Multi-parametric representation of voxel-based quantitative magnetic resonance imaging

PLoS One. 2014 Nov 13;9(11):e111688. doi: 10.1371/journal.pone.0111688. eCollection 2014.


The aim of the study was to explore the possibilities of multi-parametric representations of voxel-wise quantitative MRI data to objectively discriminate pathological cerebral tissue in patients with brain disorders. For this purpose, we recruited 19 patients with Multiple Sclerosis (MS) as benchmark samples and 19 age and gender matched healthy subjects as a reference group. The subjects were examined using quantitative Magnetic Resonance Imaging (MRI) measuring the tissue structure parameters: relaxation rates, R(1) and R(2), and proton density. The resulting parameter images were normalized to a standard template. Tissue structure in MS patients was assessed by voxel-wise comparisons with the reference group and with correlation to a clinical measure, the Expanded Disability Status Scale (EDSS). The results were visualized by conventional geometric representations and also by multi-parametric representations. Data showed that MS patients had lower R(1) and R(2), and higher proton density in periventricular white matter and in wide-spread areas encompassing central and sub-cortical white matter structures. MS-related tissue abnormality was highlighted in posterior white matter whereas EDSS correlation appeared especially in the frontal cortex. The multi-parameter representation highlighted disease-specific features. In conclusion, the proposed method has the potential to visualize both high-probability focal anomalies and diffuse tissue changes. Results from voxel-based statistical analysis, as exemplified in the present work, may guide radiologists where in the image to inspect for signs of disease. Future clinical studies must validate the usability of the method in clinical practice.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Brain Mapping / methods*
  • Disease Progression
  • Female
  • Frontal Lobe / diagnostic imaging
  • Frontal Lobe / pathology*
  • Humans
  • Image Processing, Computer-Assisted
  • Magnetic Resonance Imaging / methods*
  • Male
  • Middle Aged
  • Multiple Sclerosis / diagnostic imaging
  • Multiple Sclerosis / pathology*
  • Radiography
  • White Matter / diagnostic imaging
  • White Matter / pathology*

Grant support

The National Research Council (grant number VR/NT 2008-3368), Linköping University, and the County Council of Östergötland are acknowledged for financial support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. SyntheticMR AB provided support in the form of salary for author JBMW, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.