• Korean Journal of Radiology
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• v.24 no.11
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• pp.1131-1141
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• 2023

Objective: Cortical iron deposition has recently been shown to occur in Alzheimer's disease (AD). In this study, we aimed to evaluate how cortical gray matter iron, measured using quantitative susceptibility mapping (QSM), differs in the clinical cognitive impairment spectrum. Materials and Methods: This retrospective study evaluated 73 participants (mean age ± standard deviation, 66.7 ± 7.6 years; 52 females and 21 males) with normal cognition (NC), 158 patients with mild cognitive impairment (MCI), and 48 patients with AD dementia. The participants underwent brain magnetic resonance imaging using a three-dimensional multi-dynamic multi-echo sequence on a 3-T scanner. We employed a deep neural network (QSMnet+) and used automatic segmentation software based on FreeSurfer v6.0 to extract anatomical labels and volumes of interest in the cortex. We used analysis of covariance to investigate the differences in susceptibility among the clinical diagnostic groups in each brain region. Multivariable linear regression analysis was performed to study the association between susceptibility values and cognitive scores including the Mini-Mental State Examination (MMSE). Results: Among the three groups, the frontal (P < 0.001), temporal (P = 0.004), parietal (P = 0.001), occipital (P < 0.001), and cingulate cortices (P < 0.001) showed a higher mean susceptibility in patients with MCI and AD than in NC subjects. In the combined MCI and AD group, the mean susceptibility in the cingulate cortex (β = -216.21, P = 0.019) and insular cortex (β = -276.65, P = 0.001) were significant independent predictors of MMSE scores after correcting for age, sex, education, regional volume, and APOE4 carrier status. Conclusion: Iron deposition in the cortex, as measured by QSMnet+, was higher in patients with AD and MCI than in NC participants. Iron deposition in the cingulate and insular cortices may be an early imaging marker of cognitive impairment related neurodegeneration.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.31-52
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• 2024

Considering the significant relationship between a tree's branch structure and physiology, understanding the detailed branch structure is crucial for fields such as species classification, and 3D tree modelling. Recently, terrestrial laser scanning (TLS) and quantitative structure model (QSM) have enhanced the understanding of branch structures by capturing the radius, length, and branching angle of branches. Previous studies examining branch structure with TL S and QSM often relied on mean or median of branch structure parameters, such as the radius ratio and length ratio in parent-child relationships, as representative values. Additionally, these studies have typically focused on the relationship between trunk and the first order branches. This study aims to explore the distribution of branch structure parameters up to the third order in Aesculus hippocastanum, Ginkgo biloba, and Prunus yedoensis. The gamma distribution best represented the distributions of branch structure parameters, as evidenced by the average of Kolmogorov-Smirnov statistics (radius = 0.048; length = 0.061; angle = 0.050). Comparisons of the mode, mean, and median were conducted to determine the most representative measure indicating the central tendency of branch structure parameters. The estimated distributions showed differences between the mode and mean (average of normalized differences for radius ratio = 11.2%; length ratio = 17.0%; branching angle = 8.2%), and between the mode and median (radius ratio = 7.5%; length ratio = 11.5%; branching angle = 5.5%). Comparisons of the estimated distributions across branch orders and species were conducted, showing variations across branch orders and species. This study suggests that examining the estimated distribution of the branch structure parameter offers a more detailed description of branch structure, capturing the central tendencies of branch structure parameters. We also emphasize the importance of examining higher branch orders to gain a comprehensive understanding of branch structure, highlighting the differences across branch orders.