1
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Nguyen TTT, Greene LA, Mnatsakanyan H, Badr CE. Revolutionizing Brain Tumor Care: Emerging Technologies and Strategies. Biomedicines 2024; 12:1376. [PMID: 38927583 PMCID: PMC11202201 DOI: 10.3390/biomedicines12061376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 06/16/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024] Open
Abstract
Glioblastoma multiforme (GBM) is one of the most aggressive forms of brain tumor, characterized by a daunting prognosis with a life expectancy hovering around 12-16 months. Despite a century of relentless research, only a select few drugs have received approval for brain tumor treatment, largely due to the formidable barrier posed by the blood-brain barrier. The current standard of care involves a multifaceted approach combining surgery, irradiation, and chemotherapy. However, recurrence often occurs within months despite these interventions. The formidable challenges of drug delivery to the brain and overcoming therapeutic resistance have become focal points in the treatment of brain tumors and are deemed essential to overcoming tumor recurrence. In recent years, a promising wave of advanced treatments has emerged, offering a glimpse of hope to overcome the limitations of existing therapies. This review aims to highlight cutting-edge technologies in the current and ongoing stages of development, providing patients with valuable insights to guide their choices in brain tumor treatment.
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Affiliation(s)
- Trang T. T. Nguyen
- Ronald O. Perelman Department of Dermatology, Perlmutter Cancer Center, NYU Grossman School of Medicine, NYU Langone Health, New York, NY 10016, USA
| | - Lloyd A. Greene
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY 10032, USA;
| | - Hayk Mnatsakanyan
- Department of Neurology, Massachusetts General Hospital, Neuroscience Program, Harvard Medical School, Boston, MA 02129, USA; (H.M.); (C.E.B.)
| | - Christian E. Badr
- Department of Neurology, Massachusetts General Hospital, Neuroscience Program, Harvard Medical School, Boston, MA 02129, USA; (H.M.); (C.E.B.)
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2
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Radunsky D, Solomon C, Stern N, Blumenfeld-Katzir T, Filo S, Mezer A, Karsa A, Shmueli K, Soustelle L, Duhamel G, Girard OM, Kepler G, Shrot S, Hoffmann C, Ben-Eliezer N. A comprehensive protocol for quantitative magnetic resonance imaging of the brain at 3 Tesla. PLoS One 2024; 19:e0297244. [PMID: 38820354 PMCID: PMC11142522 DOI: 10.1371/journal.pone.0297244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Accepted: 01/01/2024] [Indexed: 06/02/2024] Open
Abstract
Quantitative MRI (qMRI) has been shown to be clinically useful for numerous applications in the brain and body. The development of rapid, accurate, and reproducible qMRI techniques offers access to new multiparametric data, which can provide a comprehensive view of tissue pathology. This work introduces a multiparametric qMRI protocol along with full postprocessing pipelines, optimized for brain imaging at 3 Tesla and using state-of-the-art qMRI tools. The total scan time is under 50 minutes and includes eight pulse-sequences, which produce range of quantitative maps including T1, T2, and T2* relaxation times, magnetic susceptibility, water and macromolecular tissue fractions, mean diffusivity and fractional anisotropy, magnetization transfer ratio (MTR), and inhomogeneous MTR. Practical tips and limitations of using the protocol are also provided and discussed. Application of the protocol is presented on a cohort of 28 healthy volunteers and 12 brain regions-of-interest (ROIs). Quantitative values agreed with previously reported values. Statistical analysis revealed low variability of qMRI parameters across subjects, which, compared to intra-ROI variability, was x4.1 ± 0.9 times higher on average. Significant and positive linear relationship was found between right and left hemispheres' values for all parameters and ROIs with Pearson correlation coefficients of r>0.89 (P<0.001), and mean slope of 0.95 ± 0.04. Finally, scan-rescan stability demonstrated high reproducibility of the measured parameters across ROIs and volunteers, with close-to-zero mean difference and without correlation between the mean and difference values (across map types, mean P value was 0.48 ± 0.27). The entire quantitative data and postprocessing scripts described in the manuscript are publicly available under dedicated GitHub and Figshare repositories. The quantitative maps produced by the presented protocol can promote longitudinal and multi-center studies, and improve the biological interpretability of qMRI by integrating multiple metrics that can reveal information, which is not apparent when examined using only a single contrast mechanism.
