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Barakovic M, Weigel M, Cagol A, Schaedelin S, Galbusera R, Lu PJ, Chen X, Melie-Garcia L, Ocampo-Pineda M, Bahn E, Stadelmann C, Palombo M, Kappos L, Kuhle J, Magon S, Granziera C. A novel imaging marker of cortical "cellularity" in multiple sclerosis patients. Sci Rep 2024; 14:9848. [PMID: 38684744 PMCID: PMC11059177 DOI: 10.1038/s41598-024-60497-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 04/23/2024] [Indexed: 05/02/2024] Open
Abstract
Pathological data showed focal inflammation and regions of diffuse neuronal loss in the cortex of people with multiple sclerosis (MS). In this work, we applied a novel model ("soma and neurite density imaging (SANDI)") to multishell diffusion-weighted MRI data acquired in healthy subjects and people with multiple sclerosis (pwMS), in order to investigate inflammation and degeneration-related changes in the cortical tissue of pwMS. We aimed to (i) establish whether SANDI is applicable in vivo clinical data; (ii) investigate inflammatory and degenerative changes using SANDI soma fraction (fsoma)-a marker of cellularity-in both cortical lesions and in the normal-appearing-cortex and (iii) correlate SANDI fsoma with clinical and biological measures in pwMS. We applied a simplified version of SANDI to a clinical scanners. We then provided evidence that pwMS exhibited an overall decrease in cortical SANDI fsoma compared to healthy subjects, suggesting global degenerative processes compatible with neuronal loss. On the other hand, we have found that progressive pwMS showed a higher SANDI fsoma in the outer part of the cortex compared to relapsing-remitting pwMS, possibly supporting current pathological knowledge of increased innate inflammatory cells in these regions. A similar finding was obtained in subpial lesions in relapsing-remitting patients, reflecting existing pathological data in these lesion types. A significant correlation was found between SANDI fsoma and serum neurofilament light chain-a biomarker of inflammatory axonal damage-suggesting a relationship between SANDI soma fraction and inflammatory processes in pwMS again. Overall, our data show that SANDI fsoma is a promising biomarker to monitor changes in cellularity compatible with neurodegeneration and neuroinflammation in the cortex of MS patients.
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Affiliation(s)
- Muhamed Barakovic
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Alessandro Cagol
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Sabine Schaedelin
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Riccardo Galbusera
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Po-Jui Lu
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Xinjie Chen
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Lester Melie-Garcia
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Mario Ocampo-Pineda
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Erik Bahn
- Institute of Neuropathology, University Medical Center, Göttingen, Germany
| | | | - Marco Palombo
- School of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | - Ludwig Kappos
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Kuhle
- Department of Neurology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Stefano Magon
- Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland.
- Department of Neurology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland.
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland.
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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: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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3
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Schiavi S, Palombo M, Zacà D, Tazza F, Lapucci C, Castellan L, Costagli M, Inglese M. Mapping tissue microstructure across the human brain on a clinical scanner with soma and neurite density image metrics. Hum Brain Mapp 2023; 44:4792-4811. [PMID: 37461286 PMCID: PMC10400787 DOI: 10.1002/hbm.26416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 05/02/2023] [Accepted: 06/23/2023] [Indexed: 08/05/2023] Open
Abstract
Soma and neurite density image (SANDI) is an advanced diffusion magnetic resonance imaging biophysical signal model devised to probe in vivo microstructural information in the gray matter (GM). This model requires acquisitions that include b values that are at least six times higher than those used in clinical practice. Such high b values are required to disentangle the signal contribution of water diffusing in soma from that diffusing in neurites and extracellular space, while keeping the diffusion time as short as possible to minimize potential bias due to water exchange. These requirements have limited the use of SANDI only to preclinical or cutting-edge human scanners. Here, we investigate the potential impact of neglecting water exchange in the SANDI model and present a 10-min acquisition protocol that enables to characterize both GM and white matter (WM) on 3 T scanners. We implemented analytical simulations to (i) evaluate the stability of the fitting of SANDI parameters when diminishing the number of shells; (ii) estimate the bias due to potential exchange between neurites and extracellular space in such reduced acquisition scheme, comparing it with the bias due to experimental noise. Then, we demonstrated the feasibility and assessed the repeatability and reproducibility of our approach by computing microstructural metrics of SANDI with AMICO toolbox and other state-of-the-art models on five healthy subjects. Finally, we applied our protocol to five multiple sclerosis patients. Results suggest that SANDI is a practical method to characterize WM and GM tissues in vivo on performant clinical scanners.
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Affiliation(s)
- Simona Schiavi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
| | - Marco Palombo
- CUBRIC, School of PsychologyCardiff UniversityCardiffUK
- School of Computer Science and InformaticsCardiff UniversityCardiffUK
| | | | - Francesco Tazza
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
| | - Caterina Lapucci
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
- HNSR, IRRCS Ospedale Policlinico San MartinoGenoaItaly
| | - Lucio Castellan
- Department of NeuroradiologyIRCCS Ospedale Policlinico San MartinoGenoaItaly
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
- Laboratory of Medical Physics and Magnetic ResonanceIRCCS Stella MarisPisaItaly
| | - Matilde Inglese
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
- IRCCS Ospedale Policlinico San MartinoGenoaItaly
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4
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Reddaway J, Richardson PE, Bevan RJ, Stoneman J, Palombo M. Microglial morphometric analysis: so many options, so little consistency. Front Neuroinform 2023; 17:1211188. [PMID: 37637472 PMCID: PMC10448193 DOI: 10.3389/fninf.2023.1211188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 07/05/2023] [Indexed: 08/29/2023] Open
Abstract
Quantification of microglial activation through morphometric analysis has long been a staple of the neuroimmunologist's toolkit. Microglial morphological phenomics can be conducted through either manual classification or constructing a digital skeleton and extracting morphometric data from it. Multiple open-access and paid software packages are available to generate these skeletons via semi-automated and/or fully automated methods with varying degrees of accuracy. Despite advancements in methods to generate morphometrics (quantitative measures of cellular morphology), there has been limited development of tools to analyze the datasets they generate, in particular those containing parameters from tens of thousands of cells analyzed by fully automated pipelines. In this review, we compare and critique the approaches using cluster analysis and machine learning driven predictive algorithms that have been developed to tackle these large datasets, and propose improvements for these methods. In particular, we highlight the need for a commitment to open science from groups developing these classifiers. Furthermore, we call attention to a need for communication between those with a strong software engineering/computer science background and neuroimmunologists to produce effective analytical tools with simplified operability if we are to see their wide-spread adoption by the glia biology community.
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Affiliation(s)
- Jack Reddaway
- Division of Neuroscience, School of Biosciences, Cardiff University, Cardiff, United Kingdom
- Hodge Centre for Neuropsychiatric Immunology, Neuroscience and Mental Health Innovation Institute (NMHII), Cardiff University, Cardiff, United Kingdom
| | | | - Ryan J. Bevan
- UK Dementia Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Jessica Stoneman
- Division of Neuroscience, School of Biosciences, Cardiff University, Cardiff, United Kingdom
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
- School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
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Endt S, Engel M, Naldi E, Assereto R, Molendowska M, Mueller L, Mayrink Verdun C, Pirkl CM, Palombo M, Jones DK, Menzel MI. In Vivo Myelin Water Quantification Using Diffusion-Relaxation Correlation MRI: A Comparison of 1D and 2D Methods. Appl Magn Reson 2023; 54:1571-1588. [PMID: 38037641 PMCID: PMC10682074 DOI: 10.1007/s00723-023-01584-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/18/2023] [Accepted: 07/25/2023] [Indexed: 12/02/2023]
Abstract
Multidimensional Magnetic Resonance Imaging (MRI) is a versatile tool for microstructure mapping. We use a diffusion weighted inversion recovery spin echo (DW-IR-SE) sequence with spiral readouts at ultra-strong gradients to acquire a rich diffusion-relaxation data set with sensitivity to myelin water. We reconstruct 1D and 2D spectra with a two-step convex optimization approach and investigate a variety of multidimensional MRI methods, including 1D multi-component relaxometry, 1D multi-component diffusometry, 2D relaxation correlation imaging, and 2D diffusion-relaxation correlation spectroscopic imaging (DR-CSI), in terms of their potential to quantify tissue microstructure, including the myelin water fraction (MWF). We observe a distinct spectral peak that we attribute to myelin water in multi-component T1 relaxometry, T1-T2 correlation, T1-D correlation, and T2-D correlation imaging. Due to lower achievable echo times compared to diffusometry, MWF maps from relaxometry have higher quality. Whilst 1D multi-component T1 data allows much faster myelin mapping, 2D approaches could offer unique insights into tissue microstructure and especially myelin diffusion.
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Affiliation(s)
- Sebastian Endt
- Technical University of Munich, Munich, Germany
- AImotion Bavaria, Technische Hochschule Ingolstadt, Ingolstadt, Germany
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Maria Engel
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Emanuele Naldi
- Technische Universität Braunschweig, Braunschweig, Germany
| | | | - Malwina Molendowska
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
- Medical Radiation Physics, Lund University, Lund, Sweden
| | - Lars Mueller
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
- Biomedical Imaging Science Department, Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM), University of Leeds, Leeds, United Kingdom
| | - Claudio Mayrink Verdun
- Technical University of Munich, Munich, Germany
- Munich Center for Machine Learning, Munich, Germany
| | | | - Marco Palombo
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Marion I. Menzel
- Technical University of Munich, Munich, Germany
- AImotion Bavaria, Technische Hochschule Ingolstadt, Ingolstadt, Germany
- GE HealthCare, Munich, Germany
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6
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Örzsik B, Palombo M, Asllani I, Dijk DJ, Harrison NA, Cercignani M. Higher order diffusion imaging as a putative index of human sleep-related microstructural changes and glymphatic clearance. Neuroimage 2023; 274:120124. [PMID: 37084927 DOI: 10.1016/j.neuroimage.2023.120124] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 03/16/2023] [Accepted: 04/18/2023] [Indexed: 04/23/2023] Open
Abstract
The brain has a unique macroscopic waste clearance system, termed the glymphatic system which utilises perivascular tunnels surrounded by astroglia to promote cerebrospinal-interstitial fluid exchange. Rodent studies have demonstrated a marked increase in glymphatic clearance during sleep which has been linked to a sleep-induced expansion of the extracellular space and concomitant reduction in intracellular volume. However, despite being implicated in the pathophysiology of multiple human neurodegenerative disorders, non-invasive techniques for imaging glymphatic clearance in humans are currently limited. Here we acquired multi-shell diffusion weighted MRI (dwMRI) in twenty-one healthy young participants (6 female, 22.3 ± 3.2 years) each scanned twice, once during wakefulness and once during sleep induced by a combination of one night of sleep deprivation and 10 mg of the hypnotic zolpidem 30 min before scanning. To capture hypothesised sleep-associated changes in intra/extracellular space, dwMRI were analysed using higher order diffusion modelling with the prediction that sleep-associated increases in interstitial (extracellular) fluid volume would result in a decrease in diffusion kurtosis, particularly in areas associated with slow wave generation at the onset of sleep. In line with our hypothesis, we observed a global reduction in diffusion kurtosis (t15=2.82, p = 0.006) during sleep as well as regional reductions in brain areas associated with slow wave generation during early sleep and default mode network areas that are highly metabolically active during wakefulness. Analysis with a higher-order representation of diffusion (MAP-MRI) further indicated that changes within the intra/extracellular domain rather than membrane permeability likely underpin the observed sleep-associated decrease in kurtosis. These findings identify higher-order modelling of dwMRI as a potential new non-invasive method for imaging glymphatic clearance and extend rodent findings to suggest that sleep is also associated with an increase in interstitial fluid volume in humans.
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Affiliation(s)
- Balázs Örzsik
- Radiology, Leiden University Medical Center, Leiden, the Netherlands; CISC, Brighton and Sussex Medical School, Brighton, United Kingdom.
| | - Marco Palombo
- CUBRIC, Cardiff University, United Kingdom; School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | - Iris Asllani
- CISC, Brighton and Sussex Medical School, Brighton, United Kingdom; Rochester Institute of Technology, New York, United States
| | - Derk-Jan Dijk
- Surrey Sleep Research Centre, Faculty of Health and Medical Sciences, University of Surrey, Guildford UK; UK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey, Guildford UK
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7
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Figini M, Castellano A, Bailo M, Callea M, Cadioli M, Bouyagoub S, Palombo M, Pieri V, Mortini P, Falini A, Alexander DC, Cercignani M, Panagiotaki E. Comprehensive Brain Tumour Characterisation with VERDICT-MRI: Evaluation of Cellular and Vascular Measures Validated by Histology. Cancers (Basel) 2023; 15:2490. [PMID: 37173965 PMCID: PMC10177485 DOI: 10.3390/cancers15092490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/14/2023] [Accepted: 04/17/2023] [Indexed: 05/15/2023] Open
Abstract
The aim of this work was to extend the VERDICT-MRI framework for modelling brain tumours, enabling comprehensive characterisation of both intra- and peritumoural areas with a particular focus on cellular and vascular features. Diffusion MRI data were acquired with multiple b-values (ranging from 50 to 3500 s/mm2), diffusion times, and echo times in 21 patients with brain tumours of different types and with a wide range of cellular and vascular features. We fitted a selection of diffusion models that resulted from the combination of different types of intracellular, extracellular, and vascular compartments to the signal. We compared the models using criteria for parsimony while aiming at good characterisation of all of the key histological brain tumour components. Finally, we evaluated the parameters of the best-performing model in the differentiation of tumour histotypes, using ADC (Apparent Diffusion Coefficient) as a clinical standard reference, and compared them to histopathology and relevant perfusion MRI metrics. The best-performing model for VERDICT in brain tumours was a three-compartment model accounting for anisotropically hindered and isotropically restricted diffusion and isotropic pseudo-diffusion. VERDICT metrics were compatible with the histological appearance of low-grade gliomas and metastases and reflected differences found by histopathology between multiple biopsy samples within tumours. The comparison between histotypes showed that both the intracellular and vascular fractions tended to be higher in tumours with high cellularity (glioblastoma and metastasis), and quantitative analysis showed a trend toward higher values of the intracellular fraction (fic) within the tumour core with increasing glioma grade. We also observed a trend towards a higher free water fraction in vasogenic oedemas around metastases compared to infiltrative oedemas around glioblastomas and WHO 3 gliomas as well as the periphery of low-grade gliomas. In conclusion, we developed and evaluated a multi-compartment diffusion MRI model for brain tumours based on the VERDICT framework, which showed agreement between non-invasive microstructural estimates and histology and encouraging trends for the differentiation of tumour types and sub-regions.
