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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