Thalamic Magnetic Susceptibility (χ) Alterations in Neurodegenerative Diseases: A Systematic Review and Meta-Analysis of Quantitative Susceptibility Mapping Studies
Corresponding Author
Sadegh Ghaderi
Neuromuscular Research Center, Department of Neurology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
Address reprint requests to: S.G., Neuromuscular Research Center, Department of Neurology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran. E-mail: [email protected]
Search for more papers by this authorSana Mohammadi
Neuromuscular Research Center, Department of Neurology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
Search for more papers by this authorAmir Mahmoud Ahmadzadeh
Department of Radiology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
Search for more papers by this authorKimia Darmiani
School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
Search for more papers by this authorMelika Arab Bafrani
Students' Scientific Research Center (SSRC), Tehran University of Medical Sciences, Tehran, Iran
Search for more papers by this authorNahid Jashirenezhad
The Persian Gulf Nuclear Medicine Research Center, Bushehr University of Medical Sciences, Bushehr, Iran
Search for more papers by this authorMaryam Helfi
Department of Medical Physics, School of Medicine, Mashhad University of Medical Science, Mashhad, Iran
Search for more papers by this authorSanaz Alibabaei
Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
Search for more papers by this authorSareh Azadi
Department of Biotechnology, Faculty of Allied Medicine, Iran University of Medical Science, Tehran, Iran
Search for more papers by this authorSahar Heidary
Health Institute, Medical Physics Department, Yeditepe University, Istanbul, Turkey
Search for more papers by this authorFarzad Fatehi
Neuromuscular Research Center, Department of Neurology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
Neurology Department, University Hospitals of Leicester NHS Trust, Leicester, UK
Search for more papers by this authorCorresponding Author
Sadegh Ghaderi
Neuromuscular Research Center, Department of Neurology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
Address reprint requests to: S.G., Neuromuscular Research Center, Department of Neurology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran. E-mail: [email protected]
Search for more papers by this authorSana Mohammadi
Neuromuscular Research Center, Department of Neurology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
Search for more papers by this authorAmir Mahmoud Ahmadzadeh
Department of Radiology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
Search for more papers by this authorKimia Darmiani
School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
Search for more papers by this authorMelika Arab Bafrani
Students' Scientific Research Center (SSRC), Tehran University of Medical Sciences, Tehran, Iran
Search for more papers by this authorNahid Jashirenezhad
The Persian Gulf Nuclear Medicine Research Center, Bushehr University of Medical Sciences, Bushehr, Iran
Search for more papers by this authorMaryam Helfi
Department of Medical Physics, School of Medicine, Mashhad University of Medical Science, Mashhad, Iran
Search for more papers by this authorSanaz Alibabaei
Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
Search for more papers by this authorSareh Azadi
Department of Biotechnology, Faculty of Allied Medicine, Iran University of Medical Science, Tehran, Iran
Search for more papers by this authorSahar Heidary
Health Institute, Medical Physics Department, Yeditepe University, Istanbul, Turkey
Search for more papers by this authorFarzad Fatehi
Neuromuscular Research Center, Department of Neurology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
Neurology Department, University Hospitals of Leicester NHS Trust, Leicester, UK
Search for more papers by this authorSana Mohammadi, Amir Mahmoud Ahmadzadeh, Kimia Darmiani, Melika Arab Bafrani, Nahid Jashirenezhad, Maryam Helfi, Sanaz Alibabaei, and Sareh Azadi contributed equally to this study.
Abstract
Background
Quantitative Susceptibility Mapping (QSM) provides a non-invasive post-processing method to investigate alterations in magnetic susceptibility (χ), reflecting iron content within brain regions implicated in neurodegenerative diseases (NDDs).
Purpose
To investigate alterations in thalamic χ in patients with NDDs using QSM.
Study Type
Systematic review and meta-analysis.
Population
A total of 696 patients with NDDs and 760 healthy controls (HCs) were included in 27 studies.
Field Strength/Sequence
Three-dimensional multi-echo gradient echo sequence for QSM at mostly 3 Tesla.