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Affiliation(s)
- Matteo Figini
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Michele Bailo
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Marcella Callea
- Pathology Unit, IRCCS Ospedale San Raffaele, 20132 Milan, Italy
| | | | - Samira Bouyagoub
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton BN1 9RR, UK
| | - Marco Palombo
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK
| | - Valentina Pieri
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Pietro Mortini
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Andrea Falini
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Daniel C. Alexander
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Mara Cercignani
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton BN1 9RR, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK
| | - Eleftheria Panagiotaki
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
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8
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Spindler M, Palombo M, Zhang H, Thiel CM. Dysfunction of the hypothalamic-pituitary adrenal axis and its influence on aging: the role of the hypothalamus. Sci Rep 2023; 13:6866. [PMID: 37105986 PMCID: PMC10140145 DOI: 10.1038/s41598-023-33922-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 04/20/2023] [Indexed: 04/29/2023] Open
Abstract
As part of the hypothalamic-pituitary adrenal (HPA) axis, the hypothalamus exerts pivotal influence on metabolic and endocrine homeostasis. With age, these processes are subject to considerable change, resulting in increased prevalence of physical disability and cardiac disorders. Yet, research on the aging human hypothalamus is lacking. To assess detailed hypothalamic microstructure in middle adulthood, 39 healthy participants (35-65 years) underwent comprehensive structural magnetic resonance imaging. In addition, we studied HPA axis dysfunction proxied by hair cortisol and waist circumference as potential risk factors for hypothalamic alterations. We provide first evidence of regionally different hypothalamic microstructure, with age effects in its anterior-superior subunit, a critical area for HPA axis regulation. Further, we report that waist circumference was related to increased free water and decreased iron content in this region. In age, hair cortisol was additionally associated with free water content, such that older participants with higher cortisol levels were more vulnerable to free water content increase than younger participants. Overall, our results suggest no general age-related decline in hypothalamic microstructure. Instead, older individuals could be more susceptible to risk factors of hypothalamic decline especially in the anterior-superior subregion, including HPA axis dysfunction, indicating the importance of endocrine and stress management in age.
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Affiliation(s)
- Melanie Spindler
- Biological Psychology, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky Universität Oldenburg, 26129, Oldenburg, Germany.
- Cluster of Excellence "Hearing4all", Carl Von Ossietzky Universität Oldenburg, 26129, Oldenburg, Germany.
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology & School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | - Hui Zhang
- Department of Computer Science and Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK
| | - Christiane M Thiel
- Biological Psychology, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky Universität Oldenburg, 26129, Oldenburg, Germany
- Cluster of Excellence "Hearing4all", Carl Von Ossietzky Universität Oldenburg, 26129, Oldenburg, Germany
- Research Centre Neurosensory Science, Carl von Ossietzky Universität Oldenburg, 26129, Oldenburg, Germany
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9
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Warner W, Palombo M, Cruz R, Callaghan R, Shemesh N, Jones DK, Dell'Acqua F, Ianus A, Drobnjak I. Temporal Diffusion Ratio (TDR) for imaging restricted diffusion: Optimisation and pre-clinical demonstration. Neuroimage 2023; 269:119930. [PMID: 36750150 PMCID: PMC7615244 DOI: 10.1016/j.neuroimage.2023.119930] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 01/12/2023] [Accepted: 02/02/2023] [Indexed: 02/07/2023] Open
Abstract
Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell'Acqua et al., proc. ISMRM 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for informing on pore sizes, in particular the presence of large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion gradient waveforms. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time introduces and optimises the TDR method in simulation for a range of different tissues and scanner constraints and validates it in a pre-clinical demonstration. We consider both substrates containing cylinders and spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration, short diffusion time and high gradient strength with an acquisition with long gradient duration, long diffusion time and low gradient strength, maximises the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for informing on pore sizes and restricted diffusion in general.
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Affiliation(s)
- William Warner
- Centre for Medical Image Computing (CMIC), Computer Science Department, University College London, United Kingdom
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
| | - Renata Cruz
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | | | - Noam Shemesh
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Flavio Dell'Acqua
- NatBrainLab, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
| | - Ivana Drobnjak
- Centre for Medical Image Computing (CMIC), Computer Science Department, University College London, United Kingdom.
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10
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Palombo M, Valindria V, Singh S, Chiou E, Giganti F, Pye H, Whitaker HC, Atkinson D, Punwani S, Alexander DC, Panagiotaki E. Joint estimation of relaxation and diffusion tissue parameters for prostate cancer with relaxation-VERDICT MRI. Sci Rep 2023; 13:2999. [PMID: 36810476 PMCID: PMC9943845 DOI: 10.1038/s41598-023-30182-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 02/17/2023] [Indexed: 02/23/2023] Open
Abstract
This work presents a biophysical model of diffusion and relaxation MRI for prostate called relaxation vascular, extracellular and restricted diffusion for cytometry in tumours (rVERDICT). The model includes compartment-specific relaxation effects providing T1/T2 estimates and microstructural parameters unbiased by relaxation properties of the tissue. 44 men with suspected prostate cancer (PCa) underwent multiparametric MRI (mp-MRI) and VERDICT-MRI followed by targeted biopsy. We estimate joint diffusion and relaxation prostate tissue parameters with rVERDICT using deep neural networks for fast fitting. We tested the feasibility of rVERDICT estimates for Gleason grade discrimination and compared with classic VERDICT and the apparent diffusion coefficient (ADC) from mp-MRI. The rVERDICT intracellular volume fraction fic discriminated between Gleason 3 + 3 and 3 + 4 (p = 0.003) and Gleason 3 + 4 and ≥ 4 + 3 (p = 0.040), outperforming classic VERDICT and the ADC from mp-MRI. To evaluate the relaxation estimates we compare against independent multi-TE acquisitions, showing that the rVERDICT T2 values are not significantly different from those estimated with the independent multi-TE acquisition (p > 0.05). Also, rVERDICT parameters exhibited high repeatability when rescanning five patients (R2 = 0.79-0.98; CV = 1-7%; ICC = 92-98%). The rVERDICT model allows for accurate, fast and repeatable estimation of diffusion and relaxation properties of PCa sensitive enough to discriminate Gleason grades 3 + 3, 3 + 4 and ≥ 4 + 3.
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Affiliation(s)
- Marco Palombo
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK.
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK.
| | - Vanya Valindria
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Saurabh Singh
- Centre for Medical Imaging, University College London, London, UK
| | - Eleni Chiou
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Francesco Giganti
- Division of Surgery and Interventional Science, University College London, London, UK
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
| | - Hayley Pye
- Molecular Diagnostics and Therapeutics Group, Division of Surgery & Interventional Science, University College London, London, UK
| | - Hayley C Whitaker
- Molecular Diagnostics and Therapeutics Group, Division of Surgery & Interventional Science, University College London, London, UK
| | - David Atkinson
- Centre for Medical Imaging, University College London, London, UK
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Eleftheria Panagiotaki
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
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11
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Morelli L, Palombo M, Buizza G, Riva G, Pella A, Fontana G, Imparato S, Iannalfi A, Orlandi E, Paganelli C, Baroni G. Microstructural parameters from DW-MRI for tumour characterization and local recurrence prediction in particle therapy of skull-base chordoma. Med Phys 2023; 50:2900-2913. [PMID: 36602230 DOI: 10.1002/mp.16202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 11/21/2022] [Accepted: 12/15/2022] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Quantitative imaging such as Diffusion-Weighted MRI (DW-MRI) can be exploited to non-invasively derive patient-specific tumor microstructure information for tumor characterization and local recurrence risk prediction in radiotherapy. PURPOSE To characterize tumor microstructure according to proliferative capacity and predict local recurrence through microstructural markers derived from pre-treatment conventional DW-MRI, in skull-base chordoma (SBC) patients treated with proton (PT) and carbon ion (CIRT) radiotherapy. METHODS Forty-eight patients affected by SBC, who underwent conventional DW-MRI before treatment and were enrolled for CIRT (n = 25) or PT (n = 23), were retrospectively selected. Clinically verified local recurrence information (LR) and histological information (Ki-67, proliferation index) were collected. Apparent diffusion coefficient (ADC) maps were calculated from pre-treatment DW-MRI and, from these, a set of microstructural parameters (cellular radius R, volume fraction vf, diffusion D) were derived by applying a fine-tuning procedure to a framework employing Monte Carlo simulations on synthetic cell substrates. In addition, apparent cellularity (ρapp ) was estimated from vf and R for an easier clinical interpretation. Histogram-based metrics (mean, median, variance, entropy) from estimated parameters were considered to investigate differences (Mann-Whitney U-test, α = 0.05) in estimated tumor microstructure in SBCs characterized by low or high cell proliferation (Ki-67). Recurrence-free survival analyses were also performed to assess the ability of the microstructural parameters to stratify patients according to the risk of local recurrence (Kaplan-Meier curves, log-rank test α = 0.05). RESULTS Refined microstructural markers revealed optimal capabilities in discriminating patients according to cell proliferation, achieving best results with mean values (p-values were 0.0383, 0.0284, 0.0284, 0.0468, and 0.0088 for ADC, R, vf, D, and ρapp, respectively). Recurrence-free survival analyses showed significant differences between populations at high and low risk of local recurrence as stratified by entropy values of estimated microstructural parameters (p = 0.0110). CONCLUSION Patient-specific microstructural information was non-invasively derived providing potentially useful tools for SBC treatment personalization and optimization in particle therapy.
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Affiliation(s)
- Letizia Morelli
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - 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
| | - Giulia Buizza
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Giulia Riva
- National Center of Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Andrea Pella
- National Center of Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Giulia Fontana
- National Center of Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Sara Imparato
- National Center of Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Alberto Iannalfi
- National Center of Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Ester Orlandi
- National Center of Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
- National Center of Oncological Hadrontherapy (CNAO), Pavia, Italy
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12
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Margoni M, Pagani E, Preziosa P, Palombo M, Gueye M, Azzimonti M, Filippi M, Rocca MA. In vivo quantification of brain soma and neurite density abnormalities in multiple sclerosis. J Neurol 2023; 270:433-445. [PMID: 36153468 DOI: 10.1007/s00415-022-11386-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 09/12/2022] [Accepted: 09/13/2022] [Indexed: 01/07/2023]
Abstract
BACKGROUND Soma and neurite density imaging (SANDI) is a new biophysical model that incorporates soma in addition to neurite density, thus possibly providing more specific information about the complex pathological processes of multiple sclerosis (MS). PURPOSE To discriminate the pathological abnormalities of MS white matter (WM) lesions, normal-appearing (NA) WM and cortex and to evaluate the associations among SANDI-derived measures, clinical disability, and conventional MRI variables. METHODS Twenty healthy controls (HC) and 23 MS underwent a 3 T brain MRI. Using SANDI on diffusion-weighted sequence, the fractions of neurite (fneurite) and soma (fsoma) were assessed in WM lesions, NAWM, and cortex. RESULTS Compared to HC WM, MS NAWM showed lower fneurite (false discovery rate [FDR]-p = 0.011). In MS patients, WM lesions showed lower fneurite and fsoma compared to both HC and MS NAWM (FDR-p < 0.001 for all). In the cortex, MS patients had lower fneurite and fsoma compared to HC (FDR-p ≤ 0.009). Compared to both HC and RRMS, PMS patients had lower fneurite in NAWM (vs HC: FDR-p < 0.001; vs RRMS: FDR-p = 0.003) and cortex (vs HC: FDR-p < 0.001; vs RRMS: p = 0.031, not surviving FDR correction), and lower cortical fsoma (vs HC: FDR-p < 0.001; vs RRMS: FDR-p = 0.009). Compared to HC, PMS also showed a higher fsoma in NAWM (FDR-p = 0.015). Fneurite and fsoma in the different brain compartments were correlated with age, phenotype, disease duration, disability, WM lesion volumes, normalized brain, cortical, and WM volumes (r from - 0.761 to 0.821, FDR-p ≤ 0.4). CONCLUSIONS SANDI may represent a clinically relevant model to discriminate different neurodegenerative phenomena that gradually accumulate through MS disease course.
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Affiliation(s)
- Monica Margoni
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paolo Preziosa
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | - Mor Gueye
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Matteo Azzimonti
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Maria Assunta Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Vita-Salute San Raffaele University, Milan, Italy.
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13
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Schilling KG, Palombo M, O'Grady KP, Combes AJE, Anderson AW, Landman BA, Smith SA. Minimal number of sampling directions for robust measures of the spherical mean diffusion weighted signal: Effects of sampling directions, b-value, signal-to-noise ratio, hardware, and fitting strategy. Magn Reson Imaging 2022; 94:25-35. [PMID: 35931321 PMCID: PMC9904413 DOI: 10.1016/j.mri.2022.07.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/27/2022] [Accepted: 07/27/2022] [Indexed: 01/13/2023]
Abstract
Several recent multi-compartment diffusion MRI investigations and modeling strategies have utilized the orientationally-averaged, or spherical mean, diffusion-weighted signal to study tissue microstructure of the central nervous system. Most experimental designs sample a large number of diffusion weighted directions in order to calculate the spherical mean signal, however, sampling a subset of these directions may increase scanning efficiency and enable either a decrease in scan time or the ability to sample more diffusion weightings. Here, we aim to determine the minimum number of gradient directions needed for a robust measurement of the spherical mean signal. We used computer simulations to characterize the variation of the measured spherical mean signal as a function of the number of gradient directions, while also investigating the effects of diffusion weighting (b-value), signal-to-noise ratio (SNR), available hardware, and spherical mean fitting strategy. We then utilize empirically acquired data in the brain and spinal cord to validate simulations, showing experimental results are in good agreement with simulations. We summarize these results by providing an intuitive lookup table to facilitate the determination of the minimal number of sampling directions needed for robust spherical mean measurements, and give recommendations based on SNR and experimental conditions.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Electrical Engineering and Computer Engineering, Vanderbilt University, Nashville, TN, United States.
