1
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Hutchinson G, Thotland J, Pisharady PK, Garwood M, Lenglet C, Kauppinen RA. T1 relaxation and axon fibre configuration in human white matter. NMR IN BIOMEDICINE 2024:e5234. [PMID: 39097977 DOI: 10.1002/nbm.5234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 06/18/2024] [Accepted: 07/22/2024] [Indexed: 08/06/2024]
Abstract
Understanding the effects of white matter (WM) axon fibre microstructure on T1 relaxation is important for neuroimaging. Here, we have studied the interrelationship between T1 and axon fibre configurations at 3T and 7T. T1 and S0 (=signal intensity at zero TI) were computed from MP2RAGE images acquired with six inversion recovery times. Multishell diffusion MRI images were analysed for fractional anisotropy (FA); MD; V1; the volume fractions for the first (f1), second (f2) and third (f3) fibre configuration; and fibre density cross-section images for the first (fdc1), second (fdc2) and third (fdc3) fibres. T1 values were plotted as a function of FA, f1, f2, f3, fdc1, fdc2 and fdc3 to examine interrelationships between the longitudinal relaxation and the diffusion MRI microstructural measures. T1 values decreased with increasing FA, f1 and f2 in a nonlinear fashion. At low FA values (from 0.2 to 0.4), a steep shortening of T1 was followed by a shallow shortening by 6%-10% at both fields. The steep shortening was associated with decreasing S0 and MD. T1 also decreased with increasing fdc1 values in a nonlinear fashion. Instead, only a small T1 change as a function of either f3 or fdc3 was observed. In WM areas selected by fdc1 only masks, T1 was shorter than in those with fdc2/fdc3. In WM areas with high single fibre populations, as delineated by f1/fdc1 masks, T1 was shorter than in tissue with high complex fibre configurations, as segmented by f2/fdc2 or f3/fdc3 masks. T1 differences between these WM areas are attributable to combined effects by T1 anisotropy and lowered FA. The current data show strong interrelationships between T1, axon fibre configuration and orientation in healthy WM. It is concluded that diffusion MRI microstructural measures are essential in the effort to interpret quantitative T1 images in terms of tissue state in health and disease.
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Affiliation(s)
- Grace Hutchinson
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Jeromy Thotland
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Pramod K Pisharady
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Michael Garwood
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Risto A Kauppinen
- Department of Electric and Electronic Engineering, University of Bristol, Bristol, UK
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2
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Behroozi M, Graïc JM, Gerussi T. Beyond the surface: how ex-vivo diffusion-weighted imaging reveals large animal brain microstructure and connectivity. Front Neurosci 2024; 18:1411982. [PMID: 38988768 PMCID: PMC11233460 DOI: 10.3389/fnins.2024.1411982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 06/12/2024] [Indexed: 07/12/2024] Open
Abstract
Diffusion-weighted Imaging (DWI) is an effective and state-of-the-art neuroimaging method that non-invasively reveals the microstructure and connectivity of tissues. Recently, novel applications of the DWI technique in studying large brains through ex-vivo imaging enabled researchers to gain insights into the complex neural architecture in different species such as those of Perissodactyla (e.g., horses and rhinos), Artiodactyla (e.g., bovids, swines, and cetaceans), and Carnivora (e.g., felids, canids, and pinnipeds). Classical in-vivo tract-tracing methods are usually considered unsuitable for ethical and practical reasons, in large animals or protected species. Ex-vivo DWI-based tractography offers the chance to examine the microstructure and connectivity of formalin-fixed tissues with scan times and precision that is not feasible in-vivo. This paper explores DWI's application to ex-vivo brains of large animals, highlighting the unique insights it offers into the structure of sometimes phylogenetically different neural networks, the connectivity of white matter tracts, and comparative evolutionary adaptations. Here, we also summarize the challenges, concerns, and perspectives of ex-vivo DWI that will shape the future of the field in large brains.