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Affiliation(s)
- Dvir Radunsky
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | - Chen Solomon
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | - Neta Stern
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | | | - Shir Filo
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Aviv Mezer
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Anita Karsa
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | | | | | | | - Gal Kepler
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- School of Neurobiology, Biochemistry and Biophysics, Faculty of Life Science, Tel Aviv University, Tel Aviv, Israel
| | - Shai Shrot
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel
| | - Chen Hoffmann
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel
| | - Noam Ben-Eliezer
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Center for Advanced Imaging Innovation and Research (CAI2R), New-York University Langone Medical Center, New York, NY, United States of America
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3
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Reiter JT, Schulte F, Bauer T, David B, Endler C, Isaak A, Schuch F, Bitzer F, Witt JA, Hattingen E, Deichmann R, Attenberger U, Becker AJ, Helmstaedter C, Radbruch A, Surges R, Friedman A, Rüber T. Evidence for interictal blood-brain barrier dysfunction in people with epilepsy. Epilepsia 2024; 65:1462-1474. [PMID: 38436479 DOI: 10.1111/epi.17929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 02/11/2024] [Accepted: 02/12/2024] [Indexed: 03/05/2024]
Abstract
OBJECTIVE Interictal blood-brain barrier dysfunction in chronic epilepsy has been demonstrated in animal models and pathological specimens. Ictal blood-brain barrier dysfunction has been shown in humans in vivo using an experimental quantitative magnetic resonance imaging (MRI) protocol. Here, we hypothesized that interictal blood-brain barrier dysfunction is also present in people with drug-resistant epilepsy. METHODS Thirty-nine people (21 females, mean age at MRI ± SD = 30 ± 8 years) with drug-resistant epilepsy were prospectively recruited and underwent interictal T1-relaxometry before and after administration of a paramagnetic contrast agent. Likewise, quantitative T1 was acquired in 29 people without epilepsy (12 females, age at MRI = 48 ± 18 years). Quantitative T1 difference maps were calculated and served as a surrogate imaging marker for blood-brain barrier dysfunction. Values of quantitative T1 difference maps inside hemispheres ipsilateral to the presumed seizure onset zone were then compared, on a voxelwise level and within presumed seizure onset zones, to the contralateral side of people with epilepsy and to people without epilepsy. RESULTS Compared to the contralateral side, ipsilateral T1 difference values were significantly higher in white matter (corrected p < .05), gray matter (uncorrected p < .05), and presumed seizure onset zones (p = .04) in people with epilepsy. Compared to people without epilepsy, significantly higher T1 difference values were found in the anatomical vicinity of presumed seizure onset zones (p = .004). A subgroup of people with hippocampal sclerosis demonstrated significantly higher T1 difference values in the ipsilateral hippocampus and in regions strongly interconnected with the hippocampus compared to people without epilepsy (corrected p < .01). Finally, z-scores reflecting the deviation of T1 difference values within the presumed seizure onset zone were associated with verbal memory performance (p = .02) in people with temporal lobe epilepsy. SIGNIFICANCE Our results indicate a blood-brain barrier dysfunction in drug-resistant epilepsy that is detectable interictally in vivo, anatomically related to the presumed seizure onset zone, and associated with cognitive deficits.
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Affiliation(s)
- Johannes T Reiter
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Freya Schulte
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Tobias Bauer
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Bastian David
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Christoph Endler
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Alexander Isaak
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Fabiane Schuch
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Felix Bitzer
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
| | | | - Elke Hattingen
- Institute of Neuroradiology, University Hospital and Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Ralf Deichmann
- Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Ulrike Attenberger
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Albert J Becker
- Department of Neuropathology, University Hospital Bonn, Bonn, Germany
| | | | | | - Rainer Surges
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Alon Friedman
- Department of Medical Neuroscience, Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada
- Departments of Cognitive and Brain Sciences, Physiology, and Cell Biology, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Theodor Rüber
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
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4
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Arias-Ramos N, Vieira C, Pérez-Carro R, López-Larrubia P. Integrative Magnetic Resonance Imaging and Metabolomic Characterization of a Glioblastoma Rat Model. Brain Sci 2024; 14:409. [PMID: 38790388 PMCID: PMC11118082 DOI: 10.3390/brainsci14050409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 04/14/2024] [Accepted: 04/18/2024] [Indexed: 05/26/2024] Open
Abstract
Glioblastoma (GBM) stands as the most prevalent and lethal malignant brain tumor, characterized by its highly infiltrative nature. This study aimed to identify additional MRI and metabolomic biomarkers of GBM and its impact on healthy tissue using an advanced-stage C6 glioma rat model. Wistar rats underwent a stereotactic injection of C6 cells (GBM group, n = 10) or cell medium (sham group, n = 4). A multiparametric MRI, including anatomical T2W and T1W images, relaxometry maps (T2, T2*, and T1), the magnetization transfer ratio (MTR), and diffusion tensor imaging (DTI), was performed. Additionally, ex vivo magnetic resonance spectroscopy (MRS) HRMAS spectra were acquired. The MRI analysis revealed significant differences in the T2 maps, T1 maps, MTR, and mean diffusivity parameters between the GBM tumor and the rest of the studied regions, which were the contralateral areas of the GBM rats and both regions of the sham rats (the ipsilateral and contralateral). The ex vivo spectra revealed markers of neuronal loss, apoptosis, and higher glucose uptake by the tumor. Notably, the myo-inositol and phosphocholine levels were elevated in both the tumor and the contralateral regions of the GBM rats compared to the sham rats, suggesting the effects of the tumor on the healthy tissue. The MRI parameters related to inflammation, cellularity, and tissue integrity, along with MRS-detected metabolites, serve as potential biomarkers for the tumor evolution, treatment response, and impact on healthy tissue. These techniques can be potent tools for evaluating new drugs and treatment targets.