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
| | - Kristin P O'Grady
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Anna J E Combes
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Adam W Anderson
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Bennett A Landman
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Electrical Engineering and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Seth A Smith
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
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Pérot JB, Célestine M, Palombo M, Dhenain M, Humbert S, Brouillet E, Flament J. Longitudinal multimodal MRI characterization of a knock-in mouse model of Huntington's disease reveals early gray and white matter alterations. Hum Mol Genet 2022; 31:3581-3596. [PMID: 35147158 PMCID: PMC9616570 DOI: 10.1093/hmg/ddac036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 01/19/2022] [Accepted: 01/31/2022] [Indexed: 11/24/2022] Open
Abstract
Pathogenesis of the inherited neurodegenerative disorder Huntington's disease (HD) is progressive with a long presymptomatic phase in which subtle changes occur up to 15 years before the onset of symptoms. Thus, there is a need for early, functional biomarker to better understand disease progression and to evaluate treatment efficacy far from onset. Recent studies have shown that white matter may be affected early in mutant HTT gene carriers. A previous study performed on 12 months old Ki140CAG mice showed reduced glutamate level measured by Chemical Exchange Saturation Transfer of glutamate (gluCEST), especially in the corpus callosum. In this study, we scanned longitudinally Ki140CAG mice with structural MRI, diffusion tensor imaging, gluCEST and magnetization transfer imaging, in order to assess white matter integrity over the life of this mouse model characterized by slow progression of symptoms. Our results show early defects of diffusion properties in the anterior part of the corpus callosum at 5 months of age, preceding gluCEST defects in the same region at 8 and 12 months that spread to adjacent regions. At 12 months, frontal and piriform cortices showed reduced gluCEST, as well as the pallidum. MT imaging showed reduced signal in the septum at 12 months. Cortical and striatal atrophy then appear at 18 months. Vulnerability of the striatum and motor cortex, combined with alterations of anterior corpus callosum, seems to point out the potential role of white matter in the brain dysfunction that characterizes HD and the pertinence of gluCEST and DTI as biomarkers in HD.
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Affiliation(s)
- Jean-Baptiste Pérot
- Laboratoire des Maladies Neurodégénératives, Molecular Imaging Research Center (MIRCen), Université Paris-Saclay, Commissariat à l’Energie Atomique et aux Energies Alternatives (CEA), Centre National de la Recherche Scientifique (CNRS), Fontenay -aux-Roses 92260, France
| | - Marina Célestine
- Laboratoire des Maladies Neurodégénératives, Molecular Imaging Research Center (MIRCen), Université Paris-Saclay, Commissariat à l’Energie Atomique et aux Energies Alternatives (CEA), Centre National de la Recherche Scientifique (CNRS), Fontenay -aux-Roses 92260, France
| | - Marco Palombo
- Department of Computer Science, Centre for Medical Image Computing, University College London, London WC1E 6BT, UK
| | - Marc Dhenain
- Laboratoire des Maladies Neurodégénératives, Molecular Imaging Research Center (MIRCen), Université Paris-Saclay, Commissariat à l’Energie Atomique et aux Energies Alternatives (CEA), Centre National de la Recherche Scientifique (CNRS), Fontenay -aux-Roses 92260, France
| | - Sandrine Humbert
- Université Grenoble Alpes, INSERM, U1216, Grenoble Institut Neurosciences, Grenoble 38000 , France
| | - Emmanuel Brouillet
- Laboratoire des Maladies Neurodégénératives, Molecular Imaging Research Center (MIRCen), Université Paris-Saclay, Commissariat à l’Energie Atomique et aux Energies Alternatives (CEA), Centre National de la Recherche Scientifique (CNRS), Fontenay -aux-Roses 92260, France
| | - Julien Flament
- Laboratoire des Maladies Neurodégénératives, Molecular Imaging Research Center (MIRCen), Université Paris-Saclay, Commissariat à l’Energie Atomique et aux Energies Alternatives (CEA), Centre National de la Recherche Scientifique (CNRS), Fontenay -aux-Roses 92260, France
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15
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Jelescu IO, de Skowronski A, Geffroy F, Palombo M, Novikov DS. Neurite Exchange Imaging (NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange. Neuroimage 2022; 256:119277. [PMID: 35523369 PMCID: PMC10363376 DOI: 10.1016/j.neuroimage.2022.119277] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 04/26/2022] [Accepted: 05/01/2022] [Indexed: 01/18/2023] Open
Abstract
Biophysical models of diffusion in white matter have been center-stage over the past two decades and are essentially based on what is now commonly referred to as the "Standard Model" (SM) of non-exchanging anisotropic compartments with Gaussian diffusion. In this work, we focus on diffusion MRI in gray matter, which requires rethinking basic microstructure modeling blocks. In particular, at least three contributions beyond the SM need to be considered for gray matter: water exchange across the cell membrane - between neurites and the extracellular space; non-Gaussian diffusion along neuronal and glial processes - resulting from structural disorder; and signal contribution from soma. For the first contribution, we propose Neurite Exchange Imaging (NEXI) as an extension of the SM of diffusion, which builds on the anisotropic Kärger model of two exchanging compartments. Using datasets acquired at multiple diffusion weightings (b) and diffusion times (t) in the rat brain in vivo, we investigate the suitability of NEXI to describe the diffusion signal in the gray matter, compared to the other two possible contributions. Our results for the diffusion time window 20-45 ms show minimal diffusivity time-dependence and more pronounced kurtosis decay with time, which is well fit by the exchange model. Moreover, we observe lower signal for longer diffusion times at high b. In light of these observations, we identify exchange as the mechanism that best explains these signal signatures in both low-b and high-b regime, and thereby propose NEXI as the minimal model for gray matter microstructure mapping. We finally highlight multi-b multi-t acquisition protocols as being best suited to estimate NEXI model parameters reliably. Using this approach, we estimate the inter-compartment water exchange time to be 15 - 60 ms in the rat cortex and hippocampus in vivo, which is of the same order or shorter than the diffusion time in typical diffusion MRI acquisitions. This suggests water exchange as an essential component for interpreting diffusion MRI measurements in gray matter.
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Affiliation(s)
- Ileana O Jelescu
- CIBM Center for Biomedical Imaging, Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland; School of Biology and Medicine, University of Lausanne (UNIL), Lausanne, Switzerland.
| | - Alexandre de Skowronski
- CIBM Center for Biomedical Imaging, Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | | | - Marco Palombo
- School of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK; School of Computer Science and Informatics, Cardiff University, Cardiff, UK; Department of Computer Science, Centre for Medical Image Computing, University College London, London, UK
| | - Dmitry S Novikov
- Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA
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16
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Ianuş A, Carvalho J, Fernandes FF, Cruz R, Chavarrias C, Palombo M, Shemesh N. Soma and Neurite Density MRI (SANDI) of the in-vivo mouse brain and comparison with the Allen Brain Atlas. Neuroimage 2022; 254:119135. [PMID: 35339686 DOI: 10.1016/j.neuroimage.2022.119135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/15/2022] [Accepted: 03/22/2022] [Indexed: 10/18/2022] Open
Abstract
Diffusion MRI (dMRI) provides unique insights into the neural tissue milieu by probing interactions between diffusing molecules and tissue microstructure. Most dMRI techniques focus on white matter (WM) tissues, nevertheless, interest in gray matter characterizations is growing. The Soma and Neurite Density MRI (SANDI) methodology harnesses a model incorporating water diffusion in spherical objects (assumed to be associated with cell bodies) and in impermeable "sticks" (assumed to represent neurites), which potentially enables the characterization of cellular and neurite densities. Recognising the importance of rodents in animal models of development, aging, plasticity, and disease, we here employ SANDI for in-vivo preclinical imaging and provide a first validation of the methodology by comparing SANDI metrics with cellular density reflected by the Allen mouse brain atlas. SANDI was implemented on a 9.4T scanner equipped with a cryogenic coil, and in-vivo experiments were carried out on N = 6 mice. Pixelwise, ROI-based, and atlas comparisons were performed, magnitude vs. real-valued analyses were compared, and shorter acquisitions with reduced the number of b-value shells were investigated. Our findings reveal good reproducibility of the SANDI parameters, including the sphere and stick fractions, as well as sphere size (CoV < 7%, 12% and 3%, respectively). Additionally, we find a very good rank correlation between SANDI-driven sphere fraction and Allen mouse brain atlas contrast that represents cellular density. We conclude that SANDI is a viable preclinical MRI technique that can greatly contribute to research on brain tissue microstructure.
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Affiliation(s)
- Andrada Ianuş
- Champalimaud Research, Champalimaud Foundation, Av. Brasilia, Lisbon 1400-038, Portugal.
| | - Joana Carvalho
- Champalimaud Research, Champalimaud Foundation, Av. Brasilia, Lisbon 1400-038, Portugal
| | - Francisca F Fernandes
- Champalimaud Research, Champalimaud Foundation, Av. Brasilia, Lisbon 1400-038, Portugal
| | - Renata Cruz
- Champalimaud Research, Champalimaud Foundation, Av. Brasilia, Lisbon 1400-038, Portugal
| | - Cristina Chavarrias
- Champalimaud Research, Champalimaud Foundation, Av. Brasilia, Lisbon 1400-038, Portugal
| | - Marco Palombo
- Center for Medical Image Computing, Department of Computer Science, University College London, UK; Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, UK; School of Computer Science and Informatics, Cardiff University, UK
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Foundation, Av. Brasilia, Lisbon 1400-038, Portugal.
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17
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Palombo M, Barbetta A, Cametti C, Favero G, Capuani S. Transient Anomalous Diffusion MRI Measurement Discriminates Porous Polymeric Matrices Characterized by Different Sub-Microstructures and Fractal Dimension. Gels 2022; 8:gels8020095. [PMID: 35200475 PMCID: PMC8871551 DOI: 10.3390/gels8020095] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 01/20/2022] [Accepted: 01/30/2022] [Indexed: 11/17/2022] Open
Abstract
Considering the current development of new nanostructured and complex materials and gels, it is critical to develop a sub-micro-scale sensitivity tool to quantify experimentally new parameters describing sub-microstructured porous systems. Diffusion NMR, based on the measurement of endogenous water's diffusion displacement, offers unique information on the structural features of materials and tissues. In this paper, we applied anomalous diffusion NMR protocols to quantify the subdiffusion of water and to measure, in an alternative, non-destructive and non-invasive modality, the fractal dimension dw of systems characterized by micro and sub-micro geometrical structures. To this end, three highly heterogeneous porous-polymeric matrices were studied. All the three matrices composed of glycidylmethacrylate-divynilbenzene porous monoliths obtained through the High Internal Phase Emulsion technique were characterized by pores of approximately spherical symmetry, with diameters in the range of 2-10 μm. Pores were interconnected by a plurality of window holes present on pore walls, which were characterized by size coverings in the range of 0.5-2 μm. The walls were characterized by a different degree of surface roughness. Moreover, complementary techniques, namely Field Emission Scanning Electron Microscopy (FE-SEM) and dielectric spectroscopy, were used to corroborate the NMR results. The experimental results showed that the anomalous diffusion α parameter that quantifies subdiffusion and dw = 2/α changed in parallel to the specific surface area S (or the surface roughness) of the porous matrices, showing a submicroscopic sensitivity. The results reported here suggest that the anomalous diffusion NMR method tested may be a valid experimental tool to corroborate theoretical and simulation results developed and performed for describing highly heterogeneous and complex systems. On the other hand, non-invasive and non-destructive anomalous subdiffusion NMR may be a useful tool to study the characteristic features of new highly heterogeneous nanostructured and complex functional materials and gels useful in cultural heritage applications, as well as scaffolds useful in tissue engineering.
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Affiliation(s)
- Marco Palombo
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff CF24 4HQ, UK;
- School of Computer Science and Informatics, Cardiff University, Maindy Road, Cardiff CF24 4HQ, UK
| | - Andrea Barbetta
- Chemistry Department, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy;
| | - Cesare Cametti
- Physics Department, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy;
| | - Gabriele Favero
- Department of Environmental Biology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy;
| | - Silvia Capuani
- National Research Council—Institute for Complex Systems (CNR-ISC) c/o, Physics Department, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
- CREF, Museo Storico Della Fisica e Centro Studi e Ricerche Enrico Fermi, 00185 Rome, Italy
- Correspondence: ; Tel.: +39-06-4991-3928
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18
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de Almeida Martins JP, Nilsson M, Lampinen B, Palombo M, While PT, Westin CF, Szczepankiewicz F. Neural networks for parameter estimation in microstructural MRI: Application to a diffusion-relaxation model of white matter. Neuroimage 2021; 244:118601. [PMID: 34562578 PMCID: PMC9651573 DOI: 10.1016/j.neuroimage.2021.118601] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 08/26/2021] [Accepted: 09/18/2021] [Indexed: 12/14/2022] Open
Abstract
Specific features of white matter microstructure can be investigated by using biophysical models to interpret relaxation-diffusion MRI brain data. Although more intricate models have the potential to reveal more details of the tissue, they also incur time-consuming parameter estimation that may converge to inaccurate solutions due to a prevalence of local minima in a degenerate fitting landscape. Machine-learning fitting algorithms have been proposed to accelerate the parameter estimation and increase the robustness of the attained estimates. So far, learning-based fitting approaches have been restricted to microstructural models with a reduced number of independent model parameters where dense sets of training data are easy to generate. Moreover, the degree to which machine learning can alleviate the degeneracy problem is poorly understood. For conventional least-squares solvers, it has been shown that degeneracy can be avoided by acquisition with optimized relaxation-diffusion-correlation protocols that include tensor-valued diffusion encoding. Whether machine-learning techniques can offset these acquisition requirements remains to be tested. In this work, we employ artificial neural networks to vastly accelerate the parameter estimation for a recently introduced relaxation-diffusion model of white matter microstructure. We also develop strategies for assessing the accuracy and sensitivity of function fitting networks and use those strategies to explore the impact of the acquisition protocol. The developed learning-based fitting pipelines were tested on relaxation-diffusion data acquired with optimal and sub-optimal acquisition protocols. Networks trained with an optimized protocol were observed to provide accurate parameter estimates within short computational times. Comparing neural networks and least-squares solvers, we found the performance of the former to be less affected by sub-optimal protocols; however, model fitting networks were still susceptible to degeneracy issues and their use could not fully replace a careful design of the acquisition protocol.
