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Abstract #3425

Not all voxels are created equal: reducing estimation bias in regional NODDI metrics using tissue-weighted mean

Christopher S Parker1, Thomas Veale2, Martina Bocchetta2, Catherine F Slattery2, Nick Fox2, Jonathan M Schott2, Dave M Cash1,2, and Hui Zhang1
1Centre for Medical Image Computing, Department of Computer Science, UCL, London, United Kingdom, 2Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square, Institute of Neurology, UCL, London, United Kingdom

Region-of-interest (ROI) metrics are typically computed as a mean over ROI voxels. However, for some NODDI metrics, this approach produces biased estimates in the presence of cerebrospinal fluid partial volume. We address this by introducing a tissue-weighted alternative. We compare the proposed mean to its conventional counterpart for periventricular and non-periventricular ROIs in healthy subjects and patients with young onset Alzheimer’s disease (YOAD). Results show the conventional mean overestimates orientation dispersion index and inflates inter-subject variation, particularly for periventricular ROIs and the YOAD cohort. This technique may improve detection of true regional effects in future group studies of neurodegenerative diseases.

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