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Şimşek K, Gallea C, Genovese G, Lehéricy S, Branzoli F, Palombo M. Age-Trajectories of Higher-Order Diffusion Properties of Major Brain Metabolites in Cerebral and Cerebellar Gray Matter Using In Vivo Diffusion-Weighted MR Spectroscopy at 3T. Aging Cell 2025; 24:e14477. [PMID: 39817637 PMCID: PMC12073910 DOI: 10.1111/acel.14477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 11/21/2024] [Accepted: 12/20/2024] [Indexed: 01/18/2025] Open
Abstract
Healthy brain aging involves changes in both brain structure and function, including alterations in cellular composition and microstructure across brain regions. Unlike diffusion-weighted MRI (dMRI), diffusion-weighted MR spectroscopy (dMRS) can assess cell-type specific microstructural changes, providing indirect information on both cell composition and microstructure through the quantification and interpretation of metabolites' diffusion properties. This work investigates age-related changes in the higher-order diffusion properties of total N-Acetyl-aspartate (neuronal biomarker), total choline (glial biomarker), and total creatine (both neuronal and glial biomarker) beyond the classical apparent diffusion coefficient in cerebral and cerebellar gray matter of healthy human brain. Twenty-five subjects were recruited and scanned using a diffusion-weighted semi-LASER sequence in two brain regions-of-interest (ROI) at 3T: posterior-cingulate (PCC) and cerebellar cortices. Metabolites' diffusion was characterized by quantifying metrics from both Gaussian and non-Gaussian signal representations and biophysical models. All studied metabolites exhibited lower apparent diffusivities and higher apparent kurtosis values in the cerebellum compared to the PCC, likely stemming from the higher microstructural complexity of cellular composition in the cerebellum. Multivariate regression analysis (accounting for ROI tissue composition as a covariate) showed slight decrease (or no change) of all metabolites' diffusivities and slight increase of all metabolites' kurtosis with age, none of which statistically significant (p > 0.05). The proposed age-trajectories provide benchmarks for identifying anomalies in the diffusion properties of major brain metabolites which could be related to pathological mechanisms altering both the brain microstructure and cellular composition.
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Affiliation(s)
- Kadir Şimşek
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
- School of Computer Science and InformaticsCardiff UniversityCardiffUK
| | - Cécile Gallea
- Paris Brain Institute – ICMTeam “Movement Investigations and Therapeutics”ParisFrance
- Paris Brain Institute – ICM, INSERM U 1127, CNRS UMR 7225Sorbonne UniversityParisFrance
| | - Guglielmo Genovese
- Department of Neuromedicine and Movement ScienceNorwegian University of Science and TechnologyTrondheimNorway
| | - Stephane Lehéricy
- Paris Brain Institute – ICMTeam “Movement Investigations and Therapeutics”ParisFrance
- Paris Brain Institute – ICM, INSERM U 1127, CNRS UMR 7225Sorbonne UniversityParisFrance
| | - Francesca Branzoli
- Paris Brain Institute – ICM, INSERM U 1127, CNRS UMR 7225Sorbonne UniversityParisFrance
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
- School of Computer Science and InformaticsCardiff UniversityCardiffUK
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Jokivuolle M, Mahmood F, Madsen KH, Harbo FSG, Johnsen L, Lundell H. Assessing tumor microstructure with time-dependent diffusion imaging: Considerations and feasibility on clinical MRI and MRI-Linac. Med Phys 2025; 52:346-361. [PMID: 39387639 PMCID: PMC11700005 DOI: 10.1002/mp.17453] [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: 03/08/2024] [Revised: 09/19/2024] [Accepted: 09/23/2024] [Indexed: 10/15/2024] Open
Abstract
BACKGROUND Quantitative imaging biomarkers (QIBs) can characterize tumor heterogeneity and provide information for biological guidance in radiotherapy (RT). Time-dependent diffusion MRI (TDD-MRI) derived parameters are promising QIBs, as they describe tissue microstructure with more specificity than traditional diffusion-weighted MRI (DW-MRI). Specifically, TDD-MRI can provide information about both restricted diffusion and diffusional exchange, which are the two time-dependent effects affecting diffusion in tissue, and relevant in tumors. However, exhaustive modeling of both effects can require long acquisitions and complex model fitting. Furthermore, several introduced TDD-MRI measurements can require high gradient strengths and/or complex gradient waveforms that are possibly not available in RT settings. PURPOSE In this study, we investigated