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Affiliation(s)
- João P de Almeida Martins
- Department of Clinical Sciences, Radiology, Lund University, Lund, Sweden; Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway.
| | - Markus Nilsson
- Department of Clinical Sciences, Radiology, Lund University, Lund, Sweden
| | - Björn Lampinen
- Department of Clinical Sciences, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Marco Palombo
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom
| | - Peter T While
- Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway; Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Carl-Fredrik Westin
- Radiology, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Filip Szczepankiewicz
- Department of Clinical Sciences, Radiology, Lund University, Lund, Sweden; Radiology, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
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19
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Slator PJ, Palombo M, Miller KL, Westin C, Laun F, Kim D, Haldar JP, Benjamini D, Lemberskiy G, de Almeida Martins JP, Hutter J. Combined diffusion-relaxometry microstructure imaging: Current status and future prospects. Magn Reson Med 2021; 86:2987-3011. [PMID: 34411331 PMCID: PMC8568657 DOI: 10.1002/mrm.28963] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 06/25/2021] [Accepted: 07/20/2021] [Indexed: 12/15/2022]
Abstract
Microstructure imaging seeks to noninvasively measure and map microscopic tissue features by pairing mathematical modeling with tailored MRI protocols. This article reviews an emerging paradigm that has the potential to provide a more detailed assessment of tissue microstructure-combined diffusion-relaxometry imaging. Combined diffusion-relaxometry acquisitions vary multiple MR contrast encodings-such as b-value, gradient direction, inversion time, and echo time-in a multidimensional acquisition space. When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters-such as diffusivity, T 1 , T 2 , and T 2 ∗ . This opens the possibility of disentangling multiple tissue compartments (within voxels) that are indistinguishable with single-contrast scans, enabling a new generation of microstructural maps with improved biological sensitivity and specificity.
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Affiliation(s)
- Paddy J. Slator
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Marco Palombo
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Carl‐Fredrik Westin
- Department of RadiologyBrigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA
| | - Frederik Laun
- Institute of RadiologyUniversity Hospital ErlangenFriedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Daeun Kim
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Justin P. Haldar
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Dan Benjamini
- The Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBethesdaMDUSA
- The Center for Neuroscience and Regenerative MedicineUniformed Service University of the Health SciencesBethesdaMDUSA
| | | | - Joao P. de Almeida Martins
- Division of Physical Chemistry, Department of ChemistryLund UniversityLundSweden
- Department of Radiology and Nuclear MedicineSt. Olav’s University HospitalTrondheimNorway
| | - Jana Hutter
- Centre for Biomedical EngineeringSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
- Centre for the Developing BrainSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
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20
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Morelli L, Buizza G, Palombo M, Riva G, Fontana G, Imparato S, Iannalfi A, Orlandi E, Paganelli C, Baroni G. Analysis of tumour microstructure estimation from conventional diffusion MRI and application to skull-base chordoma . Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:3761-3764. [PMID: 34892054 DOI: 10.1109/embc46164.2021.9630129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Skull-base chordoma (SBC) is a rare tumour whose molecular and radiological characteristics are still being investigated. In neuro-oncology microstructural imaging techniques, like diffusion-weighted MRI (DW-MRI), have been widely investigated, with the apparent diffusion coefficient (ADC) being one of the most used DW-MRI parameters due to its ease of acquisition and computation. ADC is a potential biomarker without a clear link to microstructure. The aim of this work was to derive microstructural information from conventional ADC, showing its potential for the characterisation of skull-base chordomas. Sixteen patients affected by SBC, who underwent conventional DW-MRI were retrospectively selected. From mono-exponential fits of DW-MRI, ADC maps were estimated using different sets of b-values. DW-MRI signals were simulated from synthetic substrates , which mimic the cellular packing of a tumour tissue with well-defined microstructural features. Starting from a published method, an error-driven procedure was evaluated to improve the estimates of microstructural parameters obtained through the simulated signals. A quantitative description of the tumour microstructure was then obtained from the DW-MRI images. This allowed successfully differentiating patients according to histologically-verified cell proliferation information.Clinical Relevance - The impact on cancer management derives from the expected improvement of radiation treatment quality tailored to a patient-specific non-invasive description of tumour microstructure.
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21
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De Luca A, Ianus A, Leemans A, Palombo M, Shemesh N, Zhang H, Alexander DC, Nilsson M, Froeling M, Biessels GJ, Zucchelli M, Frigo M, Albay E, Sedlar S, Alimi A, Deslauriers-Gauthier S, Deriche R, Fick R, Afzali M, Pieciak T, Bogusz F, Aja-Fernández S, Özarslan E, Jones DK, Chen H, Jin M, Zhang Z, Wang F, Nath V, Parvathaneni P, Morez J, Sijbers J, Jeurissen B, Fadnavis S, Endres S, Rokem A, Garyfallidis E, Sanchez I, Prchkovska V, Rodrigues P, Landman BA, Schilling KG. On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge. Neuroimage 2021; 240:118367. [PMID: 34237442 PMCID: PMC7615259 DOI: 10.1016/j.neuroimage.2021.118367] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 06/09/2021] [Accepted: 07/04/2021] [Indexed: 12/29/2022] Open
Abstract
Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.
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Affiliation(s)
- Alberto De Luca
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Alexander Leemans
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Marco Palombo
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Hui Zhang
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Markus Nilsson
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | - Martijn Froeling
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Geert-Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mauro Zucchelli
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | - Matteo Frigo
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | - Enes Albay
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France; Istanbul Technical University, Istanbul, Turkey
| | - Sara Sedlar
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | - Abib Alimi
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | | | - Rachid Deriche
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | | | - Maryam Afzali
- Cardiff University Brain Research, Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Tomasz Pieciak
- AGH University of Science and Technology, Kraków, Poland; LPI, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Fabian Bogusz
- AGH University of Science and Technology, Kraków, Poland
| | | | - Evren Özarslan
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Derek K Jones
- Cardiff University Brain Research, Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Haoze Chen
- School of Instruments and Electronics, North University of China, Taiyuan, China
| | - Mingwu Jin
- Department of Physics, University of Texas at Arlington, Arlington, USA
| | - Zhijie Zhang
- School of Instruments and Electronics, North University of China, Taiyuan, China
| | - Fengxiang Wang
- School of Instruments and Electronics, North University of China, Taiyuan, China
| | | | | | - Jan Morez
- Imec-Vision lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Jan Sijbers
- Imec-Vision lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Ben Jeurissen
- Imec-Vision lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Shreyas Fadnavis
- Intelligent Systems Engineering, Indiana University Bloomington, Indiana, USA
| | - Stefan Endres
- Leibniz Institute for Materials Engineering - IWT, Faculty of Production Engineering, University of Bremen, Bremen, Germany
| | - Ariel Rokem
- Department of Psychology and the eScience Institute, University of Washington, Seattle, WA USA
| | | | | | | | | | - Bennet A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, USA
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, USA; Department of Radiology and Radiological Science, Vanderbilt University Medical Center, Nashville, USA
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22
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Gyori NG, Palombo M, Clark CA, Zhang H, Alexander DC. Training data distribution significantly impacts the estimation of tissue microstructure with machine learning. Magn Reson Med 2021; 87:932-947. [PMID: 34545955 DOI: 10.1002/mrm.29014] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 08/30/2021] [Accepted: 08/30/2021] [Indexed: 12/18/2022]
Abstract
PURPOSE Supervised machine learning (ML) provides a compelling alternative to traditional model fitting for parameter mapping in quantitative MRI. The aim of this work is to demonstrate and quantify the effect of different training data distributions on the accuracy and precision of parameter estimates when supervised ML is used for fitting. METHODS We fit a two- and three-compartment biophysical model to diffusion measurements from in-vivo human brain, as well as simulated diffusion data, using both traditional model fitting and supervised ML. For supervised ML, we train several artificial neural networks, as well as random forest regressors, on different distributions of ground truth parameters. We compare the accuracy and precision of parameter estimates obtained from the different estimation approaches using synthetic test data. RESULTS When the distribution of parameter combinations in the training set matches those observed in healthy human data sets, we observe high precision, but inaccurate estimates for atypical parameter combinations. In contrast, when training data is sampled uniformly from the entire plausible parameter space, estimates tend to be more accurate for atypical parameter combinations but may have lower precision for typical parameter combinations. CONCLUSION This work highlights that estimation of model parameters using supervised ML depends strongly on the training-set distribution. We show that high precision obtained using ML may mask strong bias, and visual assessment of the parameter maps is not sufficient for evaluating the quality of the estimates.
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Affiliation(s)
- Noemi G Gyori
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.,Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Marco Palombo
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Christopher A Clark
- Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Hui Zhang
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
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23
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Afzali M, Nilsson M, Palombo M, Jones DK. SPHERIOUSLY? The challenges of estimating sphere radius non-invasively in the human brain from diffusion MRI. Neuroimage 2021; 237:118183. [PMID: 34020013 PMCID: PMC8285594 DOI: 10.1016/j.neuroimage.2021.118183] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 04/25/2021] [Accepted: 05/16/2021] [Indexed: 11/16/2022] Open
Abstract
The Soma and Neurite Density Imaging (SANDI) three-compartment model was recently proposed to disentangle cylindrical and spherical geometries, attributed to neurite and soma compartments, respectively, in brain tissue. There are some recent advances in diffusion-weighted MRI signal encoding and analysis (including the use of multiple so-called 'b-tensor' encodings and analysing the signal in the frequency-domain) that have not yet been applied in the context of SANDI. In this work, using: (i) ultra-strong gradients; (ii) a combination of linear, planar, and spherical b-tensor encodings; and (iii) analysing the signal in the frequency domain, three main challenges to robust estimation of sphere size were identified: First, the Rician noise floor in magnitude-reconstructed data biases estimates of sphere properties in a non-uniform fashion. It may cause overestimation or underestimation of the spherical compartment size and density. This can be partly ameliorated by accounting for the noise floor in the estimation routine. Second, even when using the strongest diffusion-encoding gradient strengths available for human MRI, there is an empirical lower bound on the spherical signal fraction and radius that can be detected and estimated robustly. For the experimental setup used here, the lower bound on the sphere signal fraction was approximately 10%. We employed two different ways of establishing the lower bound for spherical radius estimates in white matter. The first, examining power-law relationships between the DW-signal and diffusion weighting in empirical data, yielded a lower bound of 7μm, while the second, pure Monte Carlo simulations, yielded a lower limit of 3μm and in this low radii domain, there is little differentiation in signal attenuation. Third, if there is sensitivity to the transverse intra-cellular diffusivity in cylindrical structures, e.g., axons and cellular projections, then trying to disentangle two diffusion-time-dependencies using one experimental parameter (i.e., change in frequency-content of the encoding waveform) makes spherical radii estimates particularly challenging. We conclude that due to the aforementioned challenges spherical radii estimates may be biased when the corresponding sphere signal fraction is low, which must be considered.
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Affiliation(s)
- Maryam Afzali
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
| | - Markus Nilsson
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden.
| | - Marco Palombo
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
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24
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Kerkelä L, Nery F, Callaghan R, Zhou F, Gyori NG, Szczepankiewicz F, Palombo M, Parker GJM, Zhang H, Hall MG, Clark CA. Comparative analysis of signal models for microscopic fractional anisotropy estimation using q-space trajectory encoding. Neuroimage 2021; 242:118445. [PMID: 34375753 DOI: 10.1016/j.neuroimage.2021.118445] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 07/06/2021] [Accepted: 08/02/2021] [Indexed: 12/12/2022] Open
Abstract
Microscopic diffusion anisotropy imaging using diffusion-weighted MRI and multidimensional diffusion encoding is a promising method for quantifying clinically and scientifically relevant microstructural properties of neural tissue. Several methods for estimating microscopic fractional anisotropy (µFA), a normalized measure of microscopic diffusion anisotropy, have been introduced but the differences between the methods have received little attention thus far. In this study, the accuracy and precision of µFA estimation using q-space trajectory encoding and different signal models were assessed using imaging experiments and simulations. Three healthy volunteers and a microfibre phantom were imaged with five non-zero b-values and gradient waveforms encoding linear and spherical b-tensors. Since the ground-truth µFA was unknown in the imaging experiments, Monte Carlo random walk simulations were performed using axon-mimicking fibres for which the ground truth was known. Furthermore, parameter bias due to time-dependent diffusion was quantified by repeating the simulations with tuned waveforms, which have similar power spectra, and with triple diffusion encoding, which, unlike q-space trajectory encoding, is not based on the assumption of time-independent diffusion. The truncated cumulant expansion of the powder-averaged signal, gamma-distributed diffusivities assumption, and q-space trajectory imaging, a generalization of the truncated cumulant expansion to individual signals, were used to estimate µFA. The gamma-distributed diffusivities assumption consistently resulted in greater µFA values than the second order cumulant expansion, 0.1 greater when averaged over the whole brain. In the simulations, the generalized cumulant expansion provided the most accurate estimates. Importantly, although time-dependent diffusion caused significant overestimation of µFA using all the studied methods, the simulations suggest that the resulting bias in µFA is less than 0.1 in human white matter.
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Affiliation(s)
- Leevi Kerkelä
- UCL Great Ormond Street Institute of Child Health, University College London, London, UK.
| | - Fabio Nery
- UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Ross Callaghan
- UCL Centre for Medical Image Computing, University College London, London, UK
| | - Fenglei Zhou
- UCL Centre for Medical Image Computing, University College London, London, UK; UCL School of Pharmacy, University College London, London, UK
| | - Noemi G Gyori
- UCL Centre for Medical Image Computing, University College London, London, UK; UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Filip Szczepankiewicz
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, US; Harvard Medical School, Boston, Massachusetts, US; Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Marco Palombo
- UCL Centre for Medical Image Computing, University College London, London, UK
| | - Geoff J M Parker
- UCL Centre for Medical Image Computing, University College London, London, UK; Bioxydyn Limited, Manchester, UK; UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Hui Zhang
- UCL Centre for Medical Image Computing, University College London, London, UK
| | - Matt G Hall
- UCL Great Ormond Street Institute of Child Health, University College London, London, UK; National Physical Laboratory, Teddington, UK
| | - Chris A Clark
- UCL Great Ormond Street Institute of Child Health, University College London, London, UK
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25
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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: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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26
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Slator PJ, Hutter J, Marinescu RV, Palombo M, Jackson LH, Ho A, Chappell LC, Rutherford M, Hajnal JV, Alexander DC. Data-Driven multi-Contrast spectral microstructure imaging with InSpect: INtegrated SPECTral component estimation and mapping. Med Image Anal 2021; 71:102045. [PMID: 33934005 PMCID: PMC8543043 DOI: 10.1016/j.media.2021.102045] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 02/08/2021] [Accepted: 03/16/2021] [Indexed: 11/19/2022]
Abstract
Unsupervised learning technique for spectroscopic analysis of quantitative MRI. Shares information across voxels to improve estimation of multi-dimensional or single-dimensional spectra. Spectral maps are dramatically improved compared to existing approaches. Can potentially identify and map tissue environments; in placental diffusion-relaxometry MRI we demonstrate that it identifies components that correspond to distinct tissue types.