Assessment
Studies reporting QSM values in the thalamus of patients with NDDs were included. Following PRISMA 2020, we searched the four major databases including PubMed, Scopus, Web of Science, and Embase for peer-reviewed studies published until October 2024.
Statistical Tests
Meta-analysis was conducted using a random-effects model to calculate the standardized mean difference (SMD) between patients and HCs.
Results
The pooled SMD indicated a significant increase in thalamic χ in NDDs compared to HCs (SMD = 0.42, 95% CI: 0.05–0.79; k = 27). Notably, amyotrophic lateral sclerosis patients showed a significant increase in thalamic χ (1.09, 95% CI: 0.65–1.53, k = 2) compared to HCs. Subgroup analyses revealed significant χ alterations in younger patients (mean age ≤ 62 years; 0.56, 95% CI: 0.10–1.02, k = 11) and studies using greater coil channels (coil channels > 16; 0.64, 95% CI: 0.28–1.00, k = 9). Publication bias was not detected and quality assessment indicated that studies with a lower risk of bias presented more reliable findings (0.75, 95% CI: 0.32–1.18, k = 9). Disease type was the primary driver of heterogeneity, while other factors, such as coil type and geographic location, also contributed to variability.
Data Conclusion
Our findings support the potential of QSM for investigating thalamic involvement in NDDs. Future research should focus on disease-specific patterns, thalamic-specific nucleus analysis, and temporal evolution.
Plain Language Summary
Our research investigated changes in iron levels within the thalamus, a brain region crucial for motor and cognitive functions, in patients with various neurodegenerative diseases (NDDs). The study utilized a specific magnetic resonance imaging technique called Quantitative Susceptibility Mapping (QSM) to measure iron content. It identified a significant increase in thalamic iron levels in NDD patients compared to healthy individuals. This increase was particularly prominent in patients with Amyotrophic Lateral Sclerosis, younger individuals, and studies employing advanced imaging equipment.
Level of Evidence
2
Technical Efficacy
Stage 2
Conflict of Interest
The authors declare no conflict of interest.
Open Research
Data Availability Statement
This article contains all the data produced or analyzed during this investigation. Further inquiries should be forwarded to the corresponding author.
Supporting Information
Filename | Description |
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jmri29698-sup-0001-Supinfo.docxWord 2007 document , 530.6 KB | Figure S1. The assessment for sensitivity analysis. Table S1. The search strategies used for database searches. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
References
- 1Dev S, Babitt JL. Overview of iron metabolism in health and disease. Hemodial Int 2017; 21(Suppl 1): S6.
- 2Ward RJ, Zucca FA, Duyn JH, Crichton RR, Zecca L. The role of iron in brain ageing and neurodegenerative disorders. Lancet Neurol 2014; 13: 1045-1060.
- 3Ravanfar P, Loi SM, Syeda WT, et al. Systematic review: Quantitative susceptibility mapping (QSM) of brain iron profile in neurodegenerative diseases. Front Neurosci 2021; 15:618435.
- 4Bailo PS, Martín EL, Calmarza P, et al. The role of oxidative stress in neurodegenerative diseases and potential antioxidant therapies. Adv Lab Med 2022; 3: 342.
- 5Madden DJ, Merenstein JL. Quantitative susceptibility mapping of brain iron in healthy aging and cognition. Neuroimage 2023; 282:120401.
- 6Langkammer C, Schweser F, Krebs N, et al. Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study. Neuroimage 2012; 62: 1593-1599.
- 7Committee QCO, Bilgic B, Costagli M, et al. Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro-magnetic tissue properties study group. Magn Reson Med 2024; 91: 1834-1862.
- 8Liu C, Wei H, Gong N-J, Cronin M, Dibb R, Decker K. Quantitative susceptibility mapping: Contrast mechanisms and clinical applications. Tomography 2015; 1: 3-17.
- 9Li DT, Hui ES, Chan Q, et al. Quantitative susceptibility mapping as an indicator of subcortical and limbic iron abnormality in Parkinson's disease with dementia. NeuroImage Clin 2018; 20: 365-373.