the feasibility of a simple analysis framework for the detection of restricted diffusion and diffusional exchange effects in the TDD-MRI signal. To promote the clinical applicability, we use standard gradient waveforms on a conventional 1.5 T MRI system with moderate gradient strength (Gmax = 45 mT/m), and on a hybrid 1.5 T MRI-Linac system with low gradient strength (Gmax = 15 mT/m). METHODS Restricted diffusion and diffusional exchange were simulated in geometries mimicking tumor microstructure to investigate the DW-MRI signal behavior and to determine optimal experimental parameters. TDD-MRI was implemented using pulsed field gradient spin echo with the optimized parameters on a conventional MRI system and a MRI-Linac. Experiments in green asparagus and 10 patients with brain lesions were performed to evaluate the time-dependent diffusion (TDD) contrast in the source DW-images. RESULTS Simulations demonstrated how the TDD contrast was able to differentiate only dominating diffusional exchange in smaller cells from dominating restricted diffusion in larger cells. The maximal TDD contrast in simulations with typical cancer cell sizes and in asparagus measurements exceeded 5% on the conventional MRI but remained below 5% on the MRI-Linac. In particular, the simulated TDD contrast in typical cancer cell sizes (r = 5-10 µm) remained below or around 2% with the MRI-Linac gradient strength. In patients measured with the conventional MRI, we found sub-regions reflecting either dominating restricted diffusion or dominating diffusional exchange in and around brain lesions compared to the noisy appearing white matter. CONCLUSIONS On the conventional MRI system, the TDD contrast maps showed consistent tumor sub-regions indicating different dominating TDD effects, potentially providing information on the spatial tumor heterogeneity. On the MRI-Linac, the available TDD contrast measured in asparagus showed the same trends as with the conventional MRI but remained close to typical measurement noise levels when simulated in common cancer cell sizes. On conventional MRI systems with moderate gradient strengths, the TDD contrast could potentially be used as a tool to identify which time-dependent effects to include when choosing a biophysical model for more specific tumor characterization.
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Affiliation(s)
- Minea Jokivuolle
- Laboratory of Radiation PhysicsDepartment of OncologyOdense University HospitalOdenseDenmark
- Department of Clinical ResearchUniversity of Southern DenmarkOdenseDenmark
| | - Faisal Mahmood
- Laboratory of Radiation PhysicsDepartment of OncologyOdense University HospitalOdenseDenmark
- Department of Clinical ResearchUniversity of Southern DenmarkOdenseDenmark
| | - Kristoffer Hougaard Madsen
- Danish Research Centre for Magnetic ResonanceCentre for Functional and Diagnostic Imaging and ResearchCopenhagen University Hospital ‐ Amager and HvidovreHvidovreDenmark
- Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKongens LyngbyDenmark
| | | | - Lars Johnsen
- Laboratory of Radiation PhysicsDepartment of OncologyOdense University HospitalOdenseDenmark
| | - Henrik Lundell
- Danish Research Centre for Magnetic ResonanceCentre for Functional and Diagnostic Imaging and ResearchCopenhagen University Hospital ‐ Amager and HvidovreHvidovreDenmark
- Department of Health TechnologyTechnical University of DenmarkKongens LyngbyDenmark
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3
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Ligneul C, Najac C, Döring A, Beaulieu C, Branzoli F, Clarke WT, Cudalbu C, Genovese G, Jbabdi S, Jelescu I, Karampinos D, Kreis R, Lundell H, Marjańska M, Möller HE, Mosso J, Mougel E, Posse S, Ruschke S, Simsek K, Szczepankiewicz F, Tal A, Tax C, Oeltzschner G, Palombo M, Ronen I, Valette J. Diffusion-weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling. Magn Reson Med 2024; 91:860-885. [PMID: 37946584 DOI: 10.1002/mrm.29877] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/18/2023] [Accepted: 09/08/2023] [Indexed: 11/12/2023]
Abstract
Brain cell structure and function reflect neurodevelopment, plasticity, and aging; and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to noninvasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MRS (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modeling, and interpretation of results. These challenges were the motivation behind the organization of the Lorentz Center workshop on "Best Practices & Tools for Diffusion MR Spectroscopy" held in Leiden, the Netherlands, in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting, and modeling dMRS data, and provides links to useful resources.
