Gleaning multicomponent T1 and T2 information from steady-state imaging data

Magn Reson Med. 2008 Dec;60(6):1372-87. doi: 10.1002/mrm.21704.


The driven-equilibrium single-pulse observation of T(1) (DESPOT1) and T(2) (DESPOT2) are rapid, accurate, and precise methods for voxelwise determination of the longitudinal and transverse relaxation times. A limitation of the methods, however, is the inherent assumption of single-component relaxation. In a variety of biological tissues, in particular human white matter (WM) and gray matter (GM), the relaxation has been shown to be more completely characterized by a summation of two or more relaxation components, or species, each believed to be associated with unique microanatomical domains or water pools. Unfortunately, characterization of these components on a voxelwise, whole-brain basis has traditionally been hindered by impractical acquisition times. In this work we extend the conventional DESPOT1 and DESPOT2 approaches to include multicomponent relaxation analysis. Following numerical analysis of the new technique, renamed multicomponent driven equilibrium single pulse observation of T(1)/T(2) (mcDESPOT), whole-brain multicomponent T(1) and T(2) quantification is demonstrated in vivo with clinically realistic times of between 16 and 30 min. Results obtained from four healthy individuals and two primary progressive multiple sclerosis (MS) patients demonstrate the future potential of the approach for identifying and assessing tissue changes associated with several neurodegenerative conditions, in particular those associated with WM.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms*
  • Artificial Intelligence*
  • Brain / pathology*
  • Female
  • Humans
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods*
  • Magnetic Resonance Imaging / methods*
  • Male
  • Neurodegenerative Diseases / pathology*
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity