Multidimensional analysis of cerebral iron and vascular changes in chronic unilateral middle cerebral artery stenosis with quantitative susceptibility mapping, arterial spin labeling and high-resolution vessel wall imaging—a pilot study

Introduction

Ischemic stroke is a leading cause of disability and mortality in adults (1), with intracranial artery stenosis—particularly middle cerebral artery (MCA) stenosis—accounting for a significant proportion of cases (2). This stenosis leads to ischemia and hypoxia in the affected brain regions, causing neuronal injury and potentially contributing to conditions such as vascular dementia (3). Multiple mechanisms, both individually and in combination, are involved in this process, including excitotoxicity, mitochondrial dysfunction, free radical production, protein misfolding, apoptosis, necrosis, autophagy, and inflammation (4,5). These pathophysiological changes are closely linked to iron metabolism, influencing the distribution and balance of iron within the brain.

Quantitative susceptibility mapping (QSM) has emerged as a valuable tool for assessing the spatial distribution of iron in the brain (6,7), particularly in neurodegenerative diseases like Alzheimer’s and Parkinson’s disease. In the context of cerebral ischemia, several studies have used QSM to reveal abnormal iron deposition in the deep grey matter nuclei of patients with unilateral MCA stenosis (8-10). However, these studies have primarily focused on deep grey matter nuclei and have not explored the impact of cerebral cortex involvement and the varying degrees of cerebral artery stenosis.

In patients with intracranial atherosclerosis, vessel stenosis induces blood flow alterations that can be noninvasively assessed using arterial spin labeling (ASL) (11,12). ASL utilizes magnetically labeled water protons in arterial blood as an endogenous tracer, allowing for the independent measurement of territorial cerebral blood flow (CBF) without the need for contrast agents (13). Despite sharing similar vascular occlusions, patients may exhibit marked heterogeneity in clinical outcomes, largely determined by their ability to recruit collateral circulation and restore perfusion during the critical post-occlusion period (14). ASL has been compared with gold-standard modalities such as digital subtraction angiography (DSA) and stable xenon computed tomography (CT) CBF measurements (15). Although ASL might underestimate CBF in ischemic regions due to prolonged arterial arrival times, it offers valuable insights into collateral circulation that complement other imaging techniques. By bridging angiographic and perfusion imaging, ASL not only measures arterial transit times and quantifies CBF but also potentially sheds light on vascular stenosis-related iron deposition.

Vessel stenosis, even at the same grade, can exhibit varying plaque morphology. High-resolution vessel wall imaging (HR-VWI) is increasingly used to assess these morphological changes in the vessel wall (16). Enhancements observed in HR-VWI may reflect underlying inflammatory processes triggered by factors such as endothelial disruption or smooth muscle alterations (17,18). However, the relationship between vessel wall enhancement and iron redistribution remains poorly understood and warrants further investigation.

To date, there is no clear understanding regarding the changes in iron deposition and their underlying mechanisms in patients with cerebral ischemia. This study aims to integrate QSM, ASL, and HR-VWI to conduct a multidimensional analysis of iron. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-808/rc).

Methods

Participants

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Zhongshan Hospital, Shanghai, China (No. B2019-125R) and individual consent for this retrospective analysis was waived. Data were collected from 425 patients who underwent head magnetic resonance imaging (MRI) between September 2019 and March 2022 in Zhongshan Hospital, including ASL, QSM, time-of-flight magnetic resonance angiography (TOF-MRA), and HR-VWI. Inclusion criteria were: (I) unilateral MCA stenosis with other cerebral arteries, carotid, and vertebral arteries either normal or with mild stenosis (<30%); (II) chronic MCA stenosis with a duration ≥1 year since initial detection; (III) age ≥50 years; (IV) no history of brain tumor, brain injury, Parkinson’s disease, Alzheimer’s disease, or dementia. Exclusion criteria included: (I) acute embolic MCA stenosis or acute/subacute stroke (19); (II) cerebral microbleed/hemorrhage; (III) white matter hyperintensity with Fazekas grade 3 (20); (IV) infarct or encephalomalacia foci with diameter >15 mm; (V) non-atherosclerotic stenosis (e.g., artery dissection or vasculitis); (VI) poor MRI image quality. Ultimately, 48 patients with unilateral MCA stenosis and 24 healthy controls were included (Figure S1). Patients were categorized into two groups based on stenosis severity: <70% (n=26) and ≥70% (n=22). Clinical variables, including hypertension, diabetes, hyperlipidemia, smoking, and alcohol consumption were recorded.