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Affiliation(s)
- Clémence Ligneul
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Chloé Najac
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - André Döring
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Christian Beaulieu
- Departments of Biomedical Engineering and Radiology, University of Alberta, Alberta, Edmonton, Canada
| | - Francesca Branzoli
- Paris Brain Institute-ICM, Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - William T Clarke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Cristina Cudalbu
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Guglielmo Genovese
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minnesota, Minneapolis, USA
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Ileana Jelescu
- Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Dimitrios Karampinos
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany
| | - Roland Kreis
- MR Methodology, Department for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager anf Hvidovre, Hvidovre, Denmark
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minnesota, Minneapolis, USA
| | - Harald E Möller
- NMR Methods & Development Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jessie Mosso
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- LIFMET, EPFL, Lausanne, Switzerland
| | - Eloïse Mougel
- Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoires des Maladies Neurodégénératives, Fontenay-aux-Roses, France
| | - Stefan Posse
- Department of Neurology, University of New Mexico School of Medicine, New Mexico, Albuquerque, USA
- Department of Physics and Astronomy, University of New Mexico School of Medicine, New Mexico, Albuquerque, USA
| | - Stefan Ruschke
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany
| | - Kadir Simsek
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | | | - Assaf Tal
- Department of Chemical and Biological Physics, The Weizmann Institute of Science, Rehovot, Israel
| | - Chantal Tax
- University Medical Center Utrecht, Utrecht, The Netherlands
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Maryland, Baltimore, USA
- F. M. Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Maryland, Baltimore, USA
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | - Itamar Ronen
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton, UK
| | - Julien Valette
- Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoires des Maladies Neurodégénératives, Fontenay-aux-Roses, France
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Spotorno N, Najac C, Strandberg O, Stomrud E, van Westen D, Nilsson M, Ronen I, Hansson O. Diffusion weighted magnetic resonance spectroscopy revealed neuronal specific microstructural alterations in Alzheimer's disease. Brain Commun 2024; 6:fcae026. [PMID: 38370447 PMCID: PMC10873577 DOI: 10.1093/braincomms/fcae026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 12/19/2023] [Accepted: 01/31/2024] [Indexed: 02/20/2024] Open
Abstract
In Alzheimer's disease, reconfiguration and deterioration of tissue microstructure occur before substantial degeneration become evident. We explored the diffusion properties of both water, a ubiquitous marker measured by diffusion MRI, and N-acetyl-aspartate, a neuronal metabolite probed by diffusion-weighted magnetic resonance spectroscopy, for investigating cortical microstructural changes downstream of Alzheimer's disease pathology. To this aim, 50 participants from the Swedish BioFINDER-2 study were scanned on both 7 and 3 T MRI systems. We found that in cognitively impaired participants with evidence of both abnormal amyloid-beta (CSF amyloid-beta42/40) and tau accumulation (tau-PET), the N-acetyl-aspartate diffusion rate was significantly lower than in cognitively unimpaired participants (P < 0.05). This supports the hypothesis that intraneuronal tau accumulation hinders diffusion in the neuronal cytosol. Conversely, water diffusivity was higher in cognitively impaired participants (P < 0.001) and was positively associated with the concentration of myo-inositol, a preferentially astrocytic metabolite (P < 0.001), suggesting that water diffusion is sensitive to alterations in the extracellular space and in glia. In conclusion, measuring the diffusion properties of both water and N-acetyl-aspartate provides rich information on the cortical microstructure in Alzheimer's disease, and can be used to develop new sensitive and specific markers to microstructural changes occurring during the disease course.