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Affiliation(s)
- Mehdi Behroozi
- Department of Biopsychology, Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr-University Bochum, Bochum, Germany
| | - Jean-Marie Graïc
- Department of Comparative Biomedicine and Food Science (BCA), University of Padova, Legnaro, Italy
| | - Tommaso Gerussi
- Department of Comparative Biomedicine and Food Science (BCA), University of Padova, Legnaro, Italy
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, Hong Kong SAR, China
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3
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Zong F, Wang L, Liu H, Xue B, Bai R, Liu Y. A genetic optimisation and iterative reconstruction framework for sparse multi-dimensional diffusion-relaxation correlation MRI. Comput Biol Med 2024; 175:108508. [PMID: 38678941 DOI: 10.1016/j.compbiomed.2024.108508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 04/11/2024] [Accepted: 04/21/2024] [Indexed: 05/01/2024]
Abstract
Multi-dimensional diffusion-relaxation correlation (DRC) magnetic resonance imaging (MRI) techniques have recently been developed to investigate tissue microstructures. Sub-voxel tissue heterogeneity is resolved from the local correlation distributions of relaxation time and molecular diffusivity. However, the implementation of these techniques considerably increases the total acquisition time, and simply reducing the scan time may be at the expense of detailed structural resolution. To overcome these limitations, an optimised framework was proposed for acquiring microstructural maps of the human brain on a clinically feasible timescale. First, the acquisition parameters of the multi-dimensional DRC MRI method were sparsely optimised using a genetic algorithm with a fitness function according to the spectral resolution of the correlation map, hardware requirements, and total scan time. Next, the acquired DRC MRI data were processed using a proposed numerical algorithm based on the dynamic inverse Laplace transform (ILT). Prior knowledge from one-dimensional data was then utilised in the iterative procedure to improve the spectral resolution. Finally, the proposed framework was validated using Monte Carlo simulations and experimental data acquired from healthy participants on an MRI scanner. The results demonstrated that the suggested approach is feasible for offering high-resolution DRC maps that correspond to distinct microstructures with a limited amount of optimised acquisition data from two-dimensional DRC sampling space. By significantly reducing scan time while retaining structural resolution, this approach may enable multi-dimensional DRC MRI to be more widely used for quantitative evaluation in biological and medical settings.
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Affiliation(s)
- Fangrong Zong
- School of Artificial Intelligence, Beijing University of Post and Telecommunication, Beijing, 100876, China.
| | - Lixian Wang
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Huabing Liu
- Beijing Limecho Technology Co., Ltd., Beijing, 102200, China
| | - Bing Xue
- School of Engineering and Computer Science, Victoria University of Wellington, Victoria, 6140, New Zealand
| | - Ruiliang Bai
- Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, 310020, China; MOE Frontier Science Center for Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, 310030, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Post and Telecommunication, Beijing, 100876, China.
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4
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Johnson JTE, Irfanoglu MO, Manninen E, Ross TJ, Yang Y, Laun FB, Martin J, Topgaard D, Benjamini D. In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI. Hum Brain Mapp 2024; 45:e26697. [PMID: 38726888 PMCID: PMC11082920 DOI: 10.1002/hbm.26697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 03/28/2024] [Accepted: 04/12/2024] [Indexed: 05/13/2024] Open
Abstract
Diffusion MRI with free gradient waveforms, combined with simultaneous relaxation encoding, referred to as multidimensional MRI (MD-MRI), offers microstructural specificity in complex biological tissue. This approach delivers intravoxel information about the microstructure, local chemical composition, and importantly, how these properties are coupled within heterogeneous tissue containing multiple microenvironments. Recent theoretical advances incorporated diffusion time dependency and integrated MD-MRI with concepts from oscillating gradients. This framework probes the diffusion frequency,ω $$ \omega $$ , in addition to the diffusion tensor,D $$ \mathbf{D} $$ , and relaxation,R 1 $$ {R}_1 $$ ,R 2 $$ {R}_2 $$ , correlations. AD ω - R 1 - R 2 $$ \mathbf{D}\left(\omega \right)-{R}_1-{R}_2 $$ clinical imaging protocol was then introduced, with limited brain coverage and 3 mm3 voxel size, which hinder brain segmentation and future cohort studies. In this study, we introduce an efficient, sparse in vivo MD-MRI acquisition protocol providing whole brain coverage at 2 mm3 voxel size. We demonstrate its feasibility and robustness using a well-defined phantom and repeated scans of five healthy individuals. Additionally, we test different denoising strategies to address the sparse nature of this protocol, and show that efficient MD-MRI encoding design demands a nuanced denoising approach. The MD-MRI framework provides rich information that allows resolving the diffusion frequency dependence into intravoxel components based on theirD ω - R 1 - R 2 $$ \mathbf{D}\left(\omega \right)-{R}_1-{R}_2 $$ distribution, enabling the creation of microstructure-specific maps in the human brain. Our results encourage the broader adoption and use of this new imaging approach for characterizing healthy and pathological tissues.