MRI protocol

MRI was performed on a 3.0-T scanner (Discovery MR750; GE Healthcare, Milwaukee, WI, USA) with a 32-channel head coil. Participants underwent routine MRI along with ASL, QSM, three-dimensional (3D) TOF-MRA, and HR-VWI (3D CUBE sequence). Imaging parameters are detailed in the Table S1.

Imaging analysis

ASL and QSM image processing

CBF and susceptibility maps were generated from ASL and QSM data using an AW4.6 GE Workstation (GE Healthcare). The multi-echo phase data underwent Laplacian-based phase unwrapping, V-SHARP (i.e., variable spheric kernel size sophisticated harmonic artifact decrease for the phase data) background field removal, and morphology-enabled dipole inversion to produce susceptibility maps.

Brain regional segmentation

CBF and QSM images were registered to 3D CUBE T1-weight imaging (T1WI) using SPM12 software (http://www.fil.ion.ucl.ac.uk/spm/) in MATLAB (MathWorks, Natick, MA, USA). Automatic segmentation and normalization were performed with the CAT12 toolbox (http://www.neuro.uni-jena.de/cat/), aligning data to Montreal Neurological Institute (MNI) space. Fifty-eight cerebral cortex regions (Table S2) and fourteen deep grey matter nuclei according to the automated anatomy labeling (AAL) atlas (http://www.gin.cnrs.fr/en/tools/aal/) were segmented (Figure 1), including the caudate nucleus, putamen, globus pallidus, thalamus, substantia nigra, red nucleus, and dentate nucleus. The total intracranial volume, mean CBF, and susceptibility values were automatically calculated for regions of interest (ROIs) using a custom MATLAB script.

Figure 1 Schematic diagram of the image analysis workflow. 3D, three-dimensional; AAL, automated anatomy labeling; CSF, cerebrospinal fluid; GM, gray matter; QSM, quantitative susceptibility mapping; ROI, region of interest; WM, white matter.

VWI analysis

Stenosis severity and plaque enhancement were independently assessed by two observers (observer P.L. and Y.P.), with repeat evaluations after four weeks. They assessed the degree of stenosis in M1 and M2 segments of the MCA on TOF MRA. Percent stenosis was calculated using the formula: Stenosis=(1−DstenosisDnormal)×100%, where Dstenosis represents the artery diameter at the site of the most severe stenosis, and Dnormal is the diameter of the nearest healthy artery segment.

The enhancement of MCA plaque was evaluated by comparing the mean signal intensity of the MCA plaque (SI_plaque) and pituitary stalk (SI_stalk) on contrast-enhanced CUBE T1WI, and further categorized into three grades (Grade 0, Grade 1, Grade 2) based on relative signal intensity (21) (Figure 2).

Figure 2 Case 1: a 60-year-old female with stenosis of R-MCA. (A) R-MCA stenosis in MRA (degree <70%) (red arrow). (B) Precontract HR-VWI showed the plaque in the R-MCA (red arrow). (C) The plaque was not enhanced (grade 0) in postcontrast HR-VWI (red arrow). Case 2: a 58-year-old male with stenosis of L-MCA. (D) L-MCA stenosis in MRA (degree <70%) (red arrow). (E) Precontract HR-VWI showed the plaque in the L-MCA (red arrow). (F) The plaque was enhanced (grade 1) in postcontrast HR-VWI (red arrow). Case 3: a 64-year-old male with stenosis of R-MCA. (G) R-MCA stenosis in MRA (degree >70%) (red arrow). (H) Precontract HR-VWI showed the plaque in the R-MCA (red arrow). (I) The plaque was found to have enhancement (grade 2) in postcontrast HR-VWI (red arrow). (J) Pituitary stalk on postcontrast HR-VWI as a comparator (red arrow). HR-VWI, high-resolution vessel wall imaging; L-MCA, left middle cerebral artery; MRA, magnetic resonance angiography; R-MCA, right middle cerebral artery.

