Domaines
Neurosciences [q-bio.NC]
Fichier principal
Maumet_MBIA2012.pdf (871.1 Ko)
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Camille_Maumet_2012-10_Poster_MBIA.pdf (1.28 Mo)
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Origine | Fichiers produits par l'(les) auteur(s) |
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Format | Poster |
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