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Affiliation(s)
| | | | | | - Pilar López-Larrubia
- Instituto de Investigaciones Biomédicas Sols-Morreale, Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid (CSIC-UAM), 28029 Madrid, Spain; (N.A.-R.)
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5
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Ballestín A, Armocida D, Ribecco V, Seano G. Peritumoral brain zone in glioblastoma: biological, clinical and mechanical features. Front Immunol 2024; 15:1347877. [PMID: 38487525 PMCID: PMC10937439 DOI: 10.3389/fimmu.2024.1347877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 02/14/2024] [Indexed: 03/17/2024] Open
Abstract
Glioblastoma is a highly aggressive and invasive tumor that affects the central nervous system (CNS). With a five-year survival rate of only 6.9% and a median survival time of eight months, it has the lowest survival rate among CNS tumors. Its treatment consists of surgical resection, subsequent fractionated radiotherapy and concomitant and adjuvant chemotherapy with temozolomide. Despite the implementation of clinical interventions, recurrence is a common occurrence, with over 80% of cases arising at the edge of the resection cavity a few months after treatment. The high recurrence rate and location of glioblastoma indicate the need for a better understanding of the peritumor brain zone (PBZ). In this review, we first describe the main radiological, cellular, molecular and biomechanical tissue features of PBZ; and subsequently, we discuss its current clinical management, potential local therapeutic approaches and future prospects.
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Affiliation(s)
- Alberto Ballestín
- Tumor Microenvironment Laboratory, UMR3347 CNRS/U1021 INSERM, Institut Curie, Orsay, France
| | - Daniele Armocida
- Human Neurosciences Department, Neurosurgery Division, Sapienza University, Rome, Italy
| | - Valentino Ribecco
- Tumor Microenvironment Laboratory, UMR3347 CNRS/U1021 INSERM, Institut Curie, Orsay, France
| | - Giorgio Seano
- Tumor Microenvironment Laboratory, UMR3347 CNRS/U1021 INSERM, Institut Curie, Orsay, France
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6
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Hirschler L, Sollmann N, Schmitz‐Abecassis B, Pinto J, Arzanforoosh F, Barkhof F, Booth T, Calvo‐Imirizaldu M, Cassia G, Chmelik M, Clement P, Ercan E, Fernández‐Seara MA, Furtner J, Fuster‐Garcia E, Grech‐Sollars M, Guven NT, Hatay GH, Karami G, Keil VC, Kim M, Koekkoek JAF, Kukran S, Mancini L, Nechifor RE, Özcan A, Ozturk‐Isik E, Piskin S, Schmainda K, Svensson SF, Tseng C, Unnikrishnan S, Vos F, Warnert E, Zhao MY, Jancalek R, Nunes T, Emblem KE, Smits M, Petr J, Hangel G. Advanced MR Techniques for Preoperative Glioma Characterization: Part 1. J Magn Reson Imaging 2023; 57:1655-1675. [PMID: 36866773 PMCID: PMC10946498 DOI: 10.1002/jmri.28662] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 03/04/2023] Open
Abstract
Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2.