We introduce and demonstrate an unsupervised machine learning technique for spectroscopic analysis of quantitative MRI experiments. Our algorithm supports estimation of one-dimensional spectra from single-contrast data, and multidimensional correlation spectra from simultaneous multi-contrast data. These spectrum-based approaches allow model-free investigation of tissue properties, but require regularised inversion of a Laplace transform or Fredholm integral, which is an ill-posed calculation. Here we present a method that addresses this limitation in a data-driven way. The algorithm simultaneously estimates a canonical basis of spectral components and voxelwise maps of their weightings, thereby pooling information across whole images to regularise the ill-posed problem. We show in simulations that our algorithm substantially outperforms current voxelwise spectral approaches. We demonstrate the method on multi-contrast diffusion-relaxometry placental MRI scans, revealing anatomically-relevant sub-structures, and identifying dysfunctional placentas. Our algorithm vastly reduces the data required to reliably estimate spectra, opening up the possibility of quantitative MRI spectroscopy in a wide range of new applications. Our InSpect code is available at github.com/paddyslator/inspect.
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Affiliation(s)
- Paddy J Slator
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK.
| | - Jana Hutter
- Centre for the Developing Brain, Kings College London, London, UK; Biomedical Engineering Department, Kings College London, London, UK
| | - Razvan V Marinescu
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
| | - Marco Palombo
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
| | - Laurence H Jackson
- Centre for the Developing Brain, Kings College London, London, UK; Biomedical Engineering Department, Kings College London, London, UK
| | - Alison Ho
- Women's Health Department, King's College London, London, UK
| | - Lucy C Chappell
- Women's Health Department, King's College London, London, UK
| | - Mary Rutherford
- Centre for the Developing Brain, Kings College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, Kings College London, London, UK; Biomedical Engineering Department, Kings College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
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27
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Genovese G, Palombo M, Santin MD, Valette J, Ligneul C, Aigrot MS, Abdoulkader N, Langui D, Millecamps A, Baron-Van Evercooren A, Stankoff B, Lehericy S, Petiet A, Branzoli F. Inflammation-driven glial alterations in the cuprizone mouse model probed with diffusion-weighted magnetic resonance spectroscopy at 11.7 T. NMR Biomed 2021; 34:e4480. [PMID: 33480101 DOI: 10.1002/nbm.4480] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 01/02/2021] [Indexed: 06/12/2023]
Abstract
Inflammation of brain tissue is a complex response of the immune system to the presence of toxic compounds or to cell injury, leading to a cascade of pathological processes that include glial cell activation. Noninvasive MRI markers of glial reactivity would be very useful for in vivo detection and monitoring of inflammation processes in the brain, as well as for evaluating the efficacy of personalized treatments. Due to their specific location in glial cells, myo-inositol (mIns) and choline compounds (tCho) seem to be the best candidates for probing glial-specific intra-cellular compartments. However, their concentrations quantified using conventional proton MRS are not specific for inflammation. In contrast, it has been recently suggested that mIns intra-cellular diffusion, measured using diffusion-weighted MRS (DW-MRS) in a mouse model of reactive astrocytes, could be a specific marker of astrocytic hypertrophy. In order to evaluate the specificity of both mIns and tCho diffusion to inflammation-driven glial alterations, we performed DW-MRS in a volume of interest containing the corpus callosum and surrounding tissue of cuprizone-fed mice after 6 weeks of intoxication, and evaluated the extent of astrocytic and microglial alterations using immunohistochemistry. Both mIns and tCho apparent diffusion coefficients were significantly elevated in cuprizone-fed mice compared with control mice, and histologic evaluation confirmed the presence of severe inflammation. Additionally, mIns and tCho diffusion showed, respectively, strong and moderate correlations with histological measures of astrocytic and microglial area fractions, confirming DW-MRS as a promising tool for specific detection of glial changes under pathological conditions.
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Affiliation(s)
- Guglielmo Genovese
- Center for Neuroimaging Research-CENIR, Paris Brain Institute (Institut du Cerveau-ICM), Paris, France
- Hôpital Pitié-Salpêtrière, ICM, Sorbonne Université, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - Marco Palombo
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, UK
| | - Mathieu D Santin
- Center for Neuroimaging Research-CENIR, Paris Brain Institute (Institut du Cerveau-ICM), Paris, France
- Hôpital Pitié-Salpêtrière, ICM, Sorbonne Université, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - Julien Valette
- Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), MIRCen, Fontenay-aux-Roses, France
- Neurodegenerative Diseases Laboratory, UMR9199, CEA, CNRS, Université Paris Sud, Université Paris-Saclay, Fontenay-aux-Roses, France
| | - Clémence Ligneul
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Marie-Stéphane Aigrot
- Hôpital Pitié-Salpêtrière, ICM, Sorbonne Université, Inserm U 1127, CNRS UMR 7225, Paris, France
- Core Facility ICM Quant, Institut du Cerveau-ICM, Paris, France
| | - Nasteho Abdoulkader
- Center for Neuroimaging Research-CENIR, Paris Brain Institute (Institut du Cerveau-ICM), Paris, France
| | - Dominique Langui
- Hôpital Pitié-Salpêtrière, ICM, Sorbonne Université, Inserm U 1127, CNRS UMR 7225, Paris, France
- Core Facility ICM Quant, Institut du Cerveau-ICM, Paris, France
| | | | | | - Bruno Stankoff
- Hôpital Pitié-Salpêtrière, ICM, Sorbonne Université, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - Stéphane Lehericy
- Center for Neuroimaging Research-CENIR, Paris Brain Institute (Institut du Cerveau-ICM), Paris, France
- Hôpital Pitié-Salpêtrière, ICM, Sorbonne Université, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - Alexandra Petiet
- Center for Neuroimaging Research-CENIR, Paris Brain Institute (Institut du Cerveau-ICM), Paris, France
- Hôpital Pitié-Salpêtrière, ICM, Sorbonne Université, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - Francesca Branzoli
- Center for Neuroimaging Research-CENIR, Paris Brain Institute (Institut du Cerveau-ICM), Paris, France
- Hôpital Pitié-Salpêtrière, ICM, Sorbonne Université, Inserm U 1127, CNRS UMR 7225, Paris, France
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28
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Buizza G, Paganelli C, Ballati F, Sacco S, Preda L, Iannalfi A, Alexander DC, Baroni G, Palombo M. Improving the characterization of meningioma microstructure in proton therapy from conventional apparent diffusion coefficient measurements using Monte Carlo simulations of diffusion MRI. Med Phys 2021; 48:1250-1261. [PMID: 33369744 DOI: 10.1002/mp.14689] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 11/08/2020] [Accepted: 12/17/2020] [Indexed: 12/29/2022] Open
Abstract
PURPOSE Proton therapy could benefit from noninvasively gaining tumor microstructure information, at both planning and monitoring stages. The anatomical location of brain tumors, such as meningiomas, often hinders the recovery of such information from histopathology, and conventional noninvasive imaging biomarkers, like the apparent diffusion coefficient (ADC) from diffusion-weighted MRI (DW-MRI), are nonspecific. The aim of this study was to retrieve discriminative microstructural markers from conventional ADC for meningiomas treated with proton therapy. These markers were employed for tumor grading and tumor response assessment. METHODS DW-MRIs from patients affected by meningioma and enrolled in proton therapy were collected before (n = 35) and 3 months after (n = 25) treatment. For the latter group, the risk of an adverse outcome was inferred by their clinical history. Using Monte Carlo methods, DW-MRI signals were simulated from packings of synthetic cells built with well-defined geometrical and diffusion properties. Patients' ADC was modeled as a weighted sum of selected simulated signals. The weights that best described a patient's ADC were determined through an optimization procedure and used to estimate a set of markers of tumor microstructure: diffusion coefficient (D), volume fraction (vf), and radius (R). Apparent cellularity (ρapp ) was estimated from vf and R for an easier clinical interpretability. Differences between meningothelial and atypical subtypes, and low- and high-grade meningiomas were assessed with nonparametric statistical tests, whereas sensitivity and specificity with ROC analyses. Similar analyses were performed for patients showing low or high risk of an adverse outcome to preliminary evaluate response to treatment. RESULTS Significant (P < 0.05) differences in median ADC, D, vf, R, and ρapp values were found when comparing meningiomas' subtypes and grades. ROC analyses showed that estimated microstructural parameters reached higher specificity than ADC for subtyping (0.93 for D and vf vs 0.80 for ADC) and grading (0.75 for R vs 0.67 for ADC). High- and low-risk patients showed significant differences in ADC and microstructural parameters. The skewness of ρapp was the parameter with highest AUC (0.90) and sensitivity (0.75). CONCLUSIONS Matching measured with simulated ADC yielded a set of potential imaging markers for meningiomas grading and response monitoring in proton therapy, showing higher specificity than conventional ADC. These markers can provide discriminative information about spatial patterns of tumor microstructure implying important advantages for patient-specific proton therapy workflows.
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Affiliation(s)
- Giulia Buizza
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, 20133, Italy
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, 20133, Italy
| | - Francesco Ballati
- Diagnostic Radiology Residency School, University of Pavia, Pavia, 27100, Italy
| | - Simone Sacco
- Diagnostic Radiology Residency School, University of Pavia, Pavia, 27100, Italy
| | - Lorenzo Preda
- Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, 27100, Italy
| | - Alberto Iannalfi
- Clinical Department, National Center of Oncological Hadrontherapy (CNAO), Pavia, 27100, Italy
| | - Daniel C Alexander
- Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London (UCL), London, WC1V6LJ, UK
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, 20133, Italy.,Bioengineering Unit, National Center of Oncological Hadrontherapy (CNAO), Pavia, 27100, Italy
| | - Marco Palombo
- Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London (UCL), London, WC1V6LJ, UK
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29
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Palombo M, Ianus A, Guerreri M, Nunes D, Alexander DC, Shemesh N, Zhang H. Corrigendum to "SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI" [Neuroimage 215 (2020), 116835]. Neuroimage 2020; 226:117612. [PMID: 33291021 PMCID: PMC8636689 DOI: 10.1016/j.neuroimage.2020.117612] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Affiliation(s)
- Marco Palombo
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK.
| | - Andrada Ianus
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK; Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Michele Guerreri
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK
| | - Daniel Nunes
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Daniel C Alexander
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Hui Zhang
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK
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30
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Henriques RN, Palombo M, Jespersen SN, Shemesh N, Lundell H, Ianuş A. Double diffusion encoding and applications for biomedical imaging. J Neurosci Methods 2020; 348:108989. [PMID: 33144100 DOI: 10.1016/j.jneumeth.2020.108989] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 09/25/2020] [Accepted: 10/20/2020] [Indexed: 12/11/2022]
Abstract
Diffusion Magnetic Resonance Imaging (dMRI) is one of the most important contemporary non-invasive modalities for probing tissue structure at the microscopic scale. The majority of dMRI techniques employ standard single diffusion encoding (SDE) measurements, covering different sequence parameter ranges depending on the complexity of the method. Although many signal representations and biophysical models have been proposed for SDE data, they are intrinsically limited by a lack of specificity. Advanced dMRI methods have been proposed to provide additional microstructural information beyond what can be inferred from SDE. These enhanced contrasts can play important roles in characterizing biological tissues, for instance upon diseases (e.g. neurodegenerative, cancer, stroke), aging, learning, and development. In this review we focus on double diffusion encoding (DDE), which stands out among other advanced acquisitions for its versatility, ability to probe more specific diffusion correlations, and feasibility for preclinical and clinical applications. Various DDE methodologies have been employed to probe compartment sizes (Section 3), decouple the effects of microscopic diffusion anisotropy from orientation dispersion (Section 4), probe displacement correlations, study exchange, or suppress fast diffusing compartments (Section 6). DDE measurements can also be used to improve the robustness of biophysical models (Section 5) and study intra-cellular diffusion via magnetic resonance spectroscopy of metabolites (Section 7). This review discusses all these topics as well as important practical aspects related to the implementation and contrast in preclinical and clinical settings (Section 9) and aims to provide the readers a guide for deciding on the right DDE acquisition for their specific application.
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Affiliation(s)
- Rafael N Henriques
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Marco Palombo
- Centre for Medical Image Computing and Dept. of Computer Science, University College London, London, UK
| | - 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
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Denmark
| | - Andrada Ianuş
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal.
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31
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Hill I, Palombo M, Santin M, Branzoli F, Philippe AC, Wassermann D, Aigrot MS, Stankoff B, Baron-Van Evercooren A, Felfli M, Langui D, Zhang H, Lehericy S, Petiet A, Alexander DC, Ciccarelli O, Drobnjak I. Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelination. Neuroimage 2020; 224:117425. [PMID: 33035669 DOI: 10.1016/j.neuroimage.2020.117425] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 09/29/2020] [Accepted: 09/30/2020] [Indexed: 01/14/2023] Open
Abstract
The intra-axonal water exchange time (τi), a parameter associated with axonal permeability, could be an important biomarker for understanding and treating demyelinating pathologies such as Multiple Sclerosis. Diffusion-Weighted MRI (DW-MRI) is sensitive to changes in permeability; however, the parameter has so far remained elusive due to the lack of general biophysical models that incorporate it. Machine learning based computational models can potentially be used to estimate such parameters. Recently, for the first time, a theoretical framework using a random forest (RF) regressor suggests that this is a promising new approach for permeability estimation. In this study, we adopt such an approach and for the first time experimentally investigate it for demyelinating pathologies through direct comparison with histology. We construct a computational model using Monte Carlo simulations and an RF regressor in order to learn a mapping between features derived from DW-MRI signals and ground truth microstructure parameters. We test our model in simulations, and find strong correlations between the predicted and ground truth parameters (intra-axonal volume fraction f: R2 =0.99, τi: R2 =0.84, intrinsic diffusivity d: R2 =0.99). We then apply the model in-vivo, on a controlled cuprizone (CPZ) mouse model of demyelination, comparing the results from two cohorts of mice, CPZ (N=8) and healthy age-matched wild-type (WT, N=8). We find that the RF model estimates sensible microstructure parameters for both groups, matching values found in literature. Furthermore, we perform histology for both groups using electron microscopy (EM), measuring the thickness of the myelin sheath as a surrogate for exchange time. Histology results show that our RF model estimates are very strongly correlated with the EM measurements (ρ = 0.98 for f, ρ = 0.82 for τi). Finally, we find a statistically significant decrease in τi in all three regions of the corpus callosum (splenium/genu/body) of the CPZ cohort (<τi>=310ms/330ms/350ms) compared to the WT group (<τi>=370ms/370ms/380ms). This is in line with our expectations that τi is lower in regions where the myelin sheath is damaged, as axonal membranes become more permeable. Overall, these results demonstrate, for the first time experimentally and in vivo, that a computational model learned from simulations can reliably estimate microstructure parameters, including the axonal permeability .