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Affiliation(s)
- Jessica T. E. Johnson
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIHBaltimoreMarylandUSA
| | - M. Okan Irfanoglu
- Quantitative Medical Imaging Section, National Institute of Biomedical Imaging and Bioengineering, National Institutes of HealthBethesdaMarylandUSA
| | - Eppu Manninen
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIHBaltimoreMarylandUSA
| | - Thomas J. Ross
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of HealthBaltimoreMarylandUSA
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of HealthBaltimoreMarylandUSA
| | - Frederik B. Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Jan Martin
- Department of ChemistryLund UniversityLundSweden
| | | | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIHBaltimoreMarylandUSA
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5
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Lampinen B, Szczepankiewicz F, Lätt J, Knutsson L, Mårtensson J, Björkman-Burtscher IM, van Westen D, Sundgren PC, Ståhlberg F, Nilsson M. Probing brain tissue microstructure with MRI: principles, challenges, and the role of multidimensional diffusion-relaxation encoding. Neuroimage 2023; 282:120338. [PMID: 37598814 DOI: 10.1016/j.neuroimage.2023.120338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 06/30/2023] [Accepted: 08/17/2023] [Indexed: 08/22/2023] Open
Abstract
Diffusion MRI uses the random displacement of water molecules to sensitize the signal to brain microstructure and to properties such as the density and shape of cells. Microstructure modeling techniques aim to estimate these properties from acquired data by separating the signal between virtual tissue 'compartments' such as the intra-neurite and the extra-cellular space. A key challenge is that the diffusion MRI signal is relatively featureless compared with the complexity of brain tissue. Another challenge is that the tissue microstructure is wildly different within the gray and white matter of the brain. In this review, we use results from multidimensional diffusion encoding techniques to discuss these challenges and their tentative solutions. Multidimensional encoding increases the information content of the data by varying not only the b-value and the encoding direction but also additional experimental parameters such as the shape of the b-tensor and the echo time. Three main insights have emerged from such encoding. First, multidimensional data contradict common model assumptions on diffusion and T2 relaxation, and illustrates how the use of these assumptions cause erroneous interpretations in both healthy brain and pathology. Second, many model assumptions can be dispensed with if data are acquired with multidimensional encoding. The necessary data can be easily acquired in vivo using protocols optimized to minimize Cramér-Rao lower bounds. Third, microscopic diffusion anisotropy reflects the presence of axons but not dendrites. This insight stands in contrast to current 'neurite models' of brain tissue, which assume that axons in white matter and dendrites in gray matter feature highly similar diffusion. Nevertheless, as an axon-based contrast, microscopic anisotropy can differentiate gray and white matter when myelin alterations confound conventional MRI contrasts.
