Purpose: This study aimed to determine whether genetic factors affect the location of dilated perivascular spaces (dPVS) by comparing healthy young twins and non-twin (NT) siblings. Materials and Methods: A total of 700 healthy young adult twins and NT siblings [138 monozygotic (MZ) twin pairs, 79 dizygotic (DZ) twin pairs, and 133 NT sibling pairs] were collected from the Human Connectome Project dataset. dPVS was automatically segmented and normalized to standard space. Then, spatial similarity indices [mean squared error (MSE), structural similarity (SSIM), and dice similarity (DS)] were calculated for dPVS in the basal ganglia (BGdPVS) and white matter (WMdPVS) between paired subjects before and after propensity score matching of dPVS volumes between groups. Within-pair correlations for the regional volumes of dVPS were also assessed using the intraclass correlation coefficient. Results: The spatial similarity of dPVS was significantly higher in MZ twins [higher DS (median, 0.382 and 0.310) and SSIM (0.963 and 0.887) and lower MSE (0.005 and 0.005) for BGdPVS and WMdPVS, respectively] than in DZ twins [DS (0.121 and 0.119), SSIM (0.941 and 0.868), and MSE (0.010 and 0.011)] and NT siblings [DS (0.106 and 0.097), SSIM (0.924 and 0.848), and MSE (0.016 and 0.017)]. No significant difference was found between DZ twins and NT siblings. Similar results were found even after the subjects were matched according to dPVS volume. Regional dPVS volumes were also more correlated within pairs in MZ twins than in DZ twins and NT siblings. Conclusion Our results suggest that genetic factors affect the location of dPVS.

Purpose: This study aimed to determine whether genetic factors affect the location of dilated perivascular spaces (dPVS) by comparing healthy young twins and non-twin (NT) siblings. Materials and Methods: A total of 700 healthy young adult twins and NT siblings [138 monozygotic (MZ) twin pairs, 79 dizygotic (DZ) twin pairs, and 133 NT sibling pairs] were collected from the Human Connectome Project dataset. dPVS was automatically segmented and normalized to standard space. Then, spatial similarity indices [mean squared error (MSE), structural similarity (SSIM), and dice similarity (DS)] were calculated for dPVS in the basal ganglia (BGdPVS) and white matter (WMdPVS) between paired subjects before and after propensity score matching of dPVS volumes between groups. Within-pair correlations for the regional volumes of dVPS were also assessed using the intraclass correlation coefficient. Results: The spatial similarity of dPVS was significantly higher in MZ twins [higher DS (median, 0.382 and 0.310) and SSIM (0.963 and 0.887) and lower MSE (0.005 and 0.005) for BGdPVS and WMdPVS, respectively] than in DZ twins [DS (0.121 and 0.119), SSIM (0.941 and 0.868), and MSE (0.010 and 0.011)] and NT siblings [DS (0.106 and 0.097), SSIM (0.924 and 0.848), and MSE (0.016 and 0.017)]. No significant difference was found between DZ twins and NT siblings. Similar results were found even after the subjects were matched according to dPVS volume. Regional dPVS volumes were also more correlated within pairs in MZ twins than in DZ twins and NT siblings. Conclusion Our results suggest that genetic factors affect the location of dPVS.

Purpose: This study aimed to determine whether genetic factors affect the location of dilated perivascular spaces (dPVS) by comparing healthy young twins and non-twin (NT) siblings. Materials and Methods: A total of 700 healthy young adult twins and NT siblings [138 monozygotic (MZ) twin pairs, 79 dizygotic (DZ) twin pairs, and 133 NT sibling pairs] were collected from the Human Connectome Project dataset. dPVS was automatically segmented and normalized to standard space. Then, spatial similarity indices [mean squared error (MSE), structural similarity (SSIM), and dice similarity (DS)] were calculated for dPVS in the basal ganglia (BGdPVS) and white matter (WMdPVS) between paired subjects before and after propensity score matching of dPVS volumes between groups. Within-pair correlations for the regional volumes of dVPS were also assessed using the intraclass correlation coefficient. Results: The spatial similarity of dPVS was significantly higher in MZ twins [higher DS (median, 0.382 and 0.310) and SSIM (0.963 and 0.887) and lower MSE (0.005 and 0.005) for BGdPVS and WMdPVS, respectively] than in DZ twins [DS (0.121 and 0.119), SSIM (0.941 and 0.868), and MSE (0.010 and 0.011)] and NT siblings [DS (0.106 and 0.097), SSIM (0.924 and 0.848), and MSE (0.016 and 0.017)]. No significant difference was found between DZ twins and NT siblings. Similar results were found even after the subjects were matched according to dPVS volume. Regional dPVS volumes were also more correlated within pairs in MZ twins than in DZ twins and NT siblings. Conclusion Our results suggest that genetic factors affect the location of dPVS.