- 10Watson GDR, Hughes RN, Petter EA, et al. Thalamic projections to the subthalamic nucleus contribute to movement initiation and rescue of parkinsonian symptoms. Sci Adv 2021; 7:eabe9192.
- 11Chipika RH, Finegan E, Li Hi Shing S, et al. “Switchboard” malfunction in motor neuron diseases: Selective pathology of thalamic nuclei in amyotrophic lateral sclerosis and primary lateral sclerosis. Neuroimage Clin 2020; 27:102300.
- 12Torrico TJ, Munakomi S. Neuroanatomy, thalamus. Treasure Island, FL: StatPearls Publishing; 2024.
- 13Ruiz-Perez M, Morell-Ortega S, Gadea M, et al. DeepThalamus: A novel deep learning method for automatic segmentation of brain thalamic nuclei from multimodal ultra-high resolution MRI. arXiv 2024. https://doi.org/10.48550/arXiv.2401.07751
10.48550/arXiv.2401.07751 Google Scholar
- 14Yu B, Li L, Guan X, et al. HybraPD atlas: Towards precise subcortical nuclei segmentation using multimodality medical images in patients with Parkinson disease. Hum Brain Mapp 2021; 42: 4399-4421.
- 15Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021; 372:n71.
- 16Wells GA, Shea B, O'Connell D, et al. The Newcastle-Ottawa scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Ottawa Hospital Research Institute; 2000. Available from: https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp
- 17Herzog R, Álvarez-Pasquin MJ, Díaz C, Del Barrio JL, Estrada JM, Gil Á. Are healthcare workers' intentions to vaccinate related to their knowledge, beliefs and attitudes? A systematic review. BMC Public Health 2013; 13: 154.
- 18Parasuaraman G, Ayyasamy L, Aune D, et al. The association between body mass index, abdominal fatness, and weight change and the risk of adult asthma: A systematic review and meta-analysis of cohort studies. Sci Rep 2023; 13: 7745.
- 19Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. New York: Routledge; 1988.
10.1046/j.1526-4610.2001.111006343.x Google Scholar
- 20Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med 2002; 21: 1539-1558.
- 21Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997; 315: 629-634.
- 22Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics 1994; 50: 1088-1101.
- 23Mori S, Wu D, Ceritoglu C, et al. MRICloud: Delivering high-throughput MRI neuroinformatics as cloud-based software as a service. Comput Sci Eng 2016; 18: 21-35.
- 24Li X, Chen L, Kutten K, et al. Multi-atlas tool for automated segmentation of brain gray matter nuclei and quantification of their magnetic susceptibility. Neuroimage 2019; 191: 337-349.
- 25Yan S, Lu J, Li Y, et al. Spatiotemporal patterns of brain iron-oxygen metabolism in patients with Parkinson's disease. Eur Radiol 2024; 34: 3074-3083.
- 26Ghaderi S, Fatehi F, Kalra S, Mohammadi S, Batouli SAH. Quantitative susceptibility mapping in amyotrophic lateral sclerosis: Automatic quantification of the magnetic susceptibility in the subcortical nuclei. Amyotroph Lateral Scler Frontotemporal Degener 2024; 1-12. https://doi.org/10.1080/21678421.2024.2372648.
- 27Meng H, Zhang D, Sun Q. The applied value in brain gray matter nuclei of patients with early-stage Parkinson's disease: A study based on multiple magnetic resonance imaging techniques. Head Face Med 2023; 19: 25.
- 28Zhang X, Li L, Qi L, et al. Distribution pattern of iron deposition in the basal ganglia of different motor subtypes of Parkinson's disease. Neurosci Lett 2023; 807:137249.
- 29Sharma B, Beaudin AE, Cox E, et al. Brain iron content in cerebral amyloid angiopathy using quantitative susceptibility mapping. Front Neurosci 2023; 17:1139988.
- 30Rong Y, Xu Z, Zhu Y, et al. Combination of quantitative susceptibility mapping and diffusion kurtosis imaging provides potential biomarkers for early-stage Parkinson's disease. ACS Chem Nerosci 2022; 13: 2699-2708.