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Affiliation(s)
- Ioana Hill
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK
| | - Marco Palombo
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK.
| | - Mathieu Santin
- Institut du Cerveau et de la Moelle épinière, ICM, Sorbonne Université, Inserm 1127, CNRS UMR 7225, F-75013, Paris, France; Institut du Cerveau et de la Moelle épinière, ICM, Centre de NeuroImagerie de Recherche, CENIR, Paris, France
| | - Francesca Branzoli
- Institut du Cerveau et de la Moelle épinière, ICM, Sorbonne Université, Inserm 1127, CNRS UMR 7225, F-75013, Paris, France; Institut du Cerveau et de la Moelle épinière, ICM, Centre de NeuroImagerie de Recherche, CENIR, Paris, France
| | - Anne-Charlotte Philippe
- Institut du Cerveau et de la Moelle épinière, ICM, Sorbonne Université, Inserm 1127, CNRS UMR 7225, F-75013, Paris, France
| | - Demian Wassermann
- Université Côte d'Azur, Inria, Sophia-Antipolis, France; Parietal, CEA, Inria, Saclay, Île-de-France
| | - Marie-Stephane Aigrot
- Institut du Cerveau et de la Moelle épinière, ICM, Sorbonne Université, Inserm 1127, CNRS UMR 7225, F-75013, Paris, France
| | - Bruno Stankoff
- Institut du Cerveau et de la Moelle épinière, ICM, Sorbonne Université, Inserm 1127, CNRS UMR 7225, F-75013, Paris, France; AP-HP, Hôpital Saint-Antoine, Paris, France
| | - Anne Baron-Van Evercooren
- Institut du Cerveau et de la Moelle épinière, ICM, Sorbonne Université, Inserm 1127, CNRS UMR 7225, F-75013, Paris, France
| | - Mehdi Felfli
- Institut du Cerveau et de la Moelle épinière, ICM, Sorbonne Université, Inserm 1127, CNRS UMR 7225, F-75013, Paris, France
| | - Dominique Langui
- Institut du Cerveau et de la Moelle épinière, ICM, Sorbonne Université, Inserm 1127, CNRS UMR 7225, F-75013, Paris, France
| | - Hui Zhang
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK
| | - Stephane Lehericy
- Institut du Cerveau et de la Moelle épinière, ICM, Sorbonne Université, Inserm 1127, CNRS UMR 7225, F-75013, Paris, France; Institut du Cerveau et de la Moelle épinière, ICM, Centre de NeuroImagerie de Recherche, CENIR, Paris, France
| | - Alexandra Petiet
- Institut du Cerveau et de la Moelle épinière, ICM, Sorbonne Université, Inserm 1127, CNRS UMR 7225, F-75013, Paris, France; Institut du Cerveau et de la Moelle épinière, ICM, Centre de NeuroImagerie de Recherche, CENIR, Paris, France
| | - Daniel C Alexander
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK
| | - Olga Ciccarelli
- Dept. of Neuroinflammation, University College London, Queen Square Institute of Neurology, University College London, London, UK
| | - Ivana Drobnjak
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK
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32
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Jelescu IO, Palombo M, Bagnato F, Schilling KG. Challenges for biophysical modeling of microstructure. J Neurosci Methods 2020; 344:108861. [PMID: 32692999 PMCID: PMC10163379 DOI: 10.1016/j.jneumeth.2020.108861] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/10/2020] [Accepted: 07/14/2020] [Indexed: 02/07/2023]
Abstract
The biophysical modeling efforts in diffusion MRI have grown considerably over the past 25 years. In this review, we dwell on the various challenges along the journey of bringing a biophysical model from initial design to clinical implementation, identifying both hurdles that have been already overcome and outstanding issues. First, we describe the critical initial task of selecting which features of tissue microstructure can be estimated using a model and which acquisition protocol needs to be implemented to make the estimation possible. The model performance should necessarily be tested in realistic numerical simulations and in experimental data - adapting the fitting strategy accordingly, and parameter estimates should be validated against complementary techniques, when/if available. Secondly, the model performance and validity should be explored in pathological conditions, and, if appropriate, dedicated models for pathology should be developed. We build on examples from tumors, ischemia and demyelinating diseases. We then discuss the challenges associated with clinical translation and added value. Finally, we single out four major unresolved challenges that are related to: the availability of a microstructural ground truth, the validation of model parameters which cannot be accessed with complementary techniques, the development of a generalized standard model for any brain region and pathology, and the seamless communication between different parties involved in the development and application of biophysical models of diffusion.
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Ning L, Bonet-Carne E, Grussu F, Sepehrband F, Kaden E, Veraart J, Blumberg SB, Khoo CS, Palombo M, Kokkinos I, Alexander DC, Coll-Font J, Scherrer B, Warfield SK, Karayumak SC, Rathi Y, Koppers S, Weninger L, Ebert J, Merhof D, Moyer D, Pietsch M, Christiaens D, Gomes Teixeira RA, Tournier JD, Schilling KG, Huo Y, Nath V, Hansen C, Blaber J, Landman BA, Zhylka A, Pluim JPW, Parker G, Rudrapatna U, Evans J, Charron C, Jones DK, Tax CMW. Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: Algorithms and results. Neuroimage 2020; 221:117128. [PMID: 32673745 DOI: 10.1016/j.neuroimage.2020.117128] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 06/26/2020] [Accepted: 06/29/2020] [Indexed: 01/26/2023] Open
Abstract
Cross-scanner and cross-protocol variability of diffusion magnetic resonance imaging (dMRI) data are known to be major obstacles in multi-site clinical studies since they limit the ability to aggregate dMRI data and derived measures. Computational algorithms that harmonize the data and minimize such variability are critical to reliably combine datasets acquired from different scanners and/or protocols, thus improving the statistical power and sensitivity of multi-site studies. Different computational approaches have been proposed to harmonize diffusion MRI data or remove scanner-specific differences. To date, these methods have mostly been developed for or evaluated on single b-value diffusion MRI data. In this work, we present the evaluation results of 19 algorithms that are developed to harmonize the cross-scanner and cross-protocol variability of multi-shell diffusion MRI using a benchmark database. The proposed algorithms rely on various signal representation approaches and computational tools, such as rotational invariant spherical harmonics, deep neural networks and hybrid biophysical and statistical approaches. The benchmark database consists of data acquired from the same subjects on two scanners with different maximum gradient strength (80 and 300 mT/m) and with two protocols. We evaluated the performance of these algorithms for mapping multi-shell diffusion MRI data across scanners and across protocols using several state-of-the-art imaging measures. The results show that data harmonization algorithms can reduce the cross-scanner and cross-protocol variabilities to a similar level as scan-rescan variability using the same scanner and protocol. In particular, the LinearRISH algorithm based on adaptive linear mapping of rotational invariant spherical harmonics features yields the lowest variability for our data in predicting the fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK) and the rotationally invariant spherical harmonic (RISH) features. But other algorithms, such as DIAMOND, SHResNet, DIQT, CMResNet show further improvement in harmonizing the return-to-origin probability (RTOP). The performance of different approaches provides useful guidelines on data harmonization in future multi-site studies.
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Affiliation(s)
- Lipeng Ning
- Brigham and Women's Hospital, Boston, United States; Harvard Medical School, Boston, United States.
| | | | | | - Farshid Sepehrband
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, United States
| | - Enrico Kaden
- University College London, London, United Kingdom
| | | | | | - Can Son Khoo
- University College London, London, United Kingdom
| | | | | | | | - Jaume Coll-Font
- Boston Children's Hospital, Boston, United States; Harvard Medical School, Boston, United States
| | - Benoit Scherrer
- Boston Children's Hospital, Boston, United States; Harvard Medical School, Boston, United States
| | - Simon K Warfield
- Boston Children's Hospital, Boston, United States; Harvard Medical School, Boston, United States
| | - Suheyla Cetin Karayumak
- Brigham and Women's Hospital, Boston, United States; Harvard Medical School, Boston, United States
| | - Yogesh Rathi
- Brigham and Women's Hospital, Boston, United States; Harvard Medical School, Boston, United States
| | | | | | | | | | - Daniel Moyer
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, United States
| | - Maximilian Pietsch
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Electrical Engineering (ESAT/PSI), KU Leuven, Leuven, Belgium
| | - Rui Azeredo Gomes Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Kurt G Schilling
- Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States
| | - Yuankai Huo
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Vishwesh Nath
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Colin Hansen
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Justin Blaber
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Bennett A Landman
- Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States; Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Andrey Zhylka
- Eindhoven University of Technology, Eindhoven, Netherlands
| | | | - Greg Parker
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - John Evans
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Cyril Charron
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom; School of Psychology, Australian Catholic University, Melbourne, Australia
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
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Palombo M, Ianus A, Guerreri M, Nunes D, Alexander DC, Shemesh N, Zhang H. SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI. Neuroimage 2020; 215:116835. [PMID: 32289460 PMCID: PMC8543044 DOI: 10.1016/j.neuroimage.2020.116835] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 03/26/2020] [Accepted: 04/06/2020] [Indexed: 11/29/2022] Open
Abstract
This work introduces a compartment-based model for apparent cell body (namely soma) and neurite density imaging (SANDI) using non-invasive diffusion-weighted MRI (DW-MRI). The existing conjecture in brain microstructure imaging through DW-MRI presents water diffusion in white (WM) and gray (GM) matter as restricted diffusion in neurites, modelled by infinite cylinders of null radius embedded in the hindered extra-neurite water. The extra-neurite pool in WM corresponds to water in the extra-axonal space, but in GM it combines water in the extra-cellular space with water in soma. While several studies showed that this microstructure model successfully describe DW-MRI data in WM and GM at b ≤ 3,000 s/mm2 (or 3 ms/μm2), it has been also shown to fail in GM at high b values (b≫3,000 s/mm2 or 3 ms/μm2). Here we hypothesise that the unmodelled soma compartment (i.e. cell body of any brain cell type: from neuroglia to neurons) may be responsible for this failure and propose SANDI as a new model of brain microstructure where soma of any brain cell type is explicitly included. We assess the effects of size and density of soma on the direction-averaged DW-MRI signal at high b values and the regime of validity of the model using numerical simulations and comparison with experimental data from mouse (bmax = 40,000 s/mm2, or 40 ms/μm2) and human (bmax = 10,000 s/mm2, or 10 ms/μm2) brain. We show that SANDI defines new contrasts representing complementary information on the brain cyto- and myelo-architecture. Indeed, we show maps from 25 healthy human subjects of MR soma and neurite signal fractions, that remarkably mirror contrasts of histological images of brain cyto- and myelo-architecture. Although still under validation, SANDI might provide new insight into tissue architecture by introducing a new set of biomarkers of potential great value for biomedical applications and pure neuroscience.
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Affiliation(s)
- Marco Palombo
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK.
| | - Andrada Ianus
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK; Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Michele Guerreri
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK
| | - Daniel Nunes
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Daniel C Alexander
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Hui Zhang
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK
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35
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Vincent M, Palombo M, Valette J. Revisiting double diffusion encoding MRS in the mouse brain at 11.7T: Which microstructural features are we sensitive to? Neuroimage 2020; 207:116399. [PMID: 31778817 PMCID: PMC7014823 DOI: 10.1016/j.neuroimage.2019.116399] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 10/25/2019] [Accepted: 11/22/2019] [Indexed: 11/19/2022] Open
Abstract
Brain metabolites, such as N-acetylaspartate or myo-inositol, are constantly probing their local cellular environment under the effect of diffusion. Diffusion-weighted NMR spectroscopy therefore presents unparalleled potential to yield cell-type specific microstructural information. Double diffusion encoding (DDE) consists in applying two diffusion blocks, where gradient's direction in the second block is varied during the course of the experiment. Unlike single diffusion encoding, DDE measurements at long mixing time display some angular modulation of the signal amplitude which reflects microscopic anisotropy (μA), while requiring relatively low gradient strength. This angular dependence has been formerly used to quantify cell fiber diameter using a model of isotropically oriented infinite cylinders. However, how additional features of the cell microstructure (such as cell body diameter, fiber length and branching) may also influence the DDE signal has been little explored. Here, we used a cryoprobe as well as state-of-the-art post-processing to perform DDE acquisitions with high accuracy and precision in the mouse brain at 11.7 T. We then compared our results to simulated DDE datasets obtained in various 3D cell models in order to pinpoint which features of cell morphology may influence the most the angular dependence of the DDE signal. While the infinite cylinder model poorly fits our experimental data, we show that incorporating branched fiber structure in our model allows more realistic interpretation of the DDE signal. Lastly, data acquired in the short mixing time regime suggest that some sensitivity to cell body diameter might be retrieved, although additional experiments would be required to further support this statement.
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Affiliation(s)
- Mélissa Vincent
- Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), MIRCen, F-92260, Fontenay-aux-Roses, France; Neurodegenerative Diseases Laboratory, UMR9199, CEA, CNRS, Université Paris Sud, Université Paris-Saclay, F-92260, Fontenay-aux-Roses, France
| | - Marco Palombo
- Department of Computer Science and Centre for Medical Image Computing, University College of London, London, WC1E 6BT, United Kingdom
| | - Julien Valette
- Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), MIRCen, F-92260, Fontenay-aux-Roses, France; Neurodegenerative Diseases Laboratory, UMR9199, CEA, CNRS, Université Paris Sud, Université Paris-Saclay, F-92260, Fontenay-aux-Roses, France.