Statistical analysis

Data distribution was assessed using the Kolmogorov-Smirnov test. Clinical data were analyzed using ANOVA, Kruskal-Wallis, or Chi-square tests when appropriate. Paired t-tests and Wilcoxon signed-rank tests compared susceptibility differences between hemispheres in healthy controls and between ipsilateral and contralateral regions in the stenosis groups. Multiple linear regression was used to assess relationships between susceptibility and stenosis severity among three groups (healthy controls, patients with unilateral MCA stenosis degree <70% and ≥70%). In this model, susceptibility values recorded from 72 brain ROIs were treated as the dependent variable. The predictors included the grading of stenosis, age, sex, total intracranial volume, and hypertension. The same analysis was applied to CBF data. Partial correlation analyses were conducted to explore associations between stenosis degree, vessel wall enhancement, CBF, and susceptibility, adjusting for age, sex, and total intracranial volume (Spearman’s rank correlation coefficient: 0–0.30, weak correlation; 0.31–0.50, median correlation; >0.50, strong correlation). Inter-observer agreement was evaluated using the intra-class correlation coefficient (ICC) and the kappa test. Statistical significance was set at P<0.05, with analyses performed using Matlab 2010b (MathWorks, Natick, MA, USA) and IBM SPSS 26.0 (IBM Corp., Armonk, NY, USA).

Results

Demographic information

A total of 26 patients with unilateral MCA stenosis <70% {20 males; age [mean ± standard deviation (SD)]: 63.35±10.78 years; stenosis: 37.50% [median (Q1, Q3): 23.75–42.50%]}, 22 patients with MCA stenosis ≥70% {10 males; age (mean ± SD): 62.23±5.98 years; stenosis: 90.00% [median (Q1, Q3): 78.75–100%]}, and 24 healthy controls [8 males; age (mean ± SD): 64.54±10.95 years] were included. Of the 2 stenotic groups, 11 and 15 patients had left MCA stenosis, respectively. Significant differences were observed for sex, stenosis severity, and hypertension (P=0.006, P<0.001, P=0.022), but no other clinical variables differed among the groups. Detailed clinical characteristics are provided in Table 1.

Inter-observer agreement for susceptibility measurement

Intra-class and inter-class correlation coefficients for stenosis degree measurements were high (ICC =0.977 and 0.951, respectively). The agreement for enhancement grading was substantial (kappa =0.741, P<0.001).

Susceptibility/CBF comparisons between bilateral brain subregions of healthy controls

Paired t-tests showed no significant differences in susceptibility or CBF between bilateral regions in healthy controls, except for the precentral gyrus (P=0.043; Tables 2; Tables S3,S4).

Susceptibility/CBF comparisons between ipsilateral and contralateral brain subregions of patients with unilateral MCA stenosis

In the mild-moderate stenosis group (MCA stenosis <70%), higher susceptibility was observed in the ipsilateral globus pallidus and inferior temporal gyrus (P=0.003, P=0.004; Tables 2), while a decrease was noted in the red nucleus (P=0.044). In the severe stenosis group (MCA stenosis ≥70%), increased susceptibility was found in the ipsilateral caudate nucleus, putamen, globus pallidus, inferior temporal gyrus, and Heschl’s gyrus (P values <0.05, Tables 2).

CBF was reduced on the ipsilateral side in the putamen, globus pallidus, substantia nigra, red nucleus, and superior parietal lobule in the mild-moderate stenosis group. In the severe stenosis group, decreased CBF was noted in the ipsilateral globus pallidus, red nucleus, and superior parietal lobule, whereas increased CBF was seen in the superior frontal gyrus, middle frontal gyrus, postcentral gyrus, inferior parietal lobule, superior temporal gyrus, and middle temporal gyrus. Full details of regional differences are provided in the Supplementary Material (Tables S5-S8).

