Iron Content in Deep Gray Matter as a Function of Age Using Quantitative Susceptibility Mapping: A Multicenter Study - Li, Yan; Sethi, Sean K.; Zhang, Chunyan; Miao, Yanwei; Yerramsetty, Kiran Kumar; Palutla, Vinay Kumar; Gharabaghi, Sara; Wang, Chengyan; He, Naying; Cheng, Jingliang; Yan, Fuhua; Haacke, Ewart Mark (Frontiers in Neuroscience, 2021)
Purpose: To evaluate the effect of resolution on the quantitative susceptibility mapping (QSM) quantification; to verify the consistency of QSM across field strengths and manufacturers in evaluating the deep gray matter (DGM) of the human brain using subjects from multiple sites; and to establish a QSM baseline as a function of age for each DGM structure using both a global and regional iron analysis. Methods: Data from 623 healthy adults, ranging from 20 to 90 years old, were collected across 3 sites using gradient echo imaging on one 1.5T and two 3.0 Tesla MR scanners. Eight subcortical gray matter nucleus were semi-automatically segmented using a full-width half maximum threshold-based analysis of the QSM. Age-mean susceptibility, volumes, and total iron content correlations were reported for each measured structure for both the whole-region and RII (high iron content regions) analysis. For the purpose of studying the effect of resolution on QSM, a digitized model of the brain was applied. Results: The mean susceptibilities of CN, GP, and PUT are not significantly affected by changing the slice thickness from 0.5mm to 3mm. But for small structures, the susceptibility is reduced by 10% for 2mm thick slices. For global analysis, the mean susceptibility correlated positively with age for the CN, PUT, RN, SN and DN, meanwhile there was a strong negative correlation with age in THA. The volumes of most nucleus were negatively correlated with age. Apart from the GP, THA, and PT all the other structures showed an increasing total iron content despite the reductions in volume with age. For the RII regional high iron content analysis, mean susceptibility in most the structures was moderately to strongly correlated with age. Similar to the global analysis, apart from the GP, THA, and PT, all structures showed an increasing total iron content. Conclusion: A reasonable estimate for age-related iron behavior can be obtained from a large cross site, cross manufacturer set of data and can be used for correcting for age related iron changes when studying diseases like Parkinson’s disease and other iron related neurodegenerative diseases.