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Affiliation(s)
- Lydiane Hirschler
- C.J. Gorter MRI Center, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Nico Sollmann
- Department of Diagnostic and Interventional RadiologyUniversity Hospital UlmUlmGermany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der IsarTechnical University of MunichMunichGermany
- TUM‐Neuroimaging Center, Klinikum rechts der IsarTechnical University of MunichMunichGermany
| | - Bárbara Schmitz‐Abecassis
- Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
- Medical Delta FoundationDelftThe Netherlands
| | - Joana Pinto
- Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
| | | | - Frederik Barkhof
- Department of Radiology & Nuclear MedicineAmsterdam UMC, Vrije UniversiteitAmsterdamThe Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image ComputingUniversity College LondonLondonUK
| | - Thomas Booth
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Department of NeuroradiologyKing's College Hospital NHS Foundation TrustLondonUK
| | | | | | - Marek Chmelik
- Department of Technical Disciplines in Medicine, Faculty of Health CareUniversity of PrešovPrešovSlovakia
| | - Patricia Clement
- Department of Diagnostic SciencesGhent UniversityGhentBelgium
- Department of Medical ImagingGhent University HospitalGhentBelgium
| | - Ece Ercan
- Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Maria A. Fernández‐Seara
- Department of RadiologyClínica Universidad de NavarraPamplonaSpain
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
| | - Julia Furtner
- Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
- Research Center of Medical Image Analysis and Artificial IntelligenceDanube Private UniversityKrems an der DonauAustria
| | - Elies Fuster‐Garcia
- Biomedical Data Science Laboratory, Instituto Universitario de Tecnologías de la Información y ComunicacionesUniversitat Politècnica de ValènciaValenciaSpain
| | - Matthew Grech‐Sollars
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and NeurosurgeryUniversity College London Hospitals NHS Foundation TrustLondonUK
| | - Nazmiye Tugay Guven
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Gokce Hale Hatay
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Golestan Karami
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Vera C. Keil
- Department of Radiology & Nuclear MedicineAmsterdam UMC, Vrije UniversiteitAmsterdamThe Netherlands
- Cancer Center AmsterdamAmsterdamThe Netherlands
| | - Mina Kim
- Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering and Department of NeuroinflammationUniversity College LondonLondonUK
| | - Johan A. F. Koekkoek
- Department of NeurologyLeiden University Medical CenterLeidenThe Netherlands
- Department of NeurologyHaaglanden Medical CenterThe HagueThe Netherlands
| | - Simran Kukran
- Department of BioengineeringImperial College LondonLondonUK
- Department of Radiotherapy and ImagingInstitute of Cancer ResearchLondonUK
| | - Laura Mancini
- Lysholm Department of Neuroradiology, National Hospital for Neurology and NeurosurgeryUniversity College London Hospitals NHS Foundation TrustLondonUK
- Department of Brain Repair and Rehabilitation, Institute of NeurologyUniversity College LondonLondonUK
| | - Ruben Emanuel Nechifor
- Department of Clinical Psychology and PsychotherapyInternational Institute for the Advanced Studies of Psychotherapy and Applied Mental Health, Babes‐Bolyai UniversityCluj‐NapocaRomania
| | - Alpay Özcan
- Electrical and Electronics Engineering DepartmentBogazici University IstanbulIstanbulTurkey
| | - Esin Ozturk‐Isik
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Senol Piskin
- Department of Mechanical Engineering, Faculty of Natural Sciences and EngineeringIstinye University IstanbulIstanbulTurkey
| | - Kathleen Schmainda
- Department of BiophysicsMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Siri F. Svensson
- Department of Physics and Computational RadiologyOslo University HospitalOsloNorway
- Department of PhysicsUniversity of OsloOsloNorway
| | - Chih‐Hsien Tseng
- Medical Delta FoundationDelftThe Netherlands
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | - Saritha Unnikrishnan
- Faculty of Engineering and DesignAtlantic Technological University (ATU) SligoSligoIreland
- Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), ATU SligoSligoIreland
| | - Frans Vos
- Medical Delta FoundationDelftThe Netherlands
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamThe Netherlands
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | - Esther Warnert
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamThe Netherlands
| | - Moss Y. Zhao
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
- Stanford Cardiovascular InstituteStanford UniversityStanfordCaliforniaUSA
| | - Radim Jancalek
- Department of NeurosurgerySt. Anne's University Hospital, BrnoBrnoCzech Republic
- Faculty of Medicine, Masaryk UniversityBrnoCzech Republic
| | - Teresa Nunes
- Department of NeuroradiologyHospital Garcia de OrtaAlmadaPortugal
| | - Kyrre E. Emblem
- Department of Physics and Computational RadiologyOslo University HospitalOsloNorway
| | - Marion Smits
- Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamThe Netherlands
- Brain Tumour CentreErasmus MC Cancer InstituteRotterdamThe Netherlands
| | - Jan Petr
- Helmholtz‐Zentrum Dresden‐RossendorfInstitute of Radiopharmaceutical Cancer ResearchDresdenGermany
| | - Gilbert Hangel
- Department of NeurosurgeryMedical University of ViennaViennaAustria
- High Field MR Centre, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
- Christian Doppler Laboratory for MR Imaging BiomarkersViennaAustria
- Medical Imaging ClusterMedical University of ViennaViennaAustria