Susceptibility/CBF comparisons of brain subregions between patients and healthy controls

Except for the precentral gyrus, most brain subregions in healthy controls exhibited bilateral symmetry in susceptibility. A multiple linear regression analysis comparing the precentral gyrus across groups revealed no significant differences between the right precentral gyrus in healthy controls and the ipsilateral or contralateral sides in stenosis groups (P=0.568, P=0.619). Therefore, the susceptibility of each left subregion in healthy controls was used in further statistical analyses.

For patients with MCA stenosis <70%, higher susceptibility and lower CBF were observed in the ipsilateral middle frontal gyrus, paracentral lobule, inferior temporal gyrus, and insula compared to healthy controls (P values <0.05; Table 3). Similar findings were noted in the severe stenosis group for the putamen, globus pallidus, and insula. Ipsilateral susceptibility in the putamen was significantly higher in the severe stenosis group compared to the mild-moderate stenosis group (P<0.001). Meanwhile, the contralateral paracentral lobule exhibited higher susceptibility and lower CBF than the healthy controls (P=0.046, B=4.236; P=0.002, B=−13.948). Reduced susceptibility in the substantia nigra was observed in both ipsilateral and contralateral regions in the mild-to-moderate stenosis group (P=0.048, B=-14.920; P=0.037, B=−14.925). Details of regional comparisons are provided in the Supplementary Material (Tables S9,S10).

Association between brain subregional susceptibility, MCA stenosis degree, and MCA enhancement grade

In the ipsilateral subregions of stenosis patients, susceptibility in the putamen, globus pallidus, substantia nigra, dentate nucleus, and Heschl’s gyrus showed moderate-to-strong positive correlations with MCA stenosis degree (correlation coefficients: 0.627, 0.453, 0.345, 0.474, and 0.355, respectively; Figure 3). Conversely, negative correlations were observed in the middle occipital gyrus and cingulate gyrus (correlation coefficients: −0.304 and −0.411). A moderate negative correlation was found between CBF in the globus pallidus and stenosis degree (correlation coefficient =−0.379), while the superior frontal gyrus exhibited a moderate positive correlation (correlation coefficient =0.358). Details of Spearman analysis can be found in Table S11. Further, ipsilateral susceptibility of the putamen and stenosis degree were positively correlated with MCA vessel wall enhancement grade (correlation coefficients: 0.302 and 0.345). Ipsilateral CBF in the globus pallidus was negatively correlated with the enhancement grade (correlation coefficient: −0.342). Severe stenosis was associated with more pronounced vessel wall enhancement (Grade 2; Table 4; Figure 4).

Figure 3 Correlation between the susceptibility/CBF of brain subregions and the stenosis degree of the middle cerebral artery. In the ipsilateral sides of the stenosis group, susceptibility was correlated with the stenosis degree in the region of the putamen (A), globus pallidus (B), substantia nigra (C), dentate nucleus (D), Heschl’s gyrus (E), middle occipital gyrus (F), and cingulate gyrus (G) (r =0.627, 0.453, 0.345, 0.474, 0.355, −0.304 and −0.411; P values <0.05). CBF of globus pallidus (H) and superior frontal gyrus (I) were correlated with the stenosis degree (r =−0.379 and 0.358, respectively). CBF, cerebral blood flow.

Figure 4 Bar diagrams showing the distribution of patients with different vessel wall enhancement grades in the stenosis groups (degree <70% and degree ≥70%).

Discussion

To our knowledge, this study is the first to integrate ASL, HR-VWI, and QSM to analyze iron deposition variations across multiple cerebral regions in patients with chronic unilateral MCA stenosis. We found that sex and hypertension significantly differed between healthy controls and the two stenosis groups. Both stenosis groups exhibited abnormal iron deposition and altered CBF in specific brain regions, particularly those supplied by the MCA. However, only the bilateral susceptibility of the putamen differed between the two stenosis groups. Moderate-to-strong positive correlations were observed between susceptibility in regions such as the putamen, globus pallidus, substantia nigra, dentate nucleus and Heschl’s gyrus, and stenosis degree. Additionally, a moderate correlation was found between ipsilateral susceptibility in the putamen, CBF in the globus pallidus, stenosis degree, and vessel wall enhancement. The severe stenosis group also exhibited more prominent vessel wall enhancement compared to the mild-to-moderate stenosis group.

