MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping
Open accessAbstract
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 1.5 T, whereas FDRI analysis used Multi-Shot Echo-Planar Spin Echo images collected at 1.5 T and 3.0 T. 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 predicting the change in iron deposition in adult aging suggest that regularized QSM algorithms using single-field-strength data are possible alternatives to tissue iron estimation requiring two field strengths.
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Regional heterogeneity of brain iron and its change with age have both been confirmed in vivo by MRI.
“Regional heterogeneity of brain iron and its change with age have both been confirmed in vivo by MRI.48–51”
From The role of iron in brain ageing and neurodegenerative disorders (2014) · cites this paper as [51]
Studies at high-field strength show that the confounding effect of myelin on T2* can be accounted for by including frequency information, which is normally discarded.
“Additionally, studies at high-field strength show that the confounding effect of myelin on T2* can be accounted for by including frequency information, which is normally discarded.51,203,204”
From The role of iron in brain ageing and neurodegenerative disorders (2014) · cites this paper as [51]
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- Haacke EM Chengb NYC House MJ Liu Q Neelavalli J Ogg RJ Khan A Ayaz M Kirsch W Obenaus A 2005b Imaging iron stores in the brain using magnetic resonance imaging Magnetic Resonance Imaging 23 1 25 15733784
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- Authors
- Bilgic, B., Pfefferbaum, A., Rohlfing, T., Sullivan, E. V., & Adalsteinsson, E.
- Affiliations
- SRI International (US)
- Stanford Medicine (US)
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-02
- Volume
- 59
- Issue
- 3
- Pages
- 2625-2635
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3254708
- OpenAlex
- W2019035911
- Cited by
- 499
- References
- 40
- Citations by year
- 2026: 13 · 2025: 19 · 2024: 29 · 2023: 34 · 2022: 57 · 2021: 39 · 2020: 29 · 2019: 40 · 2018: 44 · 2017: 39 · 2016: 39 · 2015: 40 · 2014: 35 · 2013: 33 · 2012: 8
- FWCI
- 18.58
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
- PMC
- Funders
- National Institutes of Health
- National Science Foundation (NSF)
- Siemens Healthcare; Siemens-MIT Alliance
- MIT-CIMIT Medical Engineering Fellowship
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Neurology › Neurological diseases and metabolism
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- quantitative susceptibility mapping, normal aging, magnetic susceptibility, regularization, L1 and L2 regularization, striatum, globus pallidus, brainstem, myelin content, dipole fitting, substantia nigra, red nucleus, caudate nucleus, putamen
- MeSH terms
- Adult, Aged, Aged, 80 and over, Aging, Algorithms, Brain, Brain Chemistry, Brain Mapping, Brain Stem, Cadaver, Corpus Striatum, Female, Fourier Analysis, Humans, Image Processing, Computer-Assisted, Iron, Magnetic Resonance Imaging, Male, Middle Aged, Thalamus, Reproducibility of Results, Young Adult
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