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Abstract #2081

A Fast and Effective Strategy for Artifact Identification and Signal Restoring with HARDI data

Elisa Scaccianoce1,2, Francesca Baglio2, Giuseppe Baselli1, and Flavio Dell'Acqua3

1Department of Electrinics, Informations and Bioengineering, Politecnico di Milano, Milano, Italy, 2RM Lab, Don Carlo Gnocchi Foundation ONLUS, IRCCS S. Maria Nascente, Milano, Milano, Italy, 3NATBRAINLAB, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, United Kingdom, London, United Kingdom

HARDI datasets are often prone to different type of artifacts, difficult to detect even by expert users. In this work we propose a fast and effective pipeline for outlier identification and correction of HARDI datasets. Here corrupted data is first identified as outlier and then regenerated using a framework based on signal decomposition using spherical harmonics. This approach was tested on healthy controls and validated with simulated dataset. Our study confirms the efficacy of using SH for artifacts identification and correction.

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