Du et al. (9) reported significantly higher susceptibility in the ipsilateral putamen in nine patients with unilateral MCA occlusion, a finding consistent with our severe stenosis group. Mao et al. also observed similar results in the putamen (8). In both Mao et al.’s (8) and our study, an elevated iron content in the globus pallidus was found, which contrasts with Du et al.’s (9) findings, possibly due to their smaller sample size. Our study, unlike Mao et al.’s (8), classified patients by stenosis severity, which may explain discrepancies in these findings.

In both intra- and inter-group comparisons, reciprocal susceptibility changes were observed: decreased values in posterior circulation nuclei (red nucleus/substantia nigra) contrasted with increased values in certain basal ganglia nuclei, which were predominantly associated with CBF alterations. This spatial reciprocity suggests that compensatory mechanisms may operate between the anteroposterior circulations and interhemispherically, likely mediated by hemodynamic adaptations. Mechanistically, these findings are supported by Das et al.’s demonstration of transferrin receptor 2 (TfR2)-mediated mitochondrial iron transport in dopaminergic neurons (22,23) and further explained by oxygen-dependent mitochondrial iron homeostasis, wherein hypoxia drives regional iron redistribution.

In cases of mild-to-moderate MCA stenosis, cortical regions—primarily supplied by the MCA—exhibited increased susceptibility compared to controls, implying iron accumulation. This pattern was absent in severe stenosis, with the exception of the insula, suggesting that cortical iron dysregulation in stenosis is distinct from that in deep gray matter nuclei. This difference is likely related to heterogeneous iron distribution—with higher concentrations in the basal ganglia and lower levels in the cortex—and regional variations in transferrin receptor expression (24). Furthermore, decreased CBF in MCA territories correlated with increased susceptibility, implying that hypoperfusion may impair iron clearance. Oxidative stress-induced astrocyte dysfunction might further disrupt iron metabolism (24). Conversely, in severe stenosis, the observed preservation (or even elevation) of CBF coupled with attenuated susceptibility changes strongly supports the protective role of collateral circulation. This compensatory mechanism appears to effectively counteract chronic hypoperfusion-induced hypoxia, consequently mitigating the pathological iron accumulation typically associated with ischemic conditions.

The blood-brain barrier (BBB) plays a critical role in regulating iron homeostasis between the circulation and brain tissue (25,26). HR-VWI-detected plaque enhancement may serve as a predictor for iron deposition risk in intracranial atherosclerosis through three interlinked mechanisms. First, endothelial dysfunction can impair BBB integrity (25,26), facilitating iron extravasation (27). Among ASL-based BBB imaging, a diffusion-prepared pseudo-continuous ASL technique has been developed to measure the water exchange rate across the BBB (28). Blood-derived substances, including fibrinogen, thrombin, hemoglobin, iron-containing hemosiderin, free iron, plasmin, environmental toxins and metals, can be regarded as an indirect biomarker for BBB dysfunction (29). Second, inflammation-mediated vascular remodeling may promote hemoglobin degradation and subsequent hemosiderin accumulation, as inflammatory cytokines induce changes in endothelial permeability and trigger the release of heme and other iron-containing compounds; this process may underlie the vessel wall enhancement observed with HR-VWI (30-33). Moreover, under ischemic/hypoxic conditions, regulated transmigration of immune cells (e.g., macrophages) from circulation into perivascular spaces—and potentially across the glial limitans—represents an active inflammatory process (34). Notably, macrophage-mediated vascular inflammation is radiographically detectable as vessel wall enhancement (33), providing mechanistic support for the observed correlation between vessel wall enhancement and iron deposition patterns in our study. Third, hemodynamic compromise—where severe stenosis (>70%) is associated with enhanced plaque activity (35) and downstream susceptibility changes—affects regions such as the basal ganglia, which have high transferrin receptor density and watershed vascularity (24), leading to iron accumulation when proximal MCA plaques enhance. Although current data reveal only a moderate correlation between enhancement intensity and putamen susceptibility, this likely reflects (I) BBB leakage preferentially impacting specific territories and (II) a temporal dissociation between plaque enhancement detectable by HR-VWI and iron deposition. Prospective studies should validate whether plaque enhancement precedes radiologically apparent iron accumulation, and dynamic vessel wall enhancement studies could be employed to investigate as evidence of quantitative assessment of BBB permeability (36), thereby establishing HR-VWI as a clinical predictor of neurotoxic iron risk.

