title: Impact of Rician adapted Non-Local Means filtering on HARDI.
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 degrading angular resolution. These results are shown on a biological phantom 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.
authors.affiliation: NMR Lab, NeuroSpin, CEA Saclay, France.
authors.forename: Maxime
authors.initials: M
authors.lastname: Descoteaux
authors.forename: Nicolas
authors.initials: N
authors.lastname: Wiest-Daesslé
authors.forename: Sylvain
authors.initials: S
authors.lastname: Prima
authors.forename: Christian
authors.initials: C
authors.lastname: Barillot
authors.forename: Rachid
authors.initials: R
authors.lastname: Deriche
mesh_terms: D000465:Algorithms; D016477:Artifacts; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity