MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping

Abstract

Quantifying tissue iron concentration in vivo is instrumental for understanding the role of iron in physiology and in neurological diseases associated with abnormal iron distribution. Herein, we use recently-developed Quantitative Susceptibility Mapping (QSM) methodology to estimate the tissue magnetic susceptibility based on MRI signal phase. To investigate the effect of different regularization choices, we implement and compare ℓ1 and ℓ2 norm regularized QSM algorithms. These regularized approaches solve for the underlying magnetic susceptibility distribution, a sensitive measure of the tissue iron concentration, that gives rise to the observed signal phase. Regularized QSM methodology also involves a pre-processing step that removes, by dipole fitting, unwanted background phase effects due to bulk susceptibility variations between air and tissue and requires data acquisition only at a single field strength. For validation, performances of the two QSM methods were measured against published estimates of regional brain iron from postmortem and in vivo data. The in vivo comparison was based on data previously acquired using Field-Dependent Relaxation Rate Increase (FDRI), an estimate of MRI relaxivity enhancement due to increased main magnetic field strength, requiring data acquired at two different field strengths. The QSM analysis was based on susceptibility-weighted images acquired at 1.5T, whereas FDRI analysis used Multi-Shot Echo-Planar Spin Echo images collected at 1.5T and 3.0T. Both datasets were collected in the same healthy young and elderly adults. The in vivo estimates of regional iron concentration comported well with published postmortem measurements; both QSM approaches yielded the same rank ordering of iron concentration by brain structure, with the lowest in white matter and the highest in globus pallidus. Further validation was provided by comparison of the in vivo measurements, ℓ1-regularized QSM versus FDRI and ℓ2-regularized QSM versus FDRI, which again yielded perfect rank ordering of iron by brain structure. The final means of validation was to assess how well each in vivo method detected known age-related differences in regional iron concentrations measured in the same young and elderly healthy adults. Both QSM methods and FDRI were consistent in identifying higher iron concentrations in striatal and brain stem ROIs (i.e., caudate nucleus, putamen, globus pallidus, red nucleus, and substantia nigra) in the older than in the young group. The two QSM methods appeared more sensitive in detecting age differences in brain stem structures as they revealed differences of much higher statistical significance between the young and elderly groups than did FDRI. However, QSM values are influenced by factors such as the myelin content, whereas FDRI is a more specific indicator of iron content. Hence, FDRI demonstrated higher specificity to iron yet yielded noisier data despite longer scan times and lower spatial resolution than QSM. The robustness, practicality, and demonstrated ability of predictin...

Studying cyto and myeloarchitecture of the human cortex at ultra-high field with quantitative imaging: R1, R2* and magnetic susceptibility

“…If only these two sources of contrast contribute to the three quantitative maps, the number of measurements is greater than the number of unknowns even when the relaxivity and baseline values are unknown, provided that the number of measurements is greater than nine. In order to increase the reliability of the fitting procedure, the iron coefficients to susceptibility (0.73×10 -3 ppm/(mg/ Kg)) and R 2 * (0.242 Hz/(mg/Kg)) were assumed as reported in Lim et al (2013) and using the slope observed between R 2 * and susceptibility reported by Deistung et al (2013) in deep grey matter structures at 7 T. These values are in close agreement with those obtained in other studies (Bilgic et al, 2012;Langkammer et al, 2012Langkammer et al, , 2010Schweser et al, 2011;Bowtell, 2013, 2010). The use of these parameters implies the calculated iron concentration will be in mg/Kg.…”

“…Iron concentration in deep gray matter structures (globus pallidus, putamen, caudate) have been shown to have a correlation with susceptibility (Bilgic et al, 2012;Langkammer et al, 2012;Lim et al, 2013;Schweser et al, 2011;Bowtell, 2013, 2010). Myelin content does contribute to the gray and white matter contrast in phase imaging (C. Liu et al, 2011;Lodygensky et al, 2012) yet, not only is the myelin susceptibility anisotropic, its contribution to the measured phase images is driven by the microstructural compartmentalization of lipid organization and its orientation in respect to the static magnetic field (He and Yablonskiy, 2009;Luo et al, 2014;Wharton and Bowtell, 2013;Yablonskiy et al, 2012).…”

A modulated closed form solution for quantitative susceptibility mapping — A thorough evaluation and comparison to iterative methods based on edge prior knowledge

“…Iron contributes to tissue contrast especially in the deep gray matter (globus pallidus, putamen and caudate) which has histologically derived high iron concentration showing good correlation with phase and susceptibility contrast (Bilgic et al, 2012;Schweser et al, 2011;Wharton and Bowtell, 2010). The other proposed contributor to the phase contrast, particularly between white and gray matter, is myelin where pathological demyelination has shown a decreased phase contrast between gray and white matter (C. Liu et al, 2011;Lodygensky et al, 2012) and good correlation was found between myelination and phase contrast during development.…”

Seven-Tesla MRI and neuroimaging biomarkers for Alzheimer’s disease

“…70 In addition to T1-and T2-weighted imaging, high-resolution quantitative techniques such as R2*, susceptibility-weighted imaging, and quantitative susceptibility maps have shown promise in detecting microbleeds in vascular dementia, quantifying iron content in normal aging, and as a tool for assessing hippocampal subfield differences. 10,31,85 Hence, these techniques may play a role in detecting neuroanatomical atrophy and visualizing microinfarcts in AD, as well as providing insights to better understand the role of iron and inflammation in the progression of the disease.…”