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Affiliation(s)
- Björn Lampinen
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden.
| | | | - Jimmy Lätt
- Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden
| | - Linda Knutsson
- Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Johan Mårtensson
- Clinical Sciences Lund, Logopedics, Phoniatrics and Audiology, Lund University, Lund, Sweden
| | - Isabella M Björkman-Burtscher
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Danielle van Westen
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden
| | - Pia C Sundgren
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden; Lund University BioImaging Centre (LBIC), Lund University, Lund, Sweden
| | - Freddy Ståhlberg
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Markus Nilsson
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden
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6
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Johnson JT, Irfanoglu MO, Manninen E, Ross TJ, Yang Y, Laun FB, Martin J, Topgaard D, Benjamini D. In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.10.561702. [PMID: 37987005 PMCID: PMC10659440 DOI: 10.1101/2023.10.10.561702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Diffusion MRI with free gradient waveforms, combined with simultaneous relaxation encoding, referred to as multidimensional MRI (MD-MRI), offers microstructural specificity in complex biological tissue. This approach delivers intravoxel information about the microstructure, local chemical composition, and importantly, how these properties are coupled within heterogeneous tissue containing multiple microenvironments. Recent theoretical advances incorporated diffusion time dependency and integrated MD-MRI with concepts from oscillating gradients. This framework probes the diffusion frequency, ω , in addition to the diffusion tensor, D , and relaxation, R 1 , R 2 , correlations. A D ( ω ) - R 1 - R 2 clinical imaging protocol was then introduced, with limited brain coverage and 3 mm3 voxel size, which hinder brain segmentation and future cohort studies. In this study, we introduce an efficient, sparse in vivo MD-MRI acquisition protocol providing whole brain coverage at 2 mm3 voxel size. We demonstrate its feasibility and robustness using a well-defined phantom and repeated scans of five healthy individuals. Additionally, we test different denoising strategies to address the sparse nature of this protocol, and show that efficient MD-MRI encoding design demands a nuanced denoising approach. The MD-MRI framework provides rich information that allows resolving the diffusion frequency dependence into intravoxel components based on their D ( ω ) - R 1 - R 2 distribution, enabling the creation of microstructure-specific maps in the human brain. Our results encourage the broader adoption and use of this new imaging approach for characterizing healthy and pathological tissues.
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Affiliation(s)
- Jessica T.E. Johnson
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| | - M. Okan Irfanoglu
- Quantitative Medical Imaging Section, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
| | - Eppu Manninen
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Thomas J. Ross
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
| | - Frederik B. Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Jan Martin
- Department of Chemistry, Lund University, Lund, Sweden
| | | | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
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7
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Kauppinen RA, Thothard J, Leskinen HPP, Pisharady PK, Manninen E, Kettunen M, Lenglet C, Gröhn OHJ, Garwood M, Nissi MJ. Axon fiber orientation as the source of T 1 relaxation anisotropy in white matter: A study on corpus callosum in vivo and ex vivo. Magn Reson Med 2023; 90:708-721. [PMID: 37145027 DOI: 10.1002/mrm.29667] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 02/22/2023] [Accepted: 03/24/2023] [Indexed: 05/06/2023]
Abstract
PURPOSE Recent studies indicate that T1 in white matter (WM) is influenced by fiber orientation in B0 . The purpose of the study was to investigate the interrelationships between axon fiber orientation in corpus callosum (CC) and T1 relaxation time in humans in vivo as well as in rat brain ex vivo. METHODS Volunteers were scanned for relaxometric and diffusion MRI at 3 T and 7 T. Angular T1 plots from WM were computed using fractional anisotropy and fiber-to-field-angle maps. T1 and fiber-to-field angle were measured in five sections of CC to estimate the effects of inherently varying fiber orientations on T1 within the same tracts in vivo. Ex vivo rat-brain preparation encompassing posterior CC was rotated in B0 and T1 , and diffusion MRI images acquired at 9.4 T. T1 angular plots were determined at several rotation angles in B0 . RESULTS Angular T1 plots from global WM provided reference for estimated fiber orientation-linked T1 changes within CC. In anterior midbody of CC in vivo, where small axons are dominantly present, a shift in axon orientation is accompanied by a change in T1 , matching that estimated from WM T1 data. In CC, where large and giant axons are numerous, the measured T1 change is about 2-fold greater than the estimated one. Ex vivo rotation of the same midsagittal CC region of interest produced angular T1 plots at 9.4 T, matching those observed at 7 T in vivo. CONCLUSION These data causally link axon fiber orientation in B0 to the T1 relaxation anisotropy in WM.
