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Abstract #2715

A bootstrap approach to detect corrupted volume in ASL data

Marco Castellaro 1 , Denis Peruzzo 2 , Carlo Boffano 3 , Maria Grazia Bruzzone 3 , and Alessandra Bertoldo 1

1 Department of Information Engineering, University of Padova, Padova, Italy, 2 Department of Neuroimaging, Research institute IRCCS "E. Medea", Bosisio Parini, Lecco, Italy, 3 Neuroradiology Department, IRCCS Foundation Neurological Institute "C.Besta", Milano, Italy

Since ASL technique has been proposed, is necessary to and compute the average of a high number of repetitions to achieve a good SNR. This process can be affected by the presence of outliers in the data that could be caused by several artefacts or physiological tissue signal fluctuation. These outliers can highly impact estimation of perfusion. This work presents a novel method to exclude corrupted volumes and achieve more reliable estimates of perfusion. The method proposed was able to distinguish between corrupted and uncorrupted volumes on both simulated and real data.

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