- 31Zhao Y, Qu H, Wang W, et al. Assessing mild cognitive impairment in Parkinson's disease by magnetic resonance quantitative susceptibility mapping combined voxel-wise and radiomic analysis. Eur Neurol 2022; 85: 280-290.
- 32Kim M, Yoo S, Kim D, et al. Extra-basal ganglia iron content and non-motor symptoms in drug-naïve, early Parkinson's disease. Neurol Sci 2021; 42: 5297-5304.
- 33Dezortova M, Lescinskij A, Dusek P, et al. Multiparametric quantitative brain MRI in neurological and hepatic forms of Wilson's disease. J Magn Reson Imaging 2020; 51: 1829-1835.
- 34Li D, Liu Y, Zeng X, et al. Quantitative study of the changes in cerebral blood flow and iron deposition during progression of Alzheimer's disease. J Alzheimers Dis 2020; 78: 439-452.
- 35Ates S, Deistung A, Schneider R, et al. Characterization of iron accumulation in deep gray matter in myotonic dystrophy type 1 and 2 using quantitative susceptibility mapping and R2* relaxometry: A magnetic resonance imaging study at 3 Tesla. Front Neurol 2019; 10: 1320.
- 36Shahmaei V, Faeghi F, Mohammadbeigi A, Hashemi H, Ashrafi F. Evaluation of iron deposition in brain basal ganglia of patients with Parkinson's disease using quantitative susceptibility mapping. Eur J Radiol Open 2019; 6: 169-174.
- 37Uchida Y, Kan H, Sakurai K, et al. Voxel-based quantitative susceptibility mapping in Parkinson's disease with mild cognitive impairment. Mov Disord 2019; 34: 1164-1173.
- 38Ghassaban K, He N, Sethi SK, et al. Regional high iron in the substantia nigra differentiates Parkinson's disease patients from healthy controls. Front Aging Neurosci 2019; 11: 106.
- 39Chen Q, Chen Y, Zhang Y, et al. Iron deposition in Parkinson's disease by quantitative susceptibility mapping. BMC Neurosci 2019; 20: 1-8.
- 40Du L, Zhao Z, Cui A, et al. Increased iron deposition on brain quantitative susceptibility mapping correlates with decreased cognitive function in Alzheimer's disease. ACS Chem Nerosci 2018; 9: 1849-1857.
- 41Acosta-Cabronero J, Machts J, Schreiber S, et al. Quantitative susceptibility MRI to detect brain iron in amyotrophic lateral sclerosis. Radiology 2018; 289: 195-203.
- 42Kim H-G, Park S, Rhee HY, et al. Quantitative susceptibility mapping to evaluate the early stage of Alzheimer's disease. NeuroImage Clin 2017; 16: 429-438.
- 43Guan X, Xuan M, Gu Q, et al. Regionally progressive accumulation of iron in Parkinson's disease as measured by quantitative susceptibility mapping. NMR Biomed 2017; 30:e3489.
- 44Guan X, Xuan M, Gu Q, et al. Influence of regional iron on the motor impairments of Parkinson's disease: A quantitative susceptibility mapping study. J Magn Reson Imaging 2017; 45: 1335-1342.
- 45Acosta-Cabronero J, Cardenas-Blanco A, Betts MJ, et al. The whole-brain pattern of magnetic susceptibility perturbations in Parkinson's disease. Brain 2017; 140: 118-131.
- 46Langkammer C, Pirpamer L, Seiler S, et al. Quantitative susceptibility mapping in Parkinson's disease. PLoS One 2016; 11:e0162460.
- 47Domínguez JFD, Ng ACL, Poudel G, et al. Iron accumulation in the basal ganglia in Huntington's disease: Cross-sectional data from the IMAGE-HD study. J Neurol Neurosurg Psychiatry 2016; 87: 545-549.
- 48van Bergen JMG, Hua J, Unschuld PG, et al. Quantitative susceptibility mapping suggests altered brain iron in premanifest Huntington disease. AJNR Am J Neuroradiol 2016; 37: 789-796.