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36
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Pizzolato M, Palombo M, Bonet-Carne E, Tax CMW, Grussu F, Ianus A, Bogusz F, Pieciak T, Ning L, Larochelle H, Descoteaux M, Chamberland M, Blumberg SB, Mertzanidou T, Alexander DC, Afzali M, Aja-Fernández S, Jones DK, Westin CF, Rathi Y, Baete SH, Cordero-Grande L, Ladner T, Slator PJ, Hajnal JV, Thiran JP, Price AN, Sepehrband F, Zhang F, Hutter J. Acquiring and Predicting Multidimensional Diffusion (MUDI) Data: An Open Challenge. Computational Diffusion MRI 2020. [DOI: 10.1007/978-3-030-52893-5_17] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
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37
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Callaghan R, Alexander DC, Zhang H, Palombo M. Contextual Fibre Growth to Generate Realistic Axonal Packing for Diffusion MRI Simulation. ACTA ACUST UNITED AC 2019. [DOI: 10.1007/978-3-030-20351-1_33] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
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38
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Slator PJ, Hutter J, Palombo M, Jackson LH, Ho A, Panagiotaki E, Chappell LC, Rutherford MA, Hajnal JV, Alexander DC. Combined diffusion-relaxometry MRI to identify dysfunction in the human placenta. Magn Reson Med 2019; 82:95-106. [PMID: 30883915 PMCID: PMC6519240 DOI: 10.1002/mrm.27733] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 01/04/2019] [Accepted: 01/27/2019] [Indexed: 12/25/2022]
Abstract
Purpose A combined diffusion‐relaxometry MR acquisition and analysis pipeline for in vivo human placenta, which allows for exploration of coupling between T2* and apparent diffusion coefficient (ADC) measurements in a sub 10‐minute scan time. Methods We present a novel acquisition combining a diffusion prepared spin echo with subsequent gradient echoes. The placentas of 17 pregnant women were scanned in vivo, including both healthy controls and participants with various pregnancy complications. We estimate the joint T2*‐ADC spectra using an inverse Laplace transform. Results T2*‐ADC spectra demonstrate clear quantitative separation between normal and dysfunctional placentas. Conclusions Combined T2*‐diffusivity MRI is promising for assessing fetal and maternal health during pregnancy. The T2*‐ADC spectrum potentially provides additional information on tissue microstructure, compared to measuring these two contrasts separately. The presented method is immediately applicable to the study of other organs.
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Affiliation(s)
- Paddy J Slator
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom
| | - Jana Hutter
- Biomedical Engineering Department, King's College London, London, United Kingdom.,Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Marco Palombo
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom
| | - Laurence H Jackson
- Biomedical Engineering Department, King's College London, London, United Kingdom.,Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Alison Ho
- Women's Health Department, King's College London, London, United Kingdom
| | - Eleftheria Panagiotaki
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom
| | - Lucy C Chappell
- Women's Health Department, King's College London, London, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Biomedical Engineering Department, King's College London, London, United Kingdom.,Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Daniel C Alexander
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom
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Palombo M, Alexander DC, Zhang H. A generative model of realistic brain cells with application to numerical simulation of the diffusion-weighted MR signal. Neuroimage 2019; 188:391-402. [DOI: 10.1016/j.neuroimage.2018.12.025] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 11/19/2018] [Accepted: 12/11/2018] [Indexed: 10/27/2022] Open
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40
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Slator PJ, Hutter J, Marinescu RV, Palombo M, Young AL, Jackson LH, Ho A, Chappell LC, Rutherford M, Hajnal JV, Alexander DC. InSpect: INtegrated SPECTral Component Estimation and Mapping for Multi-contrast Microstructural MRI. Lecture Notes in Computer Science 2019. [DOI: 10.1007/978-3-030-20351-1_59] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Guerreri M, Palombo M, Caporale A, Fasano F, Macaluso E, Bozzali M, Capuani S. Age-related microstructural and physiological changes in normal brain measured by MRI γ-metrics derived from anomalous diffusion signal representation. Neuroimage 2018; 188:654-667. [PMID: 30583064 DOI: 10.1016/j.neuroimage.2018.12.044] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2018] [Revised: 12/11/2018] [Accepted: 12/20/2018] [Indexed: 12/29/2022] Open
Abstract
Nowadays, increasing longevity associated with declining cerebral nervous system functions, suggests the need for continued development of new imaging contrast mechanisms to support the differential diagnosis of age-related decline. In our previous papers, we developed a new imaging contrast metrics derived from anomalous diffusion signal representation and obtained from diffusion-weighted (DW) data collected by varying diffusion gradient strengths. Recently, we highlighted that the new metrics, named γ-metrics, depended on the local inhomogeneity due to differences in magnetic susceptibility between tissues and diffusion compartments in young healthy subjects, thus providing information about myelin orientation and iron content within cerebral regions. The major structural modifications occurring in brain aging are myelinated fibers damage in nerve fibers and iron accumulation in gray matter nuclei. Therefore, we investigated the potential of γ-metrics in relation to other conventional diffusion metrics such as DTI, DKI and NODDI in detecting age-related structural changes in white matter (WM) and subcortical gray matter (scGM). DW-images were acquired in 32 healthy subjects, adults and elderly (age range 20-77 years) using 3.0T and 12 b-values up to 5000 s/mm2. Association between diffusion metrics and subjects' age was assessed using linear regression. A decline in mean γ (Mγ) in the scGM and a complementary increase in radial γ (γ⊥) in frontal WM, genu of corpus callosum and anterior corona radiata with advancing age were found. We suggested that the increase in γ⊥ might reflect declined myelin density, and Mγ decrease might mirror iron accumulation. An increase in D// and a decrease in the orientation dispersion index (ODI) were associated with axonal loss in the pyramidal tracts, while their inverted trends within the thalamus were thought to be linked to reduced architectural complexity of nerve fibers. γ-metrics together with conventional diffusion-metrics can more comprehensively characterize the complex mechanisms underlining age-related changes than conventional diffusion techniques alone.
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Affiliation(s)
- Michele Guerreri
- SAIMLAL Department, Sapienza, Piazzale Aldo Moro, 5, 00185, Roma, RM, Italy; Institute for Complex Systems, CNR, Rome, Italy.
| | - Marco Palombo
- Institute for Complex Systems, CNR, Rome, Italy; Department of Computer Science & Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Alessandra Caporale
- Institute for Complex Systems, CNR, Rome, Italy; Laboratory for Structural, Physiologic and Functional Imaging, Perelman School of Medicine University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Marco Bozzali
- Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy
| | - Silvia Capuani
- Institute for Complex Systems, CNR, Rome, Italy; Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy
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Jones DK, Alexander DC, Bowtell R, Cercignani M, Dell'Acqua F, McHugh DJ, Miller KL, Palombo M, Parker GJM, Rudrapatna US, Tax CMW. Microstructural imaging of the human brain with a 'super-scanner': 10 key advantages of ultra-strong gradients for diffusion MRI. Neuroimage 2018; 182:8-38. [PMID: 29793061 DOI: 10.1016/j.neuroimage.2018.05.047] [Citation(s) in RCA: 105] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Revised: 05/17/2018] [Accepted: 05/18/2018] [Indexed: 12/13/2022] Open
Abstract
The key component of a microstructural diffusion MRI 'super-scanner' is a dedicated high-strength gradient system that enables stronger diffusion weightings per unit time compared to conventional gradient designs. This can, in turn, drastically shorten the time needed for diffusion encoding, increase the signal-to-noise ratio, and facilitate measurements at shorter diffusion times. This review, written from the perspective of the UK National Facility for In Vivo MR Imaging of Human Tissue Microstructure, an initiative to establish a shared 300 mT/m-gradient facility amongst the microstructural imaging community, describes ten advantages of ultra-strong gradients for microstructural imaging. Specifically, we will discuss how the increase of the accessible measurement space compared to a lower-gradient systems (in terms of Δ, b-value, and TE) can accelerate developments in the areas of 1) axon diameter distribution mapping; 2) microstructural parameter estimation; 3) mapping micro-vs macroscopic anisotropy features with gradient waveforms beyond a single pair of pulsed-gradients; 4) multi-contrast experiments, e.g. diffusion-relaxometry; 5) tractography and high-resolution imaging in vivo and 6) post mortem; 7) diffusion-weighted spectroscopy of metabolites other than water; 8) tumour characterisation; 9) functional diffusion MRI; and 10) quality enhancement of images acquired on lower-gradient systems. We finally discuss practical barriers in the use of ultra-strong gradients, and provide an outlook on the next generation of 'super-scanners'.
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Affiliation(s)
- D K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK; School of Psychology, Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, 3065, Australia.
| | - D C Alexander
- Centre for Medical Image Computing (CMIC), Department of Computer Science, UCL (University College London), Gower Street, London, UK; Clinical Imaging Research Centre, National University of Singapore, Singapore
| | - R Bowtell
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - M Cercignani
- Department of Psychiatry, Brighton and Sussex Medical School, Brighton, UK
| | - F Dell'Acqua
- Natbrainlab, Department of Neuroimaging, King's College London, London, UK
| | - D J McHugh
- Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK; CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge and Manchester, UK
| | - K L Miller
- Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK
| | - M Palombo
- Centre for Medical Image Computing (CMIC), Department of Computer Science, UCL (University College London), Gower Street, London, UK
| | - G J M Parker
- Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK; CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge and Manchester, UK; Bioxydyn Ltd., Manchester, UK
| | - U S Rudrapatna
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - C M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
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Palombo M, Shemesh N, Ronen I, Valette J. Insights into brain microstructure from in vivo DW-MRS. Neuroimage 2018; 182:97-116. [DOI: 10.1016/j.neuroimage.2017.11.028] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2017] [Revised: 10/09/2017] [Accepted: 11/15/2017] [Indexed: 12/27/2022] Open
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Valette J, Ligneul C, Marchadour C, Najac C, Palombo M. Brain Metabolite Diffusion from Ultra-Short to Ultra-Long Time Scales: What Do We Learn, Where Should We Go? Front Neurosci 2018; 12:2. [PMID: 29403347 PMCID: PMC5780428 DOI: 10.3389/fnins.2018.00002] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Accepted: 01/03/2018] [Indexed: 11/13/2022] Open
Abstract
In vivo diffusion-weighted MR spectroscopy (DW-MRS) allows measuring diffusion properties of brain metabolites. Unlike water, most metabolites are confined within cells. Hence, their diffusion is expected to purely reflect intracellular properties, opening unique possibilities to use metabolites as specific probes to explore cellular organization and structure. However, interpretation and modeling of DW-MRS, and more generally of intracellular diffusion, remains difficult. In this perspective paper, we will focus on the study of the time-dependency of brain metabolite apparent diffusion coefficient (ADC). We will see how measuring ADC over several orders of magnitude of diffusion times, from less than 1 ms to more than 1 s, allows clarifying our understanding of brain metabolite diffusion, by firmly establishing that metabolites are neither massively transported by active mechanisms nor massively confined in subcellular compartments or cell bodies. Metabolites appear to be instead diffusing in long fibers typical of neurons and glial cells such as astrocytes. Furthermore, we will evoke modeling of ADC time-dependency to evaluate the effect of, and possibly quantify, some structural parameters at various spatial scales, departing from a simple model of hollow cylinders and introducing additional complexity, either short-ranged (such as dendritic spines) or long-ranged (such as cellular fibers ramification). Finally, we will discuss the experimental feasibility and expected benefits of extending the range of diffusion times toward even shorter and longer values.
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Affiliation(s)
- Julien Valette
- Commissariat à l'Energie Atomique et aux Energies Alternatives, MIRCen, Fontenay-aux-Roses, France.,Neurodegenerative Diseases Laboratory, Centre National de la Recherche Scientifique, Université Paris-Sud, Université Paris-Saclay, UMR 9199, Fontenay-aux-Roses, France
| | - Clémence Ligneul
- Commissariat à l'Energie Atomique et aux Energies Alternatives, MIRCen, Fontenay-aux-Roses, France.,Neurodegenerative Diseases Laboratory, Centre National de la Recherche Scientifique, Université Paris-Sud, Université Paris-Saclay, UMR 9199, Fontenay-aux-Roses, France
| | - Charlotte Marchadour
- Commissariat à l'Energie Atomique et aux Energies Alternatives, MIRCen, Fontenay-aux-Roses, France.,Neurodegenerative Diseases Laboratory, Centre National de la Recherche Scientifique, Université Paris-Sud, Université Paris-Saclay, UMR 9199, Fontenay-aux-Roses, France
| | - Chloé Najac
- Commissariat à l'Energie Atomique et aux Energies Alternatives, MIRCen, Fontenay-aux-Roses, France.,Neurodegenerative Diseases Laboratory, Centre National de la Recherche Scientifique, Université Paris-Sud, Université Paris-Saclay, UMR 9199, Fontenay-aux-Roses, France
| | - Marco Palombo
- Department of Computer Science and Centre for Medical Image Computing, University College of London, London, United Kingdom
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Sinibaldi R, Conti A, Sinjari B, Spadone S, Pecci R, Palombo M, Komlev VS, Ortore MG, Tromba G, Capuani S, Guidotti R, De Luca F, Caputi S, Traini T, Della Penna S. Multimodal-3D imaging based on μMRI and μCT techniques bridges the gap with histology in visualization of the bone regeneration process. J Tissue Eng Regen Med 2017; 12:750-761. [PMID: 28593731 DOI: 10.1002/term.2494] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Revised: 04/23/2017] [Accepted: 06/05/2017] [Indexed: 01/05/2023]
Abstract
Bone repair/regeneration is usually investigated through X-ray computed microtomography (μCT) supported by histology of extracted samples, to analyse biomaterial structure and new bone formation processes. Magnetic resonance imaging (μMRI) shows a richer tissue contrast than μCT, despite at lower resolution, and could be combined with μCT in the perspective of conducting non-destructive 3D investigations of bone. A pipeline designed to combine μMRI and μCT images of bone samples is here described and applied on samples of extracted human jawbone core following bone graft. We optimized the coregistration procedure between μCT and μMRI images to avoid bias due to the different resolutions and contrasts. Furthermore, we used an Adaptive Multivariate Clustering, grouping homologous voxels in the coregistered images, to visualize different tissue types within a fused 3D metastructure. The tissue grouping matched the 2D histology applied only on 1 slice, thus extending the histology labelling in 3D. Specifically, in all samples, we could separate and map 2 types of regenerated bone, calcified tissue, soft tissues, and/or fat and marrow space. Remarkably, μMRI and μCT alone were not able to separate the 2 types of regenerated bone. Finally, we computed volumes of each tissue in the 3D metastructures, which might be exploited by quantitative simulation. The 3D metastructure obtained through our pipeline represents a first step to bridge the gap between the quality of information obtained from 2D optical microscopy and the 3D mapping of the bone tissue heterogeneity and could allow researchers and clinicians to non-destructively characterize and follow-up bone regeneration.