Purpose: This study aimed to determine whether genetic factors affect the location of dilated perivascular spaces (dPVS) by comparing healthy young twins and non-twin (NT) siblings. Materials and Methods: A total of 700 healthy young adult twins and NT siblings [138 monozygotic (MZ) twin pairs, 79 dizygotic (DZ) twin pairs, and 133 NT sibling pairs] were collected from the Human Connectome Project dataset. dPVS was automatically segmented and normalized to standard space. Then, spatial similarity indices [mean squared error (MSE), structural similarity (SSIM), and dice similarity (DS)] were calculated for dPVS in the basal ganglia (BGdPVS) and white matter (WMdPVS) between paired subjects before and after propensity score matching of dPVS volumes between groups. Within-pair correlations for the regional volumes of dVPS were also assessed using the intraclass correlation coefficient. Results: The spatial similarity of dPVS was significantly higher in MZ twins [higher DS (median, 0.382 and 0.310) and SSIM (0.963 and 0.887) and lower MSE (0.005 and 0.005) for BGdPVS and WMdPVS, respectively] than in DZ twins [DS (0.121 and 0.119), SSIM (0.941 and 0.868), and MSE (0.010 and 0.011)] and NT siblings [DS (0.106 and 0.097), SSIM (0.924 and 0.848), and MSE (0.016 and 0.017)]. No significant difference was found between DZ twins and NT siblings. Similar results were found even after the subjects were matched according to dPVS volume. Regional dPVS volumes were also more correlated within pairs in MZ twins than in DZ twins and NT siblings. Conclusion Our results suggest that genetic factors affect the location of dPVS.

Purpose: This study aimed to determine whether genetic factors affect the location of dilated perivascular spaces (dPVS) by comparing healthy young twins and non-twin (NT) siblings. Materials and Methods: A total of 700 healthy young adult twins and NT siblings [138 monozygotic (MZ) twin pairs, 79 dizygotic (DZ) twin pairs, and 133 NT sibling pairs] were collected from the Human Connectome Project dataset. dPVS was automatically segmented and normalized to standard space. Then, spatial similarity indices [mean squared error (MSE), structural similarity (SSIM), and dice similarity (DS)] were calculated for dPVS in the basal ganglia (BGdPVS) and white matter (WMdPVS) between paired subjects before and after propensity score matching of dPVS volumes between groups. Within-pair correlations for the regional volumes of dVPS were also assessed using the intraclass correlation coefficient. Results: The spatial similarity of dPVS was significantly higher in MZ twins [higher DS (median, 0.382 and 0.310) and SSIM (0.963 and 0.887) and lower MSE (0.005 and 0.005) for BGdPVS and WMdPVS, respectively] than in DZ twins [DS (0.121 and 0.119), SSIM (0.941 and 0.868), and MSE (0.010 and 0.011)] and NT siblings [DS (0.106 and 0.097), SSIM (0.924 and 0.848), and MSE (0.016 and 0.017)]. No significant difference was found between DZ twins and NT siblings. Similar results were found even after the subjects were matched according to dPVS volume. Regional dPVS volumes were also more correlated within pairs in MZ twins than in DZ twins and NT siblings. Conclusion Our results suggest that genetic factors affect the location of dPVS.

Purpose: Although previous studies have found an association between brain iron levels and brain function, few have explored this relationship in children with autism spectrum disorder (ASD). Thus, we aimed to determine the association between quantitative susceptibility mapping (QSM)-derived magnetic susceptibility values (MSVs) and brain function in children with ASD. Materials and Methods: The study included children with ASD who underwent both a brain magnetic resonance imaging with QSM and the Wechsler intelligence scale for children intelligence quotient (IQ) test. Select subcortical brain regions (caudate, putamen, globus pallidus, and thalamus; both right and left) were automatically segmented, and the MSVs were extracted from the QSM. The IQ score parameters (verbal comprehension, working memory, perceptual organization, and processing speed indices, and full-scale IQ) were measured. Correlation analysis was used to assess the association between age and IQ test parameters and between age and MSV. Linear regression analysis was performed to measure the relationship between the MSV and IQ test parameters. Results: A total of 23 children with ASD (median age [interquartile range]: 10 [8–14] years; 12 males) were included. Age was not correlated with any of the IQ test parameters (p > 0.05). There was a significant correlation between age and right-thalamus MSV (r = 0.443, p = 0.03); however, no such correlation was found with the MSVs of other regions (p > 0.05). Among the IQ test parameters, the verbal comprehension index significantly correlated with the left-caudate MSV (r = 0.420, p = 0.046) and the perceptual organization index significantly correlated with the right-globus-pallidus MSV (r = 0.414, p = 0.049). Conclusion Select subcortical MSVs were associated with IQ test parameters in children with ASD, suggesting that QSM is a potential neurodevelopmental marker.