- 49Murakami Y, Kakeda S, Watanabe K, et al. Usefulness of quantitative susceptibility mapping for the diagnosis of Parkinson disease. AJNR Am J Neuroradiol 2015; 36: 1102-1108.
- 50Barbosa JHO, Santos AC, Tumas V, et al. Quantifying brain iron deposition in patients with Parkinson's disease using quantitative susceptibility mapping, R2 and R2. Magn Reson Imaging 2015; 33: 559-565.
- 51Li Q, Zhu W, Wen X, Zang Z, Da Y, Lu J. Beyond the motor cortex: Thalamic iron deposition accounts for disease severity in amyotrophic lateral sclerosis. Front Neurol 2022; 13:791300.
- 52De Reuck J, Devos D, Moreau C, et al. Topographic distribution of brain iron deposition and small cerebrovascular lesions in amyotrophic lateral sclerosis and in frontotemporal lobar degeneration: A post-mortem 7.0-tesla magnetic resonance imaging study with neuropathological correlates. Acta Neurol Belg 2017; 117: 873-878.
- 53Treit S, Naji N, Seres P, et al. R2* and quantitative susceptibility mapping in deep gray matter of 498 healthy controls from 5 to 90 years. Hum Brain Mapp 2021; 42: 4597-4610.
- 54Zhang Y, Wei H, Cronin MJ, He N, Yan F, Liu C. Longitudinal atlas for normative human brain development and aging over the lifespan using quantitative susceptibility mapping. Neuroimage 2018; 171: 176-189.
- 55Burgetova R, Dusek P, Burgetova A, et al. Age-related magnetic susceptibility changes in deep grey matter and cerebral cortex of normal young and middle-aged adults depicted by whole brain analysis. Quant Imaging Med Surg 2021; 11: 3906-3919.
- 56Hallgren B, Sourander P. The effect of age on the non-haemin iron in the human brain. J Neurochem 1958; 3: 41-51.
- 57Ghaderi S, Fatehi F, Kalra S, Batouli SAH. MRI biomarkers for memory-related impairment in amyotrophic lateral sclerosis: A systematic review. Amyotroph Lateral Scler Frontotemporal Degener 2023; 7–8: 1-17.
- 58Trojsi F, Caiazzo G, Siciliano M, et al. Microstructural correlates of Edinburgh cognitive and behavioural ALS screen (ECAS) changes in amyotrophic lateral sclerosis. Psychiatry Res Neuroimaging 2019; 288: 67-75.
- 59Christidi F, Karavasilis E, Zalonis I, et al. Memory-related white matter tract integrity in amyotrophic lateral sclerosis: An advanced neuroimaging and neuropsychological study. Neurobiol Aging 2017; 49: 69-78.
- 60Zhou W, Shen B, Shen W-Q, Chen H, Zheng Y-F, Fei J-J. Dysfunction of the glymphatic system might be related to iron deposition in the normal aging brain. Front Aging Neurosci 2020; 12:559603.
- 61Chen Y, Guo X, Zeng Y, et al. Oxidative stress induces mitochondrial iron overload and ferroptotic cell death. Sci Rep 2023; 13: 15515.
- 62Liu C, Li W, Johnson GA, Wu B. High-field (9.4 T) MRI of brain dysmyelination by quantitative mapping of magnetic susceptibility. Neuroimage 2011; 56: 930-938.
- 63Lim IAL, Faria AV, Li X, et al. Human brain atlas for automated region of interest selection in quantitative susceptibility mapping: Application to determine iron content in deep gray matter structures. Neuroimage 2013; 82: 449-469.
- 64Liu J, Liu T, de Rochefort L, et al. Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map. Neuroimage 2011; 59: 2560-2568.
- 65Ghaderi S, Mohammadi S, Nezhad NJ, Karami S, Sayehmiri F. Iron quantification in basal ganglia: Quantitative susceptibility mapping as a potential biomarker for Alzheimer's disease—A systematic review and meta-analysis. Front Neurosci 2024; 18:1338891.
- 66Jin J, Su D, Zhang J, Lam JST, Zhou J, Feng T. Iron deposition in subcortical nuclei of Parkinson's disease: A meta-analysis of quantitative iron-sensitive magnetic resonance imaging studies. Chin Med J (Engl) 2024; 10:1097.