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Wen B, Zhang Z, Fu K, Zhu J, Liu L, Gao E, Qi J, Zhang Y, Cheng J, Qu F, Zhu J. Value of pre-/post-contrast-enhanced T1 mapping and readout segmentation of long variable echo-train diffusion-weighted imaging in differentiating parotid gland tumors. Eur J Radiol 2023; 162:110748. [PMID: 36905715 DOI: 10.1016/j.ejrad.2023.110748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 01/29/2023] [Accepted: 02/13/2023] [Indexed: 02/25/2023]
Abstract
PURPOSE This study aimed to explore the value of pre-/post-contrast-enhanced T1 mapping and readout segmentation of long variable echo-train diffusion-weighted imaging (RESOLVE-DWI) for the differential diagnosis of parotid gland tumors. METHODS A total of 128 patients with histopathologically confirmed parotid gland tumors [86 benign tumors (BTs) and 42 malignant tumors (MTs)] were retrospectively recruited. BTs were further divided into pleomorphic adenomas (PAs, n = 57) and Warthin's tumors (WTs, n = 15). MRI examinations were performed before and after contrast injection to measure the longitudinal relaxation time (T1) value (T1p and T1e, respectively) and the apparent diffusion coefficient (ADC) value of the parotid gland tumors. The reduction in T1 (T1d) values and the percentage of T1 reduction (T1d%) were calculated. RESULTS The T1d and ADC values of the BTs were considerably higher than those of the MTs (all P <.05). The area under the curve (AUC) of the T1d and ADC values for differentiating between BTs and MTs of the parotid was 0.618 and 0.804, respectively (all P <.05). The AUC of the T1p, T1d, T1d%, and ADC values for differentiating between PAs and WTs was 0.926, 0.945, 0.925, and 0.996, respectively (all P >.05). The ADC and T1d% + ADC values performed better in differentiating between PAs and MTs than the T1p, T1d, and T1d% (AUC values: 0.902, 0.909, 0.660, 0.726, and 0.736, respectively). The T1p, T1d, T1d%, and T1d% + T1p values all had high diagnosis efficacy in differentiating WTs from MTs (AUC values: 0.865, 0.890, 0.852, and 0.897, respectively, all P >.05). CONCLUSION T1 mapping and RESOLVE-DWI can be used to differentiate parotid gland tumors quantitatively and can be complementary to each other.
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Affiliation(s)
- Baohong Wen
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Zanxia Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Kun Fu
- Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Jing Zhu
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Liang Liu
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Eryuan Gao
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Jinbo Qi
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Yong Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China.
| | - Feifei Qu
- MR Collaboration, Siemens Healthnieer Ltd., Beijing, China
| | - Jinxia Zhu
- MR Collaboration, Siemens Healthnieer Ltd., Beijing, China
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8
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Baidya Kayal E, Sharma N, Sharma R, Bakhshi S, Kandasamy D, Mehndiratta A. T1 mapping as a surrogate marker of chemotherapy response evaluation in patients with osteosarcoma. Eur J Radiol 2022; 148:110170. [DOI: 10.1016/j.ejrad.2022.110170] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 01/14/2022] [Accepted: 01/17/2022] [Indexed: 12/25/2022]
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9
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Ge X, Wang M, Ma H, Zhu K, Wei X, Li M, Zhai X, Shen Y, Huang X, Hou M, Liu W, Wang M, Wang X. Investigated diagnostic value of synthetic relaxometry, three-dimensional pseudo-continuous arterial spin labelling and diffusion-weighted imaging in the grading of glioma. Magn Reson Imaging 2021; 86:20-27. [PMID: 34808303 DOI: 10.1016/j.mri.2021.11.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 09/27/2021] [Accepted: 11/15/2021] [Indexed: 10/19/2022]
Abstract
BACKGROUND To investigate the performance of synthetic relaxometry, three-dimensional pseudo-continuous arterial spin labelling (pCASL) and diffusion-weighted imaging (DWI) in differentiating high-grade gliomas (HGGs) from low-grade gliomas (LGGs) and to compare with the conventional MRI. METHODS Seventy-two patients with gliomas (including 27 LGGs and 45 HGGs) were studied using synthetic magnetic resonance imaging (sy-MRI), pCASL, and DWI with a 3.0 T MR scanner. T1 relaxometry (T1), T2 relaxometry (T2), as well as proton density (PD) from sy-MRI, cerebral blood flow (CBF) from pCASL, apparent diffusion coefficient (ADC) from DWI and enhancement quality (EQ), proportion enhancing (PE) from conventional contrast enhanced image based Visually-Accessible-Rembrandt-Images (VASARI) scoring system, were all analyzed by two radiologists. The Student's t-test, Mann-Whitney U test or Fisher's exact test was used to compare the parameters between LGGs and HGGs. The diagnostic performance of each parameter and their combination for glioma grading were analyzed. RESULTS Significant statistical differences in T1, PD, CBF, ADC, EQ and PE are observed between LGGs and HGGs (all P < 0.001). The ADC values have higher discrimination abilities compared with other univariable parameters, with the AUC of 0.905. AUC values for conventional contrast-enhanced method, EQ and PE from VASARI, and conventional contrast-free method, CBF + ADC, are 0.873 and 0.912 respectively. The combined T1, PD, CBF and ADC model had the best performance for differentiating LGGs and HGGs with AUC, sensitivity and specificity of 0.993, 95.5%, 100%, respectively. CONCLUSIONS Relaxometry parameters derived from synthetic MRI contributed to the discrimination of low-grade gliomas from high-grade gliomas. Proposed contrast-free approach combining T1, PD, CBF and ADC showed a strong discriminative power, and outperformed conventional approaches.