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Affiliation(s)
- Risto A Kauppinen
- Department of Electric and Electronic Engineering, University of Bristol, Bristol, UK
| | - Jeromy Thothard
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Henri P P Leskinen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Pramod K Pisharady
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Eppu Manninen
- A.I. Virtanen Institute, University of Eastern Finland, Kuopio, Finland
| | - Mikko Kettunen
- A.I. Virtanen Institute, University of Eastern Finland, Kuopio, Finland
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Olli H J Gröhn
- A.I. Virtanen Institute, University of Eastern Finland, Kuopio, Finland
| | - Michael Garwood
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Mikko J Nissi
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
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8
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Fan Q, Eichner C, Afzali M, Mueller L, Tax CMW, Davids M, Mahmutovic M, Keil B, Bilgic B, Setsompop K, Lee HH, Tian Q, Maffei C, Ramos-Llordén G, Nummenmaa A, Witzel T, Yendiki A, Song YQ, Huang CC, Lin CP, Weiskopf N, Anwander A, Jones DK, Rosen BR, Wald LL, Huang SY. Mapping the human connectome using diffusion MRI at 300 mT/m gradient strength: Methodological advances and scientific impact. Neuroimage 2022; 254:118958. [PMID: 35217204 PMCID: PMC9121330 DOI: 10.1016/j.neuroimage.2022.118958] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 01/27/2022] [Accepted: 01/31/2022] [Indexed: 12/20/2022] Open
Abstract
Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide - one in the United Kingdom in Cardiff, Wales, another in continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength diffusion MRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for diffusion MRI and where the field is headed in the coming years.
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Affiliation(s)
- Qiuyun Fan
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Cornelius Eichner
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Maryam Afzali
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9JT, UK
| | - Lars Mueller
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9JT, UK
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Image Sciences Institute, University Medical Center (UMC) Utrecht, Utrecht, the Netherlands
| | - Mathias Davids
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Mirsad Mahmutovic
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Boris Keil
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Gabriel Ramos-Llordén
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | | | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Yi-Qiao Song
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA USA
| | - Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China; Shanghai Changning Mental Health Center, Shanghai, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Alfred Anwander
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA.
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9
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Bogusz F, Pieciak T, Afzali M, Pizzolato M. Diffusion-relaxation scattered MR signal representation in a multi-parametric sequence. Magn Reson Imaging 2022; 91:52-61. [PMID: 35561868 DOI: 10.1016/j.mri.2022.05.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 05/03/2022] [Accepted: 05/05/2022] [Indexed: 11/26/2022]
Abstract
This work focuses on obtaining a more general diffusion magnetic resonance imaging (MRI) signal representation that accounts for a longitudinal T1 and transverse T2⋆ relaxations while at the same time integrating directional diffusion in the context of scattered multi-parametric acquisitions, where only a few diffusion gradient directions and b-values are available for each pair of echo and inversion times. The method is based on the three-dimensional simple harmonic oscillator-based reconstruction and estimation (SHORE) representation of the diffusion signal, which enables the estimation of the orientation distribution function and the retrieval of various quantitative indices such as the generalized fractional anisotropy or the return-to-the-origin probability while simultaneously resolving for T1 and T2⋆ relaxation times. Our technique, the Relax-SHORE, has been tested on both in silico and in vivo diffusion-relaxation scattered MR data. The results show that Relax-SHORE is accurate in the context of scattered acquisitions while guaranteeing flexibility in the diffusion signal representation from multi-parametric sequences.
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Affiliation(s)
- Fabian Bogusz
- AGH University of Science and Technology, Kraków, Poland.
