title: Non-Local Means Inpainting of MS Lesions in Longitudinal Image Processing.
abstract: In medical imaging, multiple sclerosis (MS) lesions can lead to confounding effects in automatic morphometric processing tools such as registration, segmentation and cortical extraction, and subsequently alter individual longitudinal measurements. Multiple magnetic resonance imaging (MRI) inpainting techniques have been proposed to decrease the impact of MS lesions in medical image processing, however, most of these methods make the assumption that lesions only affect white matter. Here, we propose a method to fill lesion regions using the patch-based non-local mean (NLM) strategy. The method consists of a hierarchical concentric filling strategy after identification of the lesion region. The lesion is filled iteratively, based on the surrounding tissue intensity, using an onion peel strategy. This concentric technique presents the advantage of preserving the local information and therefore the continuity of the anatomy and does not require identification of any a priori normal brain tissues. The method is first evaluated on 20 healthy subjects with simulated artificial MS lesions where we assessed our technique by measuring the peak signal-to-noise ratio (PSNR) of the images with inpainted lesion and the original healthy images. Second, in order to assess the impact of lesion filling on longitudinal image analyses, we performed a power analysis with sample size estimation to evaluate brain atrophy and ventricular growth in patients with MS. The method was compared to two different publicly available methods (FSL lesion fill and Lesion LEAP) and a more classic method, which fills the region with intensities similar to that of the surrounding healthy white matter tissue or mask the lesions. The proposed method was shown to exceed the other methods in reproducing the fidelity of healthy subject images where the lesions were inpainted. The method also improved the power to detect brain atrophy or ventricular growth by decreasing the sample size by 25% in the presence of MS lesions.
authors.affiliation: McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University Montreal, QC Canada.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Guizard
authors.affiliation: McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University Montreal, QC Canada ; Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic Cleveland, OH, USA.
authors.forename: Kunio
authors.initials: K
authors.lastname: Nakamura
authors.affiliation: Pictura Research Group, Unité Mixte de Recherche Centre National de la Recherche Scientifique, Laboratoire Bordelais de Recherche en Informatique Talence, France.
authors.forename: Pierrick
authors.initials: P
authors.lastname: Coupé
authors.affiliation: McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University Montreal, QC Canada.
authors.forename: Vladimir S
authors.initials: VS
authors.lastname: Fonov
authors.affiliation: McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University Montreal, QC Canada.
authors.forename: Douglas L
authors.initials: DL
authors.lastname: Arnold
authors.affiliation: McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University Montreal, QC Canada.
authors.forename: D Louis
authors.initials: DL
authors.lastname: Collins
keywords: MRI; MS; inpainting; lesions; non-local; patch-based; segmentation