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Affiliation(s)
- Nicola Spotorno
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Lund 22184, Sweden
| | - Chloé Najac
- Department of Radiology, C.J. Gorter MRI Center, Leiden University Medical Center, Leiden 2333, The Netherlands
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Lund 22184, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Lund 22184, Sweden
- Memory Clinic, Skåne University Hospital, Malmö 20502, Sweden
| | - Danielle van Westen
- Image and Function, Skane University Hospital, Lund 22185, Sweden
- Diagnostic Radiology, Institution for Clinical Sciences, Lund University, Lund 22185, Sweden
| | - Markus Nilsson
- Diagnostic Radiology, Institution for Clinical Sciences, Lund University, Lund 22185, Sweden
| | - Itamar Ronen
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Falmer BN1 9RR, UK
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Lund 22184, Sweden
- Memory Clinic, Skåne University Hospital, Malmö 20502, Sweden
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5
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Lampinen B, Szczepankiewicz F, Lätt J, Knutsson L, Mårtensson J, Björkman-Burtscher IM, van Westen D, Sundgren PC, Ståhlberg F, Nilsson M. Probing brain tissue microstructure with MRI: principles, challenges, and the role of multidimensional diffusion-relaxation encoding. Neuroimage 2023; 282:120338. [PMID: 37598814 DOI: 10.1016/j.neuroimage.2023.120338] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 06/30/2023] [Accepted: 08/17/2023] [Indexed: 08/22/2023] Open
Abstract
Diffusion MRI uses the random displacement of water molecules to sensitize the signal to brain microstructure and to properties such as the density and shape of cells. Microstructure modeling techniques aim to estimate these properties from acquired data by separating the signal between virtual tissue 'compartments' such as the intra-neurite and the extra-cellular space. A key challenge is that the diffusion MRI signal is relatively featureless compared with the complexity of brain tissue. Another challenge is that the tissue microstructure is wildly different within the gray and white matter of the brain. In this review, we use results from multidimensional diffusion encoding techniques to discuss these challenges and their tentative solutions. Multidimensional encoding increases the information content of the data by varying not only the b-value and the encoding direction but also additional experimental parameters such as the shape of the b-tensor and the echo time. Three main insights have emerged from such encoding. First, multidimensional data contradict common model assumptions on diffusion and T2 relaxation, and illustrates how the use of these assumptions cause erroneous interpretations in both healthy brain and pathology. Second, many model assumptions can be dispensed with if data are acquired with multidimensional encoding. The necessary data can be easily acquired in vivo using protocols optimized to minimize Cramér-Rao lower bounds. Third, microscopic diffusion anisotropy reflects the presence of axons but not dendrites. This insight stands in contrast to current 'neurite models' of brain tissue, which assume that axons in white matter and dendrites in gray matter feature highly similar diffusion. Nevertheless, as an axon-based contrast, microscopic anisotropy can differentiate gray and white matter when myelin alterations confound conventional MRI contrasts.
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Affiliation(s)
- Björn Lampinen
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden.
| | | | - Jimmy Lätt
- Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden
| | - Linda Knutsson
- Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Johan Mårtensson
- Clinical Sciences Lund, Logopedics, Phoniatrics and Audiology, Lund University, Lund, Sweden
| | - Isabella M Björkman-Burtscher
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Danielle van Westen
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden
| | - Pia C Sundgren
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden; Lund University BioImaging Centre (LBIC), Lund University, Lund, Sweden
| | - Freddy Ståhlberg
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Markus Nilsson
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden
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6
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Bruckmaier F, Allert RD, Neuling NR, Amrein P, Littin S, Briegel KD, Schätzle P, Knittel P, Zaitsev M, Bucher DB. Imaging local diffusion in microstructures using NV-based pulsed field gradient NMR. SCIENCE ADVANCES 2023; 9:eadh3484. [PMID: 37595048 PMCID: PMC10438442 DOI: 10.1126/sciadv.adh3484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 07/20/2023] [Indexed: 08/20/2023]
Abstract
Understanding diffusion in microstructures plays a crucial role in many scientific fields, including neuroscience, medicine, or energy research. While magnetic resonance (MR) methods are the gold standard for diffusion measurements, spatial encoding in MR imaging has limitations. Here, we introduce nitrogen-vacancy (NV) center-based nuclear MR (NMR) spectroscopy as a powerful tool to probe diffusion within microscopic sample volumes. We have developed an experimental scheme that combines pulsed gradient spin echo (PGSE) with optically detected NV-NMR spectroscopy, allowing local quantification of molecular diffusion and flow. We demonstrate correlated optical imaging with spatially resolved PGSE NV-NMR experiments probing anisotropic water diffusion within an individual model microstructure. Our optically detected PGSE NV-NMR technique opens up prospects for extending the current capabilities of investigating diffusion processes with the future potential of probing single cells, tissue microstructures, or ion mobility in thin film materials for battery applications.