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Affiliation(s)
- R Sinibaldi
- Department of Neuroscience, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
- Multimodal3D s.r.l., Rome, Italy
| | - A Conti
- Department of Neuroscience, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - B Sinjari
- Department of Medical and Oral Sciences and Biotechnologies, G. D'Annunzio University of Chieti and Pescara, Chieti, Italy
| | - S Spadone
- Department of Neuroscience, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - R Pecci
- Department of Technologies and Health, Istituto Superiore di Sanità, Rome, Italy
| | - M Palombo
- Department of Physics, Sapienza University of Rome, Rome, Italy
- CEA/DSV/I2BM, MIRCen, Fontenay-aux-Roses, France
| | - V S Komlev
- A.A. Baikov Institute of Metallurgy and Materials Science, Russian Academy of Sciences, Moscow, Russian Federation
| | - M G Ortore
- Department of Life and Environmental Science, Marche Polytechnic University, Ancona, Italy
| | - G Tromba
- Elettra Sincrotrone Trieste, Trieste, Italy
| | - S Capuani
- CNR (Institute for Complex Systems) c/o Physics Department Sapienza University of Rome, Rome, Italy
| | - R Guidotti
- Department of Neuroscience, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - F De Luca
- Department of Physics, Sapienza University of Rome, Rome, Italy
| | - S Caputi
- Department of Medical and Oral Sciences and Biotechnologies, G. D'Annunzio University of Chieti and Pescara, Chieti, Italy
| | - T Traini
- Department of Medical and Oral Sciences and Biotechnologies, G. D'Annunzio University of Chieti and Pescara, Chieti, Italy
| | - S Della Penna
- Department of Neuroscience, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
- Institute for Advanced Biomedical Technologies, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
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Morganti P, Fusco A, Paoletti I, Perfetto B, Del Ciotto P, Palombo M, Chianese A, Baroni A, Donnarumma G. Anti-Inflammatory, Immunomodulatory, and Tissue Repair Activity on Human Keratinocytes by Green Innovative Nanocomposites. Materials (Basel) 2017; 10:ma10070843. [PMID: 28773202 PMCID: PMC5551885 DOI: 10.3390/ma10070843] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Revised: 07/10/2017] [Accepted: 07/18/2017] [Indexed: 11/16/2022]
Abstract
The use of raw materials obtained by waste and processed through innovative industrial methodologies has generated an industry of about a trillion dollars in a short time, and in the near future will provide resources and services for the conservation and sustainable use of natural resources in order to ensure a better and fairer welfare for the human race. The production of nano-fiber chitin non-woven tissue is in accordance with the Organization for Economic Co-operation and Development (OECD) and European Union (EU) bio-economic programs: 100% biodegradable, ecological, and therefore useful in decreasing dependence on fossil fuel resources. The aim of our study is the evaluation of different formulations of a non-woven tissue obtained from electrospinning of a mixture of nanochitin fibrils, lignin, and poly (ethylene) oxide (PEO) on the restoration of damaged tissues. Wound repair is a complex process that involves epithelial and immune cells and includes the induction of metalloproteinases, inflammatory mediators, and angiogenic factors. Our in vitro results have shown that all of the realized chitin nanofibrils-bio-lignin non-woven tissues tested as nontoxic for human keratinocytes (HaCat) cells. Furthermore, the bio-composites that included bio-lignin at 0.1% have been able to modulate the expression of pro-inflammatory cytokines (Tumor Necrosis Factor-α, IL-1α, and IL8), lipopolysaccharide (LPS)-induced, and matrix metalloproteinases (MMPs) and human beta-defensin 2 (HBD-2) expression in HaCat cells, suggesting an anti-inflammatory and immunomodulatory role. Taken together, our results suggest that our chitin nanofibrils-bio-lignin non-woven tissue represents a skin-friendly tool that is able to favor a correct and fast wound repair.
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Affiliation(s)
| | - Alessandra Fusco
- Department of Experimental Medicine, Section of Microbiology and Clinical Microbiology, University of Campania Luigi Vanvitelli, via Costantinopoli 16, 80138 Naples, Italy.
| | - Iole Paoletti
- Department of Experimental Medicine, Section of Microbiology and Clinical Microbiology, University of Campania Luigi Vanvitelli, via Costantinopoli 16, 80138 Naples, Italy.
| | - Brunella Perfetto
- Department of Experimental Medicine, Section of Microbiology and Clinical Microbiology, University of Campania Luigi Vanvitelli, via Costantinopoli 16, 80138 Naples, Italy.
| | | | - Marco Palombo
- Plastic, Reconstructive and Aesthetic Surgery Department, CTO Hospital, 00142 Rome, Italy.
| | - Angelo Chianese
- Chemical Materials Environmental Engineering Department, University La Sapienza, 00185 Rome, Italy.
| | - Adone Baroni
- Multidisciplinary Department of Medical-Surgical and Dental Specialty-Dermatology, University of Campania Luigi Vanvitelli, 80138 Naples, Italy.
| | - Giovanna Donnarumma
- Department of Experimental Medicine, Section of Microbiology and Clinical Microbiology, University of Campania Luigi Vanvitelli, via Costantinopoli 16, 80138 Naples, Italy.
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Pietrelli L, Palombo M, Taresco V, Crisante F, Francolini I, Piozzi A. Copper (II) adsorption capacity of a novel hydroxytyrosol-based polyacrylate. Polym Bull (Berl) 2017. [DOI: 10.1007/s00289-016-1770-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Palombo M, Ligneul C, Valette J. Modeling diffusion of intracellular metabolites in the mouse brain up to very high diffusion-weighting: Diffusion in long fibers (almost) accounts for non-monoexponential attenuation. Magn Reson Med 2017; 77:343-350. [PMID: 27851876 PMCID: PMC5326576 DOI: 10.1002/mrm.26548] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Revised: 10/11/2016] [Accepted: 10/18/2016] [Indexed: 12/20/2022]
Abstract
PURPOSE To investigate how intracellular metabolites diffusion measured in vivo up to very high q/b in the mouse brain can be explained in terms of simple geometries. METHODS 10 mice were scanned using our new STE-LASER sequence, at 11.7 Tesla (T), up to qmax = 1 μm-1 at diffusion time td = 63.2 ms, corresponding to bmax = 60 ms/µm². We model cell fibers as randomly oriented cylinders, with radius a and intracellular diffusivity Dintracyl, and fit experimental data as a function of q to estimate Dintracyl and a. RESULTS Randomly oriented cylinders account well for measured attenuation, giving fiber radii and Dintracyl in the expected ranges (0.5-1.5 µm and 0.30-0.45 µm2/ms, respectively). The only exception is N-acetyl-aspartate (NAA) (extracted a∼0), which we show to be compatible with a small fraction of the NAA pool being confined in highly restricted compartments (with short T2). CONCLUSION The non-monoexponential signal attenuation of intracellular metabolites in the mouse brain can be described by diffusion in long and thin cylinders, yielding realistic Dintra and fiber diameters. However, this simple model may require small "corrections" for NAA, in the form of a small fraction of the NAA signal originating from a highly restricted compartment. Magn Reson Med, 2016. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Marco Palombo
- Commissariat a l’Energie Atomique et aux Energies Alternatives (CEA), Direction de la Recherche Fondamentale (DRF), Institut d’Imagerie Biomedicale (IBM), MIRCen, Fontenay-aux-Roses, France
- Centre National de la Recherche Scientifique (CNRS), Universite Paris-Sud, Universite Paris-Saclay, UMR 9199, Neurodegenerative Diseases Laboratory, Fontenay-aux-Roses, France
| | - Clemence Ligneul
- Commissariat a l’Energie Atomique et aux Energies Alternatives (CEA), Direction de la Recherche Fondamentale (DRF), Institut d’Imagerie Biomedicale (IBM), MIRCen, Fontenay-aux-Roses, France
- Centre National de la Recherche Scientifique (CNRS), Universite Paris-Sud, Universite Paris-Saclay, UMR 9199, Neurodegenerative Diseases Laboratory, Fontenay-aux-Roses, France
| | - Julien Valette
- Commissariat a l’Energie Atomique et aux Energies Alternatives (CEA), Direction de la Recherche Fondamentale (DRF), Institut d’Imagerie Biomedicale (IBM), MIRCen, Fontenay-aux-Roses, France
- Centre National de la Recherche Scientifique (CNRS), Universite Paris-Sud, Universite Paris-Saclay, UMR 9199, Neurodegenerative Diseases Laboratory, Fontenay-aux-Roses, France
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Caporale A, Palombo M, Macaluso E, Guerreri M, Bozzali M, Capuani S. The γ-parameter of anomalous diffusion quantified in human brain by MRI depends on local magnetic susceptibility differences. Neuroimage 2016; 147:619-631. [PMID: 28011255 DOI: 10.1016/j.neuroimage.2016.12.051] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Revised: 11/22/2016] [Accepted: 12/19/2016] [Indexed: 12/15/2022] Open
Abstract
Motivated by previous results obtained in vitro, we investigated the dependence of the anomalous diffusion (AD) MRI technique on local magnetic susceptibility differences (Δχ) driven by magnetic field inhomogeneity in human brains. The AD-imaging contrast investigated here is quantified by the stretched-exponential parameter γ, extracted from diffusion weighted (DW) data collected by varying diffusion gradient strengths. We performed T2* and DW experiments in eight healthy subjects at 3.0T. T2*-weighted images at different TEs=(10,20,35,55)ms and DW-EPI images with fourteen b-values from 0 to 5000s/mm2 were acquired. AD-metrics and Diffusion Tensor Imaging (DTI) parameters were compared and correlated to R2* and to Δχ values taken from literature for the gray (GM) and the white (WM) matter. Pearson's correlation test and Analysis of Variance with Bonferroni post-hoc test were used. Significant strong linear correlations were found between AD γ-metrics and R2* in both GM and WM of the human brain, but not between DTI-metrics and R2*. Depending on Δχ driven magnetic field inhomogeneity, the new contrast provided by AD-γ imaging reflects Δχ due to differences in myelin orientation and iron content within selected regions in the WM and GM, respectively. This feature of the AD-γ imaging due to the fact that γ is quantified by using MRI, may be an alternative strategy to investigate, at high magnetic fields, microstructural changes in myelin, and alterations due to iron accumulation. Possible clinical applications might be in the field of neurodegenerative diseases.
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Affiliation(s)
- A Caporale
- Morpho-functional Sciences, Department of Anatomical, Histological, Forensic and of the Locomotor System Science, Sapienza University of Rome, Italy; CNR ISC UOS Roma Sapienza, Physics Department Sapienza University of Rome, Rome, Italy.
| | - M Palombo
- CNR ISC UOS Roma Sapienza, Physics Department Sapienza University of Rome, Rome, Italy; MIRCen, CEA/DSV/I(2)BM, Fontenay-aux-Roses, France
| | - E Macaluso
- ImpAct Team, Lyon Neuroscience Research Center, Lyon, France
| | - M Guerreri
- CNR ISC UOS Roma Sapienza, Physics Department Sapienza University of Rome, Rome, Italy; Morphogenesis & Tissue Engineering, Department of Anatomical, Histological, Forensic and of the Locomotor System Science, Sapienza University of Rome, Italy
| | - M Bozzali
- Neuroimaging Laboratory Santa Lucia Foundation, Rome, Italy
| | - S Capuani
- CNR ISC UOS Roma Sapienza, Physics Department Sapienza University of Rome, Rome, Italy
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Ligneul C, Palombo M, Valette J. Metabolite diffusion up to very high b in the mouse brain in vivo: Revisiting the potential correlation between relaxation and diffusion properties. Magn Reson Med 2016; 77:1390-1398. [PMID: 27018415 PMCID: PMC5008464 DOI: 10.1002/mrm.26217] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 01/26/2016] [Accepted: 02/23/2016] [Indexed: 12/19/2022]
Abstract
PURPOSE To assess the potential correlation between metabolites diffusion and relaxation in the mouse brain, which is of importance for interpreting and modeling metabolite diffusion based on pure geometry, irrespective of relaxation properties (multicompartmental relaxation or surface relaxivity). METHODS A new diffusion-weighted magnetic resonance spectroscopy sequence is introduced, dubbed "STE-LASER," which presents several nice properties, in particular the absence of cross-terms with selection gradients and a very clean localization. Metabolite diffusion is then measured in a large voxel in the mouse brain at 11.7 Tesla using a cryoprobe, resulting in excellent signal-to-noise ratio, up to very high b-values under different echo time, mixing time, and diffusion time combinations. RESULTS Our results suggest that the correlation between relaxation and diffusion properties is extremely small or even nonexistent for metabolites in the mouse brain. CONCLUSION The present work strongly supports the interpretation and modeling of metabolite diffusion primarily based on geometry, irrespective of relaxation properties, at least under current experimental conditions. Magn Reson Med 77:1390-1398, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
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Affiliation(s)
- Clémence Ligneul
- Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Direction des Sciences du Vivant (DSV), Institut d'Imagerie Biomédicale (I2BM), MIRCen, Fontenay-aux-Roses, France.,Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud, Université Paris-Saclay, UMR 9199, Neurodegenerative Diseases Laboratory, Fontenay-aux-Roses, France
| | - Marco Palombo
- Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Direction des Sciences du Vivant (DSV), Institut d'Imagerie Biomédicale (I2BM), MIRCen, Fontenay-aux-Roses, France.,Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud, Université Paris-Saclay, UMR 9199, Neurodegenerative Diseases Laboratory, Fontenay-aux-Roses, France
| | - Julien Valette
- Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Direction des Sciences du Vivant (DSV), Institut d'Imagerie Biomédicale (I2BM), MIRCen, Fontenay-aux-Roses, France.,Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud, Université Paris-Saclay, UMR 9199, Neurodegenerative Diseases Laboratory, Fontenay-aux-Roses, France
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