Noise Floor Removal via Phase Correction of Complex Diffusion-Weighted Images: Influence on DTI and q-space Metrics

Abstract : The non-Gaussian noise distribution in magnitude Diffusion-Weighted Images (DWIs) can severely affect the estimation and reconstruction of the true diffusion signal. As a consequence, also the estimated diffusion metrics can be biased. We study the effect of phase correction, a procedure that re-establishes the Gaussianity of the noise distribution in DWIs by taking into account the corresponding phase images. We quantify the debiasing effects of phase correction in terms of diffusion signal estimation and calculated metrics. We perform in silico experiments based on a MGH Human Connectome Project dataset and on a digital phantom, accounting for different acquisition schemes, diffusion-weightings, signal to noise ratios, and for metrics based on Diffusion Tensor Imaging and on Mean Apparent Propagator Magnetic Resonance Imaging, i.e. q-space metrics. We show that phase correction is still a challenge, but also an effective tool to debias the estimation of diffusion signal and metrics from DWIs, especially at high b-values.
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Communication dans un congrès
Computational Diffusion MRI, Oct 2016, Athens, Greece. Computational Diffusion MRI 2016
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Contributeur : Marco Pizzolato <>
Soumis le : jeudi 1 septembre 2016 - 13:59:34
Dernière modification le : jeudi 11 janvier 2018 - 16:47:53

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Marco Pizzolato, Rutger Fick, Timothé Boutelier, Rachid Deriche. Noise Floor Removal via Phase Correction of Complex Diffusion-Weighted Images: Influence on DTI and q-space Metrics. Computational Diffusion MRI, Oct 2016, Athens, Greece. Computational Diffusion MRI 2016. 〈hal-01358770〉

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