There are several limitations in this study. The sample size was relatively small, and the resolution of HR-VWI limited the analysis to the M1 and M2 segments of the MCA. Additionally, cross-sectional design limits causal inferences, and longitudinal studies (such as longitudinal R2* studies) may further validate the relationship between chronic ischemia and iron deposition (37). Lastly, variations in the circle of Willis, though excluded from this study, could influence blood flow redistribution and should be explored further. Advanced ASL techniques, such as vessel-selective ASL, multi-post-labeling delay velocity-selective ASL, might facilitate more precise identification of collateral flow territories.

Conclusions

Distinct patterns of abnormal cerebral iron deposition were observed in chronic unilateral MCA stenosis, with notable interactions between stenosis severity, CBF, susceptibility, and vessel wall enhancement. This study may provide valuable insights into the mechanisms underlying iron accumulation in patients with intracranial artery stenosis.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-808/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-808/dss

Funding: This work was funded by Youth Fund of Zhongshan Hospital Fudan University (No. 2024ZSQN28) and Development Fund of Zhongshan Hospital Fudan University (No. 2023ZSFZ37).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-808/coif). P.W. was an employee of MR Research China of GE Healthcare throughout his involvement in the study. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Zhongshan Hospital, Shanghai, China (No. B2019-125R) and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Bersano A, Gatti L. Pathophysiology and Treatment of Stroke: Present Status and Future Perspectives. Int J Mol Sci 2023;24:14848. [Crossref] [PubMed]
2. Ran Y, Wang Y, Zhu M, Wu X, Malhotra A, Lei X, Zhang F, Wang X, Xie S, Zhou J, Zhu J, Cheng J, Zhu C. Higher Plaque Burden of Middle Cerebral Artery Is Associated With Recurrent Ischemic Stroke: A Quantitative Magnetic Resonance Imaging Study. Stroke 2020;51:659-62. [Crossref] [PubMed]
3. Meng Y, Yu K, Zhang L, Liu Y. Cognitive Decline in Asymptomatic Middle Cerebral Artery Stenosis Patients with Moderate and Poor Collaterals: A 2-Year Follow-Up Study. Med Sci Monit 2019;25:4051-8. [Crossref] [PubMed]
4. Sekerdag E, Solaroglu I, Gursoy-Ozdemir Y. Cell Death Mechanisms in Stroke and Novel Molecular and Cellular Treatment Options. Curr Neuropharmacol 2018;16:1396-415. [Crossref] [PubMed]
5. Cornelissen A, Guo L, Sakamoto A, Virmani R, Finn AV. New insights into the role of iron in inflammation and atherosclerosis. EBioMedicine 2019;47:598-606. [Crossref] [PubMed]
6. Vinayagamani S, Sheelakumari R, Sabarish S, Senthilvelan S, Ros R, Thomas B, Kesavadas C. Quantitative Susceptibility Mapping: Technical Considerations and Clinical Applications in Neuroimaging. J Magn Reson Imaging 2021;53:23-37. [Crossref] [PubMed]
7. Haacke EM, Liu S, Buch S, Zheng W, Wu D, Ye Y. Quantitative susceptibility mapping: current status and future directions. Magn Reson Imaging 2015;33:1-25. [Crossref] [PubMed]
8. Mao H, Dou W, Chen K, Wang X, Wang X, Guo Y, Zhang C. Evaluating iron deposition in gray matter nuclei of patients with unilateral middle cerebral artery stenosis using quantitative susceptibility mapping. Neuroimage Clin 2022;34:103021. [Crossref] [PubMed]