Purpose: This study aims to enhance the performance of deep learning models for segmenting thin anatomical structures in medical images by introducing a label-preserving data-augmentation strategy. Materials and Methods: We developed a data-augmentation technique that applies geometric transformations and their inverses sequentially to input images while preserving the corresponding labels. This method was evaluated on inner ear magnetic resonance images for the automatic segmentation of semicircular canals characterized by thin and circular structures. The dataset included both internal and external samples. For the internal dataset, 70 subjects were used for model training and eight subjects for internal validation. Images were acquired using a 3 tesla magnetic resonance imaging scanner with a three-dimensional high-resolution T2 sequence, and ground-truth segmentations were manually annotated by an experienced radiologist. For external validation, four subjects from a public dataset (Vestibular-Schwannoma-SEG dataset, part of The Cancer Imaging Archive) with high-resolution T2 images for inner ear analysis were used. We performed quantitative evaluations using metrics such as Dice, intersection over union (IoU), 95% Hausdorff distance (HD), and average surface distance (ASD). A qualitative visual assessment was also performed. Results: The proposed model exhibited improved performance in semicircular canal segmentation in both quantitative and qualitative evaluations. Metrics such as Dice, IoU, 95% HD, and ASD indicated better performance than conventional methods. Conclusion The proposed label-preserving data augmentation method improves the segmentation of thin anatomical structures in medical images and offers a robust and efficient solution for enhancing deep learning models in medical imaging.

Purpose: The aim of the present study was to investigate the potential utility of the magnetic susceptibility-based assessment of cerebral oxygen metabolism with a dataset from a memory clinic. Materials and Methods: We collected and processed three-dimensional gradient-recalled echo phase data of 290 participants. To analyze oxygen metabolism, quantitative susceptibility mapping was performed for two major veins (superior sagittal sinus and straight sinus), and the venous oxygen saturation was estimated. Results: The estimated venous oxygen saturations of the two major veins were positively correlated with the clinical and volumetric measurements. They did not differ with the clinical diagnosis or clinical dementia rating. The values of the superior sagittal sinus were associated with the presence of the apolipoprotein E type 4 allele, when considering age, sex, and their interactions. Conclusion The results demonstrate that quantitative susceptibility mapping of clinically available three-dimensional susceptibility-weighted imaging sequences in a large-scale clinical dataset can estimate cerebral oxygen metabolism.

Objective: To assess focal mineral deposition in the globus pallidus (GP) by CT and quantitative susceptibility mapping (QSM) of MRI scans and evaluate its clinical significance, particularly cerebrovascular degeneration. Materials and Methods: This study included 105 patients (66.1 ± 13.7 years; 40 male and 65 female) who underwent both CT and MRI with available QSM data between January 2017 and December 2019. The presence of focal mineral deposition in the GP on QSM (GPQSM) and CT (GPCT) was assessed visually using a three-point scale. Cerebrovascular risk factors and small vessel disease (SVD) imaging markers were also assessed. The clinical and radiological findings were compared between the different grades of GPQSM and GPCT. The relationship between GP grades and cerebrovascular risk factors and SVD imaging markers was assessed using univariable and multivariable linear regression analyses. Results: GPCT and GPQSM were significantly associated (p < 0.001) but were not identical. Higher GPCT and GPQSM grades showed smaller gray matter (p = 0.030 and p = 0.025, respectively) and white matter (p = 0.013 and p = 0.019, respectively) volumes, as well as larger GP volumes (p < 0.001 for both). Among SVD markers, white matter hyperintensity was significantly associated with GPCT (p = 0.006) and brain atrophy was significantly associated with GPQSM (p = 0.032) in at univariable analysis. In multivariable analysis, the normalized volume of the GP was independently positively associated with GPCT (p < 0.001) and GPQSM (p = 0.002), while the normalized volume of the GM was independently negatively associated with GPCT (p = 0.040) and GPQSM (p = 0.035). Conclusion Focal mineral deposition in the GP on CT and QSM might be a potential imaging marker of cerebral vascular degeneration. Both were associated with increased GP volume.

Iron has a vital role in the human body, including the central nervous system. Increased deposition of iron in the brain has been reported in aging and important neurodegenerative diseases. Owing to the unique magnetic resonance properties of iron, MRI has great potential for in vivo assessment of iron deposition, distribution, and non-invasive quantification. In this paper, we will review the MRI methods for iron assessment and their changes in aging and neurodegenerative diseases, focusing on Alzheimer’s disease. In addition, we will summarize the limitations of current approaches and introduce new areas and MRI methods for iron imaging that are expected in the future.