Abstract #2081

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

Elisa Scaccianoce^1,2, 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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