Effects of Non-Local Diffusion on Structural MRI Preprocessing and Default Network Mapping: Statistical Comparisons with Isotropic/Anisotropic Diffusion

• Published in: PloS one Vol. 6; no. 10; p. e26703
• Main Authors: Zuo, Xi-Nian, Xing, Xiu-Xia
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 31.10.2011; Public Library of Science (PLoS)
• Subjects: Anisotropy; Biology; Brain; Brain - physiology; Brain mapping; Brain Mapping - methods; Chinese medicine; Cluster Analysis; Curvature; Data processing; Data smoothing; Diffusion; Diffusion effects; Engineering; Filtration; Functional magnetic resonance imaging; Gender differences; Humans; Image processing; Image segmentation; Kernels; Laboratories; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mapping; Mathematics; Medical imaging; Medicine; Models, Statistical; Neighborhoods; Nerve Net - physiology; Network management services; Neuroimaging; Neurology; NMR; Noise; Noise reduction; Nuclear magnetic resonance; Partial differential equations; Preprocessing; Reproducibility of Results; Scanners; Segmentation; Spatial smoothing; Structure-function relationships; Studies; Traditional Chinese medicine; Beijing China; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0026703

Neuroimaging community usually employs spatial smoothing to denoise magnetic resonance imaging (MRI) data, e.g., Gaussian smoothing kernels. Such an isotropic diffusion (ISD) based smoothing is widely adopted for denoising purpose due to its easy implementation and efficient computation. Beyond these advantages, Gaussian smoothing kernels tend to blur the edges, curvature and texture of images. Researchers have proposed anisotropic diffusion (ASD) and non-local diffusion (NLD) kernels. We recently demonstrated the effect of these new filtering paradigms on preprocessing real degraded MRI images from three individual subjects. Here, to further systematically investigate the effects at a group level, we collected both structural and functional MRI data from 23 participants. We first evaluated the three smoothing strategies' impact on brain extraction, segmentation and registration. Finally, we investigated how they affect subsequent mapping of default network based on resting-state functional MRI (R-fMRI) data. Our findings suggest that NLD-based spatial smoothing maybe more effective and reliable at improving the quality of both MRI data preprocessing and default network mapping. We thus recommend NLD may become a promising method of smoothing structural MRI images of R-fMRI pipeline.