Quantitative susceptibility mapping by inversion of a perturbation field model: Correlation with brain iron in normal aging

Clare B. Poynton, Mark Jenkinson, Elfar Adalsteinsson, Edith V. Sullivan, Adolf Pfefferbaum, William Wells

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

There is increasing evidence that iron deposition occurs in specific regions of the brain in normal aging and neurodegenerative disorders such as Parkinson's, Huntington's, and Alzheimer's disease. Iron deposition changes the magnetic susceptibility of tissue, which alters the MR signal phase, and allows estimation of susceptibility differences using quantitative susceptibility mapping (QSM). We present a method for quantifying susceptibility by inversion of a perturbation model, or 'QSIP.' The perturbation model relates phase to susceptibility using a kernel calculated in the spatial domain, in contrast to previous Fourier-based techniques. A tissue/air susceptibility atlas is used to estimate B0 inhomogeneity. QSIP estimates in young and elderly subjects are compared to postmortem iron estimates, maps of the Field-Dependent Relaxation Rate Increase, and the L1-QSM method. Results for both groups showed excellent agreement with published postmortem data and in vivo FDRI: statistically significant Spearman correlations ranging from Rho=0.905 to Rho=1.00 were obtained. QSIP also showed improvement over FDRI and L1-QSM: reduced variance in susceptibility estimates and statistically significant group differences were detected in striatal and brainstem nuclei, consistent with age-dependent iron accumulation in these regions.

Original languageEnglish
Pages (from-to)339-353
Number of pages15
JournalIEEE Transactions on Medical Imaging
Volume34
Issue number1
DOIs
Publication statusPublished or Issued - 1 Jan 2015

Keywords

  • Atlases
  • brain iron
  • inverse methods
  • magnetic resonance imaging (MRI)
  • normal aging
  • quantitative susceptibility mapping

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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