- 67Gong N-J, Wong C-S, Hui ES, Chan C-C, Leung L-M. Hemisphere, gender and age-related effects on iron deposition in deep gray matter revealed by quantitative susceptibility mapping. NMR Biomed 2015; 28: 1267-1274.
- 68Taege Y, Hagemeier J, Bergsland N, et al. Assessment of mesoscopic properties of deep gray matter iron through a model-based simultaneous analysis of magnetic susceptibility and R2*—A pilot study in patients with multiple sclerosis and normal controls. Neuroimage 2019; 186: 308-320.
- 69Choi EY, Tian L, Su JH, et al. Thalamic nuclei atrophy at high and heterogenous rates during cognitively unimpaired human aging. Neuroimage 2022; 262:119584.
- 70Kumar VJ, Scheffler K, Hagberg GE, Grodd W. Quantitative susceptibility mapping of the basal ganglia and thalamus at 9.4 tesla. Front Neuroanat 2021; 15:725731.
- 71Hagemeier J, Dwyer MG, Bergsland N, et al. Effect of age on MRI phase behavior in the subcortical deep gray matter of healthy individuals. AJNR Am J Neuroradiol 2013; 34: 2144-2151.
- 72Betts MJ, Acosta-Cabronero J, Cardenas-Blanco A, Nestor PJ, Düzel E. High-resolution characterisation of the aging brain using simultaneous quantitative susceptibility mapping (QSM) and R2* measurements at 7T. Neuroimage 2016; 138: 43-63.
- 73Hagemeier J, Zivadinov R, Dwyer MG, et al. Changes of deep gray matter magnetic susceptibility over 2 years in multiple sclerosis and healthy control brain. Neuroimage Clin 2018; 18: 1007-1016.
- 74Chiang GC, Hu J, Morris E, Wang Y, Gauthier SA. Quantitative susceptibility mapping of the thalamus: Relationships with thalamic volume, total gray matter volume, and T2 lesion burden. AJNR Am J Neuroradiol 2018; 39: 467-472.
- 75Acosta-Cabronero J, Betts MJ, Cardenas-Blanco A, Yang S, Nestor PJ. In vivo MRI mapping of brain iron deposition across the adult lifespan. J Neurosci 2016; 36: 364-374.
- 76Lehman VT, Lee KH, Klassen BT, et al. MRI and tractography techniques to localize the ventral intermediate nucleus and dentatorubrothalamic tract for deep brain stimulation and MR-guided focused ultrasound: A narrative review and update. Neurosurg Focus 2020; 49: E8.
- 77Robinson SD, Bredies K, Khabipova D, Dymerska B, Marques JP, Schweser F. An illustrated comparison of processing methods for MR phase imaging and QSM: Combining array coil signals and phase unwrapping. NMR Biomed 2017; 30:e3601.
- 78Chen J, Gong N-J, Chaim KT, Otaduy MCG, Liu C. Decompose quantitative susceptibility mapping (QSM) to sub-voxel diamagnetic and paramagnetic components based on gradient-echo MRI data. Neuroimage 2021; 242:118477.
- 79Li Z, Feng R, Liu Q, et al. APART-QSM: An improved sub-voxel quantitative susceptibility mapping for susceptibility source separation using an iterative data fitting method. Neuroimage 2023; 274:120148.
- 80Ahmed M, Chen J, Arani A, et al. The diamagnetic component map from quantitative susceptibility mapping (QSM) source separation reveals pathological alteration in Alzheimer's disease-driven neurodegeneration. Neuroimage 2023; 280:120357.
- 81Lao G, Liu Q, Li Z, et al. Sub-voxel quantitative susceptibility mapping for assessing whole-brain magnetic susceptibility from ages 4 to 80. Hum Brain Mapp 2023; 44: 5953-5971.
- 82Sood S, Urriola J, Reutens D, et al. Echo time-dependent quantitative susceptibility mapping contains information on tissue properties. Magn Reson Med 2017; 77: 1946-1958.