Impact of Rician Adapted Non-Local Means Filtering on HARDI

Maxime Descoteaux 1 Nicolas Wiest-Daesslé 2 Sylvain Prima 2 Christian Barillot 2 Rachid Deriche 3
1 NEUROSPIN - Service NEUROSPIN
2 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
3 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique de l'École normale supérieure, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
Abstract : In this paper we study the impact of denoising the raw high angular resolution diffusion imaging (HARDI) data with the Non-Local Means filter adapted to Rician noise (NLMr). We first show that NLMr filtering improves robustness of apparent diffusion coefficient (ADC) and orientation distribution function (ODF) reconstructions from synthetic HARDI datasets. Our results suggest that the NLMr filtering improve the quality of anisotropy maps computed from ADC and ODF and improve the coherence of q-ball ODFs with the underlying anatomy while not de- grading angular resolution. These results are shown on a biological phan- tom with known ground truth and on a real human brain dataset. Most importantly, we show that multiple measurements of diffusion-weighted (DW) images and averaging these images along each direction can be avoided because NLMr filtering of the individual DW images produces better quality generalized fractional anisotropy maps and more accurate ODF fields than when computed from the averaged DW datasets.
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Maxime Descoteaux, Nicolas Wiest-Daesslé, Sylvain Prima, Christian Barillot, Rachid Deriche. Impact of Rician Adapted Non-Local Means Filtering on HARDI. Medical image computing and computer-assisted intervention - MICCAI 2008, Sep 2008, New-York, United States. ⟨10.1007/978-3-540-85990-1_15⟩. ⟨inria-00423318⟩

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