Robust Perfusion Maps in Arterial Spin Labeling by Means of M-Estimators

Camille Maumet¹, Pierre Maurel¹, Jean-Christophe Ferr¹, ², Christian Barillot¹

In Arterial Spin Labeling (ASL), the perfusion signal is usually extracted by averaging the volumes acquired over several repetitions. Unfortunately, the presence of artefacts is a well-known source of outliers and can drastically alter the perfusion map obtained by averaging. In this paper, we propose to compute ASL perfusion maps using Huber's M-estimator, a robust statistical function that is not overly impacted by outliers. This method is compared to an empirical approach, previously introduced in the literature, based on z-score thresholding. Overall, Huber's M-estimator is more robust than z-thresholding. Both robust approaches outperform the sample mean in the presence of outliers.