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Abstract #1695

Impact of Outliers in DTI and Q-Ball Imaging - Clinical Implications and Correction Strategies

Michael Andrew Sharman1, Julien Cohen-Adad2, Maxime Descoteaux3, Arnaud Mess4,5, Habib Benali4,5, Stphane Lehericy6,7

1UMR-S975, CRICM-UPMC/Inserm, Paris, le-de-France, France; 2Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, United States; 3Department of Computer Science, Sherbrooke University, Qubec, Canada; 4UMR-S678, UPMC/Inserm, Paris, France; 5IFR49, Paris, France; 6Centre for NeuroImaging Research (CENIR), Hospital Piti-Salptrire , Paris, France; 7UMR-S975, CRICM-UPMC/Inserm, Paris, France

Corrupted images within acquired diffusion weighted MRI data can have an impact on the estimation of the tensor (in diffusion tensor imaging) and diffusion ODF (in q-ball imaging). In this study we performed a series of simulations and real data analyses to quantify this impact on derived metrics such as fractional anisotropy (FA) and generalised FA. From the results of these invetigations, we propose processing strategies to detect and correct corruption artifacts arising from large, unpredicatable signal variations.