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Affiliation(s)
- Xin Ge
- School of Clinical Medicine, Ningxia Medical University, Yinchuan, China
| | - Minglei Wang
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Hui Ma
- Department of Neurosurgery, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Kai Zhu
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China
| | | | - Min Li
- GE Healthcare, MR Enhancement Application, Beijing, China
| | - Xuefeng Zhai
- Department of Pathology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Ying Shen
- School of Nursing, Ningxia Medical University, Yinchuan, China
| | - Xueying Huang
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Mingli Hou
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Wenxiao Liu
- School of Clinical Medicine, Ningxia Medical University, Yinchuan, China
| | - Minxing Wang
- School of Clinical Medicine, Ningxia Medical University, Yinchuan, China
| | - Xiaodong Wang
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China.
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10
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Qiu J, Deng K, Wang P, Chen C, Luo Y, Yuan S, Wen J. Application of diffusion kurtosis imaging to the study of edema in solid and peritumoral areas of glioma. Magn Reson Imaging 2021; 86:10-16. [PMID: 34793876 DOI: 10.1016/j.mri.2021.11.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 11/08/2021] [Accepted: 11/10/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVE When gliomas grow in an infiltrative form, high-grade malignant glioma tissue extends beyond the contrast-enhancing tumor boundary, and this diffuse non-enhancing tumor infiltration is not visible on conventional MRI. The purpose of this study was to evaluate the of diffusion kurtosis imaging (DKI)-derived parameters in a group of patients with pre-operative gliomas, evaluating changes in the solid tumor and peritumoral edema area, and investigating their use for evaluating the recurrence and prognosis of gliomas. METHODS In this retrospective study, 51 patients with gliomas who underwent biopsy or surgery underwent DKI scans before surgery. DKI scans were performed to generate DKI parameter maps of the solid tumor and peritumoral edema areas. In the solid tumor area, the kurtosis parameters showed the highest area under the curve (AUC), sensitivity, and specificity for distinguishing high- and low-grade gliomas (all P < 0.01). RESULTS In the peritumoral edema area, significant differences were found between groups with grade III and IV gliomas (P < 0.05). DKI parameters were found to correlate with clinical Ki-67 scores within the solid tumor area (MK: R2 = 0.288, P < 0.001; Kr: R2 = 0.270, P < 0.001; Ka: R2 = 0.274, P < 0.001; MD: R2 = 0.223, P < 0.001; FA: R2 = 0.098, P < 0.01). No significant correlations were found between Ki-67 and kurtosis parameters of peritumoral edema. CONCLUSIONS In this study, DKI showed potential utility for studying solid tumor and peritumoral edema of high grade gliomas.
