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Abstract #3372

Impact of denoising in diffusion-weighted data on spherical deconvolution modelling

Marina Rakic1, Luis Miguel Lacerda1, Ahmad Beyh2, Pedro Luque-Laguna1, Rachel Barrett2, Francisco De Santiago Requejo1, Steven Williams1, Gareth Barker1, Fernando Zelaya1, and Flavio Dell'Acqua1,2

1Dept. of Neuroimaging, King's College London, London, United Kingdom, 2Dept. of Forensic and Neurodevelopmental Science, King's College London, London, United Kingdom

A well-known dilemma in DW-MRI acquisitions is to determine the extent to which signal-to-noise ratio (SNR) can be can be sacrificed in favour of higher spatial resolution on one hand, and in favour of shorter acquisition time on the other. In this study we quantify the reproducibility of spherical deconvolution results at 3 spatial resolutions with and without denoising, as it is still unclear how denoising methods6 affect the uncertainty in subsequent diffusion model fitting and whether it introduces or improves bias in modelled fibre direction.

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