| | - Tomasz Pieciak
- AGH University of Science and Technology, Kraków, Poland; LPI, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Maryam Afzali
- Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM), Leeds, United Kingdom; Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Marco Pizzolato
- Department of applied mathematics and computer science, Technical University of Denmark, Kongens Lyngby, Denmark; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
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10
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Nonparametric D-R 1-R 2 distribution MRI of the living human brain. Neuroimage 2021; 245:118753. [PMID: 34852278 DOI: 10.1016/j.neuroimage.2021.118753] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/17/2021] [Accepted: 11/22/2021] [Indexed: 11/23/2022] Open
Abstract
Diffusion-relaxation correlation NMR can simultaneously characterize both the microstructure and the local chemical composition of complex samples that contain multiple populations of water. Recent developments on tensor-valued diffusion encoding and Monte Carlo inversion algorithms have made it possible to transfer diffusion-relaxation correlation NMR from small-bore scanners to clinical MRI systems. Initial studies on clinical MRI systems employed 5D D-R1 and D-R2 correlation to characterize healthy brain in vivo. However, these methods are subject to an inherent bias that originates from not including R2 or R1 in the analysis, respectively. This drawback can be remedied by extending the concept to 6D D-R1-R2 correlation. In this work, we present a sparse acquisition protocol that records all data necessary for in vivo 6D D-R1-R2 correlation MRI across 633 individual measurements within 25 min-a time frame comparable to previous lower-dimensional acquisition protocols. The data were processed with a Monte Carlo inversion algorithm to obtain nonparametric 6D D-R1-R2 distributions. We validated the reproducibility of the method in repeated measurements of healthy volunteers. For a post-therapy glioblastoma case featuring cysts, edema, and partially necrotic remains of tumor, we present representative single-voxel 6D distributions, parameter maps, and artificial contrasts over a wide range of diffusion-, R1-, and R2-weightings based on the rich information contained in the D-R1-R2 distributions.
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11
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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: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [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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12
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Cottaar M, Wu W, Tendler BC, Nagy Z, Miller K, Jbabdi S. Quantifying myelin in crossing fibers using diffusion-prepared phase imaging: Theory and simulations. Magn Reson Med 2021; 86:2618-2634. [PMID: 34254349 PMCID: PMC8581995 DOI: 10.1002/mrm.28907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 06/03/2021] [Accepted: 06/08/2021] [Indexed: 11/24/2022]
Abstract
PURPOSE Myelin has long been the target of neuroimaging research. However, most available techniques can only provide a voxel-averaged estimate of myelin content. In the human brain, white matter fiber pathways connecting different brain areas and carrying different functions often cross each other in the same voxel. A measure that can differentiate the degree of myelination of crossing fibers would provide a more specific marker of myelination. THEORY AND METHODS One MRI signal property that is sensitive to myelin is the phase accumulation. This sensitivity is used by measuring the phase accumulation of the signal remaining after diffusion-weighting, which is called diffusion-prepared phase imaging (DIPPI). Including diffusion-weighting before estimating the phase accumulation has two distinct advantages for estimating the degree of myelination: (1) It increases the relative contribution of intra-axonal water, whose phase is related linearly to the thickness of the surrounding myelin (in particular the log g-ratio); and (2) it gives directional information, which can be used to distinguish between crossing fibers. Here the DIPPI sequence is described, an approach is proposed to estimate the log g-ratio, and simulations are used and DIPPI data acquired in an isotropic phantom to quantify other sources of phase accumulation. RESULTS The expected bias is estimated in the log g-ratio for reasonable in vivo acquisition parameters caused by eddy currents (~4%-10%), remaining extra-axonal signal (~15%), and gradients in the bulk off-resonance field (<10% for most of the brain). CONCLUSION This new sequence may provide a g-ratio estimate per fiber population crossing within a voxel.