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Affiliation(s)
- Jun Qiu
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Kexue Deng
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Peng Wang
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Chuanyu Chen
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Yi Luo
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Shuya Yuan
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Jie Wen
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
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11
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Maurer GD, Tichy J, Harter PN, Nöth U, Weise L, Quick-Weller J, Deichmann R, Steinbach JP, Bähr O, Hattingen E. Matching Quantitative MRI Parameters with Histological Features of Treatment-Naïve IDH Wild-Type Glioma. Cancers (Basel) 2021; 13:cancers13164060. [PMID: 34439213 PMCID: PMC8392045 DOI: 10.3390/cancers13164060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 08/05/2021] [Accepted: 08/10/2021] [Indexed: 11/16/2022] Open
Abstract
Quantitative MRI allows to probe tissue properties by measuring relaxation times and may thus detect subtle changes in tissue composition. In this work we analyzed different relaxation times (T1, T2, T2* and T2') and histological features in 321 samples that were acquired from 25 patients with newly diagnosed IDH wild-type glioma. Quantitative relaxation times before intravenous application of gadolinium-based contrast agent (GBCA), T1 relaxation time after GBCA as well as the relative difference between T1 relaxation times pre-to-post GBCA (T1rel) were compared with histopathologic features such as the presence of tumor cells, cell and vessel density, endogenous markers for hypoxia and cell proliferation. Image-guided stereotactic biopsy allowed for the attribution of each tissue specimen to its corresponding position in the respective relaxation time map. Compared to normal tissue, T1 and T2 relaxation times and T1rel were prolonged in samples containing tumor cells. The presence of vascular proliferates was associated with higher T1rel values. Immunopositivity for lactate dehydrogenase A (LDHA) involved slightly longer T1 relaxation times. However, low T2' values, suggesting high amounts of deoxyhemoglobin, were found in samples with elevated vessel densities, but not in samples with increased immunopositivity for LDHA. Taken together, some of our observations were consistent with previous findings but the correlation of quantitative MRI and histologic parameters did not confirm all our pathophysiology-based assumptions.
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Affiliation(s)
- Gabriele D. Maurer
- Senckenberg Institute of Neurooncology, Goethe University Hospital, 60528 Frankfurt am Main, Germany; (J.T.); (J.P.S.); (O.B.)
- Correspondence:
| | - Julia Tichy
- Senckenberg Institute of Neurooncology, Goethe University Hospital, 60528 Frankfurt am Main, Germany; (J.T.); (J.P.S.); (O.B.)
| | - Patrick N. Harter
- Institute of Neurology (Edinger Institute), Goethe University Hospital, 60528 Frankfurt am Main, Germany;
- German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, 60590 Frankfurt am Main, Germany
- German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Frankfurt Cancer Institute (FCI), 60596 Frankfurt am Main, Germany
| | - Ulrike Nöth
- Brain Imaging Center, Goethe University, 60528 Frankfurt am Main, Germany; (U.N.); (R.D.)
| | - Lutz Weise
- Division of Neurosurgery, Dalhousie University Halifax, Halifax, NS B3H 4R2, Canada;
| | - Johanna Quick-Weller
- Department of Neurosurgery, Goethe University Hospital, 60528 Frankfurt am Main, Germany;
| | - Ralf Deichmann
- Brain Imaging Center, Goethe University, 60528 Frankfurt am Main, Germany; (U.N.); (R.D.)
| | - Joachim P. Steinbach
- Senckenberg Institute of Neurooncology, Goethe University Hospital, 60528 Frankfurt am Main, Germany; (J.T.); (J.P.S.); (O.B.)
| | - Oliver Bähr
- Senckenberg Institute of Neurooncology, Goethe University Hospital, 60528 Frankfurt am Main, Germany; (J.T.); (J.P.S.); (O.B.)
- Department of Neurology, Klinikum Aschaffenburg-Alzenau, 63739 Aschaffenburg, Germany
| | - Elke Hattingen
- Institute of Neuroradiology, Goethe University Hospital, 60528 Frankfurt am Main, Germany;
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12
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Radunsky D, Stern N, Nassar J, Tsarfaty G, Blumenfeld-Katzir T, Ben-Eliezer N. Quantitative platform for accurate and reproducible assessment of transverse (T 2 ) relaxation time. NMR IN BIOMEDICINE 2021; 34:e4537. [PMID: 33993573 DOI: 10.1002/nbm.4537] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 04/02/2021] [Accepted: 04/19/2021] [Indexed: 06/12/2023]
Abstract
MRI's transverse relaxation time (T2 ) is sensitive to tissues' composition and pathological state. While variations in T2 values can be used as clinical biomarkers, it is challenging to quantify this parameter in vivo due to the complexity of the MRI signal model, differences in protocol implementations, and hardware imperfections. Herein, we provide a detailed analysis of the echo modulation curve (EMC) platform, offering accurate and reproducible mapping of T2 values, from 2D multi-slice multi-echo spin-echo (MESE) protocols. Computer simulations of the full Bloch equations are used to generate an advanced signal model, which accounts for stimulated echoes and transmit field (B1+ ) inhomogeneities. In addition to quantifying T2 values, the EMC platform also provides proton density (PD) maps, and fat-water fraction maps. The algorithm's accuracy, reproducibility, and insensitivity to T1 values are validated on a phantom constructed by the National Institute of Standards and Technology and on in vivo human brains. EMC-derived T2 maps show excellent agreement with ground truth values for both in vitro and in vivo models. Quantitative values are accurate and stable across scan settings and for the physiological range of T2 values, while showing robustness to main field (B0 ) inhomogeneities, to variations in T1 relaxation time, and to magnetization transfer. Extension of the algorithm to two-component fitting yields accurate fat and water T2 maps along with their relative fractions, similar to a reference three-point Dixon technique. Overall, the EMC platform allows to generate accurate and stable T2 maps, with a full brain coverage using a standard MESE protocol and at feasible scan times. The utility of EMC-based T2 maps was demonstrated on several clinical applications, showing robustness to variations in other magnetic properties. The algorithm is available online as a full stand-alone package, including an intuitive graphical user interface.