9. Du L, Zhao Z, Liu X, Chen Y, Gao W, Wang Y, Liu J, Liu B, Ma G. Alterations of Iron Level in the Bilateral Basal Ganglia Region in Patients With Middle Cerebral Artery Occlusion. Front Neurosci 2020;14:608058. [Crossref] [PubMed]
10. Mao H, Dou W, Wang X, Chen K, Wang X, Guo Y, Zhang C. Iron Deposition in Gray Matter Nuclei of Patients With Intracranial Artery Stenosis: A Quantitative Susceptibility Mapping Study. Front Neurol 2021;12:785822. [Crossref] [PubMed]
11. Li Z, Li N, Qu Y, Gai F, Zhang G, Zhang G. Application of 3.0T magnetic resonance arterial spin labeling (ASL) technology in mild and moderate intracranial atherosclerotic stenosis. Exp Ther Med 2016;12:297-301. [Crossref] [PubMed]
12. Chalela JA, Alsop DC, Gonzalez-Atavales JB, Maldjian JA, Kasner SE, Detre JA. Magnetic resonance perfusion imaging in acute ischemic stroke using continuous arterial spin labeling. Stroke 2000;31:680-7. [Crossref] [PubMed]
13. Iutaka T, de Freitas MB, Omar SS, Scortegagna FA, Nael K, Nunes RH, Pacheco FT, Maia Júnior ACM, do Amaral LLF, da Rocha AJ. Arterial Spin Labeling: Techniques, Clinical Applications, and Interpretation. Radiographics 2023;43:e220088. [Crossref] [PubMed]
14. Kucinski T, Koch C, Eckert B, Becker V, Krömer H, Heesen C, Grzyska U, Freitag HJ, Röther J, Zeumer H. Collateral circulation is an independent radiological predictor of outcome after thrombolysis in acute ischaemic stroke. Neuroradiology 2003;45:11-8. [Crossref] [PubMed]
15. Zaharchuk G, Do HM, Marks MP, Rosenberg J, Moseley ME, Steinberg GK. Arterial spin-labeling MRI can identify the presence and intensity of collateral perfusion in patients with moyamoya disease. Stroke 2011;42:2485-91. [Crossref] [PubMed]
16. Mandell DM, Mossa-Basha M, Qiao Y, Hess CP, Hui F, Matouk C, Johnson MH, Daemen MJ, Vossough A, Edjlali M, Saloner D, Ansari SA, Wasserman BA, Mikulis DJVessel Wall Imaging Study Group of the American Society of Neuroradiology. Intracranial Vessel Wall MRI: Principles and Expert Consensus Recommendations of the American Society of Neuroradiology. AJNR Am J Neuroradiol 2017;38:218-29. [Crossref] [PubMed]
17. Nörenberg D, Ebersberger HU, Diederichs G, Hamm B, Botnar RM, Makowski MR. Molecular magnetic resonance imaging of atherosclerotic vessel wall disease. Eur Radiol 2016;26:910-20. [Crossref] [PubMed]
18. Chalouhi N, Ali MS, Jabbour PM, Tjoumakaris SI, Gonzalez LF, Rosenwasser RH, Koch WJ, Dumont AS. Biology of intracranial aneurysms: role of inflammation. J Cereb Blood Flow Metab 2012;32:1659-76. [Crossref] [PubMed]
19. Wintermark M, Sanelli PC, Albers GW, Bello J, Derdeyn C, Hetts SW, Johnson MH, Kidwell C, Lev MH, Liebeskind DS, Rowley H, Schaefer PW, Sunshine JL, Zaharchuk G, Meltzer CC. Imaging recommendations for acute stroke and transient ischemic attack patients: A joint statement by the American Society of Neuroradiology, the American College of Radiology, and the Society of NeuroInterventional Surgery. AJNR Am J Neuroradiol 2013;34:E117-27. [Crossref] [PubMed]
20. Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 2013;12:822-38. [Crossref] [PubMed]
21. Ouyang F, Wang B, Wu Q, Yang Q, Meng X, Liu J, Xu Z, Lv L, Zeng X. Association of intravascular enhancement sign detected on high-resolution vessel wall imaging with ischaemic events in middle cerebral artery occlusion. Eur J Radiol 2023;165:110922. [Crossref] [PubMed]
