MRI Volumetric Analysis of Multiple Sclerosis: Methodology and Validation

Lihong Li, Xiang Li, Hongbing Lu, Wei Huang, Christopher Christodoulou, Alina Tudorica, Lauren B. Krupp, Zhengrong Liang

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

We present an automatic mixture-based algorithm for segmentation of brain tissues (white and gray matters-WM and GM), cerebral spinal fluid (CSF), and brain lesions to quantitatively analyze multiple sclerosis. The method performs intensity-based tissue classification using multispectral magnetic resonance (MR) images based on a stochastic model. With the existence of white Gaussian noise and spatially invariant blurring in acquired MR images, a Karhunen-Loéve (K-L) domain Wiener filter is applied for accurate noise reduction and resolution restoration on blurred and noisy images to minimize the partial volume effect (PVE), which is a major limiting factor for the quantitative analysis. Following that, we utilize a Markov random field Gibbs model to integrate the local spatial information into the well-established expectation-maximization model-fitting algorithm. Each voxel is then classified by a maximum a posterior (MAP) criterion, indicating its probabilities of belonging to each class, i.e., each voxel is labeled as a mixel with different tissue percentages, leading to further minimization of the PVE. The volumes of WM, GM, CSF, and brain lesions are extracted from the mixture-based segmentation and the corresponding brain atrophies are computed. In this study, we have investigated the accuracy and repeatability of the algorithm with inclusion of noise analysis and point spread function for image resolution enhancement. Experimental results on phantom, healthy volunteer, and patient studies are presented.

Original languageEnglish (US)
Pages (from-to)1686-1692
Number of pages7
JournalIEEE Transactions on Nuclear Science
Volume50
Issue number5 II
DOIs
StatePublished - Oct 1 2003

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Keywords

  • MRI
  • Markov random field
  • Mixture
  • Multispectral
  • Partial volume effect
  • Segmentation

ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Nuclear Energy and Engineering
  • Electrical and Electronic Engineering

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