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Affiliation(s)
- Fleming Bruckmaier
- Department of Chemistry, TUM School of Natural Sciences, Technical University of Munich, 85748 Garching, Germany
| | - Robin D. Allert
- Department of Chemistry, TUM School of Natural Sciences, Technical University of Munich, 85748 Garching, Germany
| | - Nick R. Neuling
- Department of Chemistry, TUM School of Natural Sciences, Technical University of Munich, 85748 Garching, Germany
| | - Philipp Amrein
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Sebastian Littin
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Karl D. Briegel
- Department of Chemistry, TUM School of Natural Sciences, Technical University of Munich, 85748 Garching, Germany
| | - Philip Schätzle
- Department of Sustainable Systems Engineering (INATECH), University of Freiburg, Emmy-Noether-Str. 2, 79110 Freiburg, Germany
| | - Peter Knittel
- Fraunhofer Institute for Applied Solid State Physics, Tullastr. 72, 79108 Freiburg, Germany
| | - Maxim Zaitsev
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Dominik B. Bucher
- Department of Chemistry, TUM School of Natural Sciences, Technical University of Munich, 85748 Garching, Germany
- Munich Center for Quantum Science and Technology (MCQST), Schellingstr. 4, 80799 München, Germany
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7
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Howard AF, Cottaar M, Drakesmith M, Fan Q, Huang SY, Jones DK, Lange FJ, Mollink J, Rudrapatna SU, Tian Q, Miller KL, Jbabdi S. Estimating axial diffusivity in the NODDI model. Neuroimage 2022; 262:119535. [PMID: 35931306 PMCID: PMC9802007 DOI: 10.1016/j.neuroimage.2022.119535] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/20/2022] [Accepted: 08/01/2022] [Indexed: 01/03/2023] Open
Abstract
To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model (d∥=1.7μm2/ms). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of ∼2-2.5μm2/ms, in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data.
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Affiliation(s)
- Amy Fd Howard
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
| | - Michiel Cottaar
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Mark Drakesmith
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States; Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China; Academy of Medical Engineering and Translational Medicine, Medical College, Tianjin University, Tianjin, China
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Frederik J Lange
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jeroen Mollink
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Suryanarayana Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Philips Innovation Campus, Bangalore, India
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States
| | - Karla L Miller
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Saad Jbabdi
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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8
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Olesen JL, Østergaard L, Shemesh N, Jespersen SN. Diffusion time dependence, power-law scaling, and exchange in gray matter. Neuroimage 2022; 251:118976. [PMID: 35168088 PMCID: PMC8961002 DOI: 10.1016/j.neuroimage.2022.118976] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 12/24/2021] [Accepted: 02/04/2022] [Indexed: 12/27/2022] Open
Abstract
Characterizing neural tissue microstructure is a critical goal for future neuroimaging. Diffusion MRI (dMRI) provides contrasts that reflect diffusing spins' interactions with myriad microstructural features of biological systems. However, the specificity of dMRI remains limited due to the ambiguity of its signals vis-à-vis the underlying microstructure. To improve specificity, biophysical models of white matter (WM) typically express dMRI signals according to the Standard Model (SM) and have more recently in gray matter (GM) taken spherical compartments into account (the SANDI model) in attempts to represent cell soma. The validity of the assumptions underlying these models, however, remains largely undetermined, especially in GM. To validate these assumptions experimentally, observing their unique, functional properties, such as the b-1/2 power-law associated with one-dimensional diffusion, has emerged as a fruitful strategy. The absence of this signature in GM, in turn, has been explained by neurite water exchange, non-linear morphology, and/or by obscuring soma signal contributions. Here, we present diffusion simulations in realistic neurons demonstrating that curvature and branching does not destroy the stick power-law behavior in impermeable neurites, but also that their signal is drowned by the soma signal under typical experimental conditions. Nevertheless, by studying the GM dMRI signal's behavior as a function of diffusion weighting as well as time, we identify an attainable experimental regime in which the neurite signal dominates. Furthermore, we find that exchange-driven time dependence produces a signal behavior opposite to that which would be expected from restricted diffusion, thereby providing a functional signature that disambiguates the two effects. We present data from dMRI experiments in ex vivo rat brain at ultrahigh field of 16.4T and observe a time dependence that is consistent with substantial exchange but also with a GM stick power-law. The first finding suggests significant water exchange between neurites and the extracellular space while the second suggests a small sub-population of impermeable neurites. To quantify these observations, we harness the Kärger exchange model and incorporate the corresponding signal time dependence in the SM and SANDI models.
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Affiliation(s)
- Jonas L Olesen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Leif Østergaard
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Sune N Jespersen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark.
