PDE-based spatial smoothing: A practical demonstration of impacts on MRI brain extraction, tissue segmentation and registration

Xiu Xia Xing, You Long Zhou, Jonathan S. Adelstein, Xi Nian Zuo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)731-738
Number of pages8
JournalMagnetic Resonance Imaging
Volume29
Issue number5
DOIs
StatePublished - Jun 2011

Keywords

  • Anisotropic diffusion
  • Brain extraction
  • MRI denoise
  • Non-local means
  • Partial differential equation
  • Registration
  • Segmentation

ASJC Scopus subject areas

  • Biophysics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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