Journal
MAGNETIC RESONANCE IMAGING
Volume 29, Issue 5, Pages 731-738Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.mri.2011.02.007
Keywords
MRI denoise; Partial differential equation; Anisotropic diffusion; Non-local means; Brain extraction; Segmentation; Registration
Funding
- Startup Foundation for Distinguished Research Professor of Institute for Psychology [Y0CX492S03]
- National Nature Science Foundation of China [10671131]
- Beijing Natural Science Foundation [1092006]
- Scientific Research Foundation for the Youth [X1006012200801]
- Youth Specialized in Differential Equations of Beijing University of Technology [X3006012200801]
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Spatial smoothing is typically used to denoise magnetic resonance imaging (MRI) data. Gaussian smoothing kernels, associated with heat equations or isotropic diffusion (ISD), are widely adopted for this purpose because of their easy implementation and efficient computation, but despite these advantages, Gaussian smoothing kernels blur the edges, curvature and texture of images. To overcome these issues, researchers have proposed anisotropic diffusion (ASD) and non-local means [i.e., diffusion (NLD)] kernels. However, these new filtering paradigms are rarely applied to MRI analyses. In the current study, using real degraded MRI data, we demonstrated the effect of denoising using ISD, ASD and NLD kernels. Furthermore, we evaluated their impact on three common preprocessing steps of MRI data analysis: brain extraction, segmentation and registration. Results suggest that NLD-based spatial smoothing is most effective at improving the quality of MRI data preprocessing and thus should become the new standard method of smoothing in MRI data processing. (C) 2011 Elsevier Inc. All rights reserved.
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