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Novello L, Henriques RN, Ianuş A, Feiweier T, Shemesh N, Jovicich J. In vivo Correlation Tensor MRI reveals microscopic kurtosis in the human brain on a clinical 3T scanner. Neuroimage 2022; 254:119137. [PMID: 35339682 DOI: 10.1016/j.neuroimage.2022.119137] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 02/17/2022] [Accepted: 03/22/2022] [Indexed: 12/15/2022] Open
Abstract
Diffusion MRI (dMRI) has become one of the most important imaging modalities for noninvasively probing tissue microstructure. Diffusional Kurtosis MRI (DKI) quantifies the degree of non-gaussian diffusion, which in turn has been shown to increase sensitivity towards, e.g., disease and orientation mapping in neural tissue. However, the specificity of DKI is limited as different sources can contribute to the total intravoxel diffusional kurtosis, including: variance in diffusion tensor magnitudes (Kiso), variance due to diffusion anisotropy (Kaniso), and microscopic kurtosis (μK) related to restricted diffusion, microstructural disorder, and/or exchange. Interestingly, μK is typically ignored in diffusion MRI signal modeling as it is assumed to be negligible in neural tissues. However, recently, Correlation Tensor MRI (CTI) based on Double-Diffusion-Encoding (DDE) was introduced for kurtosis source separation, revealing non negligible μK in preclinical imaging. Here, we implemented CTI for the first time on a clinical 3T scanner and investigated the sources of total kurtosis in healthy subjects. A robust framework for kurtosis source separation in humans is introduced, followed by estimation of μK (and the other kurtosis sources) in the healthy brain. Using this clinical CTI approach, we find that μK significantly contributes to total diffusional kurtosis both in gray and white matter tissue but, as expected, not in the ventricles. The first μK maps of the human brain are presented, revealing that the spatial distribution of μK provides a unique source of contrast, appearing different from isotropic and anisotropic kurtosis counterparts. Moreover, group average templates of these kurtosis sources have been generated for the first time, which corroborated our findings at the underlying individual-level maps. We further show that the common practice of ignoring μK and assuming the multiple gaussian component approximation for kurtosis source estimation introduces significant bias in the estimation of other kurtosis sources and, perhaps even worse, compromises their interpretation. Finally, a twofold acceleration of CTI is discussed in the context of potential future clinical applications. We conclude that CTI has much potential for future in vivo microstructural characterizations in healthy and pathological tissue.
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Affiliation(s)
- Lisa Novello
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, Italy.
| | | | - Andrada Ianuş
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | | | - Noam Shemesh
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Jorge Jovicich
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, Italy
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Ianus A, Alexander DC, Zhang H, Palombo M. Mapping complex cell morphology in the grey matter with double diffusion encoding MR: A simulation study. Neuroimage 2021; 241:118424. [PMID: 34311067 PMCID: PMC8961003 DOI: 10.1016/j.neuroimage.2021.118424] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 07/13/2021] [Accepted: 07/21/2021] [Indexed: 01/18/2023] Open
Abstract
This paper investigates the impact of cell body (namely soma) size and branching of cellular projections on diffusion MR imaging (dMRI) and spectroscopy (dMRS) signals for both standard single diffusion encoding (SDE) and more advanced double diffusion encoding (DDE) measurements using numerical simulations. The aim is to investigate the ability of dMRI/dMRS to characterize the complex morphology of brain cells focusing on these two distinctive features of brain grey matter. To this end, we employ a recently developed computational framework to create three dimensional meshes of neuron-like structures for Monte Carlo simulations, using diffusion coefficients typical of water and brain metabolites. Modelling the cellular structure as realistically connected spherical soma and cylindrical cellular projections, we cover a wide range of combinations of sphere radii and branching order of cellular projections, characteristic of various grey matter cells. We assess the impact of spherical soma size and branching order on the b-value dependence of the SDE signal as well as the time dependence of the mean diffusivity (MD) and mean kurtosis (MK). Moreover, we also assess the impact of spherical soma size and branching order on the angular modulation of DDE signal at different mixing times, together with the mixing time dependence of the apparent microscopic anisotropy (μA), a promising contrast derived from DDE measurements. The SDE results show that spherical soma size has a measurable impact on both the b-value dependence of the SDE signal and the MD and MK diffusion time dependence for both water and metabolites. On the other hand, we show that branching order has little impact on either, especially for water. In contrast, the DDE results show that spherical soma size has a measurable impact on the DDE signal's angular modulation at short mixing times and the branching order of cellular projections significantly impacts the mixing time dependence of the DDE signal's angular modulation as well as of the derived μA, for both water and metabolites. Our results confirm that SDE based techniques may be sensitive to spherical soma size, and most importantly, show for the first time that DDE measurements may be more sensitive to the dendritic tree complexity (as parametrized by the branching order of cellular projections), paving the way for new ways of characterizing grey matter morphology, non-invasively using dMRS and potentially dMRI.
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Affiliation(s)
- A Ianus
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom; Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - D C Alexander
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom
| | - H Zhang
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom
| | - M Palombo
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom.
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