22. Das A, Nag S, Mason AB, Barroso MM. Endosome-mitochondria interactions are modulated by iron release from transferrin. J Cell Biol 2016;214:831-45. [Crossref] [PubMed]
23. Mastroberardino PG, Hoffman EK, Horowitz MP, Betarbet R, Taylor G, Cheng D, Na HM, Gutekunst CA, Gearing M, Trojanowski JQ, Anderson M, Chu CT, Peng J, Greenamyre JT. A novel transferrin/TfR2-mediated mitochondrial iron transport system is disrupted in Parkinson's disease. Neurobiol Dis 2009;34:417-31. [Crossref] [PubMed]
24. Levi S, Ripamonti M, Moro AS, Cozzi A. Iron imbalance in neurodegeneration. Mol Psychiatry 2024;29:1139-52. [Crossref] [PubMed]
25. Faraci FM. Watching Small Vessel Disease Grow. Circ Res 2018;122:810-2. [Crossref] [PubMed]
26. Wong SM, Jansen JFA, Zhang CE, Hoff EI, Staals J, van Oostenbrugge RJ, Backes WH. Blood-brain barrier impairment and hypoperfusion are linked in cerebral small vessel disease. Neurology 2019;92:e1669-77. [Crossref] [PubMed]
27. Wang R, Wang M, Ye J, Sun G, Sun X. Mechanism overview and target mining of atherosclerosis: Endothelial cell injury in atherosclerosis is regulated by glycolysis Int J Mol Med 2021;47:65-76. (Review). [Crossref] [PubMed]
28. Shao X, Ma SJ, Casey M, D'Orazio L, Ringman JM, Wang DJJ. Mapping water exchange across the blood-brain barrier using 3D diffusion-prepared arterial spin labeled perfusion MRI. Magn Reson Med 2019;81:3065-79. [Crossref] [PubMed]
29. Zlokovic BV. Neurovascular pathways to neurodegeneration in Alzheimer's disease and other disorders. Nat Rev Neurosci 2011;12:723-38. [Crossref] [PubMed]
30. Rajeev V, Fann DY, Dinh QN, Kim HA, De Silva TM, Lai MKP, Chen CL, Drummond GR, Sobey CG, Arumugam TV. Pathophysiology of blood brain barrier dysfunction during chronic cerebral hypoperfusion in vascular cognitive impairment. Theranostics 2022;12:1639-58. [Crossref] [PubMed]
31. Ahmad S, Khan SA, Kindelin A, Mohseni T, Bhatia K, Hoda MN, Ducruet AF. Acetyl-11-keto-β-boswellic acid (AKBA) Attenuates Oxidative Stress, Inflammation, Complement Activation and Cell Death in Brain Endothelial Cells Following OGD/Reperfusion. Neuromolecular Med 2019;21:505-16. [Crossref] [PubMed]
32. McDonald DM. Angiogenesis and remodeling of airway vasculature in chronic inflammation. Am J Respir Crit Care Med 2001;164:S39-45. [Crossref] [PubMed]
33. Hamrick F, de Havenon A, Taussky P, Alexander MD, McNally JS, Grandhi R. Application of vessel wall magnetic resonance imaging in intracranial cerebrovascular pathology. J Neurosonol Neuroimag 2019;11:105-14.
34. Bijnen M, Sridhar S, Keller A, Greter M. Brain macrophages in vascular health and dysfunction. Trends Immunol 2025;46:46-60. [Crossref] [PubMed]
35. Ryu CW, Jahng GH, Shin HS. Gadolinium enhancement of atherosclerotic plaque in the middle cerebral artery: relation to symptoms and degree of stenosis. AJNR Am J Neuroradiol 2014;35:2306-10. [Crossref] [PubMed]
36. Heye AK, Culling RD, Valdés Hernández Mdel C, Thrippleton MJ, Wardlaw JM. Assessment of blood-brain barrier disruption using dynamic contrast-enhanced MRI. A systematic review. Neuroimage Clin 2014;6:262-74. [Crossref] [PubMed]
37. Uchida Y, Kan H, Kano Y, Onda K, Sakurai K, Takada K, Ueki Y, Matsukawa N, Hillis AE, Oishi K. Longitudinal Changes in Iron and Myelination Within Ischemic Lesions Associate With Neurological Outcomes: A Pilot Study. Stroke 2024;55:1041-50. [Crossref] [PubMed]