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Affiliation(s)
- Dvir Radunsky
- Department of Biomedical Engineering, Tel Aviv University, Israel
| | - Neta Stern
- Department of Biomedical Engineering, Tel Aviv University, Israel
| | - Jannette Nassar
- Department of Biomedical Engineering, Tel Aviv University, Israel
| | - Galia Tsarfaty
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Israel
| | | | - Noam Ben-Eliezer
- Department of Biomedical Engineering, Tel Aviv University, Israel
- Sagol School of Neuroscience, Tel Aviv University, Israel
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University Langone Medical Center, New York, New York, USA
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13
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Seiler A, Schöngrundner S, Stock B, Nöth U, Hattingen E, Steinmetz H, Klein JC, Baudrexel S, Wagner M, Deichmann R, Gracien RM. Cortical aging - new insights with multiparametric quantitative MRI. Aging (Albany NY) 2020; 12:16195-16210. [PMID: 32852283 PMCID: PMC7485732 DOI: 10.18632/aging.103629] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 06/22/2020] [Indexed: 02/07/2023]
Abstract
Understanding the microstructural changes related to physiological aging of the cerebral cortex is pivotal to differentiate healthy aging from neurodegenerative processes. The aim of this study was to investigate the age-related global changes of cortical microstructure and regional patterns using multiparametric quantitative MRI (qMRI) in healthy subjects with a wide age range. 40 healthy participants (age range: 2nd to 8th decade) underwent high-resolution qMRI including T1, PD as well as T2, T2* and T2′ mapping at 3 Tesla. Cortical reconstruction was performed with the FreeSurfer toolbox, followed by tests for correlations between qMRI parameters and age. Cortical T1 values were negatively correlated with age (p=0.007) and there was a widespread age-related decrease of cortical T1 involving the frontal and the parietotemporal cortex, while T2 was correlated positively with age, both in frontoparietal areas and globally (p=0.004). Cortical T2′ values showed the most widespread associations across the cortex and strongest correlation with age (r= -0.724, p=0.0001). PD and T2* did not correlate with age. Multiparametric qMRI allows to characterize cortical aging, unveiling parameter-specific patterns. Quantitative T2′ mapping seems to be a promising imaging biomarker of cortical age-related changes, suggesting that global cortical iron deposition is a prominent process in healthy aging.
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Affiliation(s)
- Alexander Seiler
- Department of Neurology, Goethe University, Frankfurt am Main, Germany.,Brain Imaging Center, Goethe University, Frankfurt am Main, Germany
| | - Sophie Schöngrundner
- Department of Neurology, Goethe University, Frankfurt am Main, Germany.,Department of Neuroradiology, Goethe University, Frankfurt am Main, Germany.,Brain Imaging Center, Goethe University, Frankfurt am Main, Germany
| | - Benjamin Stock
- Department of Neurology, Goethe University, Frankfurt am Main, Germany.,Department of Neuroradiology, Goethe University, Frankfurt am Main, Germany.,Brain Imaging Center, Goethe University, Frankfurt am Main, Germany
| | - Ulrike Nöth
- Brain Imaging Center, Goethe University, Frankfurt am Main, Germany
| | - Elke Hattingen
- Department of Neuroradiology, Goethe University, Frankfurt am Main, Germany
| | - Helmuth Steinmetz
- Department of Neurology, Goethe University, Frankfurt am Main, Germany
| | - Johannes C Klein
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Simon Baudrexel
- Department of Neurology, Goethe University, Frankfurt am Main, Germany.,Brain Imaging Center, Goethe University, Frankfurt am Main, Germany
| | - Marlies Wagner
- Department of Neuroradiology, Goethe University, Frankfurt am Main, Germany.,Brain Imaging Center, Goethe University, Frankfurt am Main, Germany
| | - Ralf Deichmann
- Brain Imaging Center, Goethe University, Frankfurt am Main, Germany
| | - René-Maxime Gracien
- Department of Neurology, Goethe University, Frankfurt am Main, Germany.,Brain Imaging Center, Goethe University, Frankfurt am Main, Germany
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