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Affiliation(s)
- Michiel Cottaar
- Wellcome Centre for Integrative Neuroimaging—Centre for Functional Magnetic Resonance Imaging of the BrainJohn Radcliffe HospitalUniversity of OxfordOxfordUnited Kingdom
| | - Wenchuan Wu
- Wellcome Centre for Integrative Neuroimaging—Centre for Functional Magnetic Resonance Imaging of the BrainJohn Radcliffe HospitalUniversity of OxfordOxfordUnited Kingdom
| | - Benjamin C. Tendler
- Wellcome Centre for Integrative Neuroimaging—Centre for Functional Magnetic Resonance Imaging of the BrainJohn Radcliffe HospitalUniversity of OxfordOxfordUnited Kingdom
| | - Zoltan Nagy
- Laboratory for Social and Neural Systems ResearchUniversity of ZurichZurichSwitzerland
| | - Karla Miller
- Wellcome Centre for Integrative Neuroimaging—Centre for Functional Magnetic Resonance Imaging of the BrainJohn Radcliffe HospitalUniversity of OxfordOxfordUnited Kingdom
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging—Centre for Functional Magnetic Resonance Imaging of the BrainJohn Radcliffe HospitalUniversity of OxfordOxfordUnited Kingdom
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13
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Fixel-based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities. Neuroimage 2021; 241:118417. [PMID: 34298083 DOI: 10.1016/j.neuroimage.2021.118417] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 07/11/2021] [Accepted: 07/20/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organization. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple "crossing" fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the "Fixel-Based Analysis" (FBA) framework, which implements bespoke solutions to this end. It has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to Fixel-Based Analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of all current FBA studies, categorized across a broad range of neuro-scientific domains, listing key design choices and summarizing their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the FBA framework, and outline some directions and future opportunities.
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14
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Kamagata K, Andica C, Kato A, Saito Y, Uchida W, Hatano T, Lukies M, Ogawa T, Takeshige-Amano H, Akashi T, Hagiwara A, Fujita S, Aoki S. Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases. Int J Mol Sci 2021; 22:ijms22105216. [PMID: 34069159 PMCID: PMC8155849 DOI: 10.3390/ijms22105216] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/10/2021] [Accepted: 05/10/2021] [Indexed: 12/27/2022] Open
Abstract
There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.
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Affiliation(s)
- Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan; (C.A.); (Y.S.); (W.U.); (T.A.); (A.H.); (S.F.); (S.A.)
- Correspondence:
| | - Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan; (C.A.); (Y.S.); (W.U.); (T.A.); (A.H.); (S.F.); (S.A.)
| | - Ayumi Kato
- Department of Multidisciplinary Internal Medicine, Faculty of Medicine, Tottori University, Yonago 683-8504, Japan;
| | - Yuya Saito
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan; (C.A.); (Y.S.); (W.U.); (T.A.); (A.H.); (S.F.); (S.A.)
| | - Wataru Uchida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan; (C.A.); (Y.S.); (W.U.); (T.A.); (A.H.); (S.F.); (S.A.)
| | - Taku Hatano
- Department of Neurology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (T.H.); (T.O.); (H.T.-A.)
| | - Matthew Lukies
- Department of Diagnostic and Interventional Radiology, Alfred Health, Melbourne, VIC 3004, Australia;
| | - Takashi Ogawa
- Department of Neurology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (T.H.); (T.O.); (H.T.-A.)
| | - Haruka Takeshige-Amano
- Department of Neurology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (T.H.); (T.O.); (H.T.-A.)
| | - Toshiaki Akashi
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan; (C.A.); (Y.S.); (W.U.); (T.A.); (A.H.); (S.F.); (S.A.)
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan; (C.A.); (Y.S.); (W.U.); (T.A.); (A.H.); (S.F.); (S.A.)
| | - Shohei Fujita
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan; (C.A.); (Y.S.); (W.U.); (T.A.); (A.H.); (S.F.); (S.A.)
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan; (C.A.); (Y.S.); (W.U.); (T.A.); (A.H.); (S.F.); (S.A.)
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15
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Reymbaut A, Critchley J, Durighel G, Sprenger T, Sughrue M, Bryskhe K, Topgaard D. Toward nonparametric diffusion- T1 characterization of crossing fibers in the human brain. Magn Reson Med 2021; 85:2815-2827. [PMID: 33301195 PMCID: PMC7898694 DOI: 10.1002/mrm.28604] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 10/26/2020] [Accepted: 10/27/2020] [Indexed: 12/24/2022]
Abstract
PURPOSE To estimate T 1 for each distinct fiber population within voxels containing multiple brain tissue types. METHODS A diffusion- T 1 correlation experiment was carried out in an in vivo human brain using tensor-valued diffusion encoding and multiple repetition times. The acquired data were inverted using a Monte Carlo algorithm that retrieves nonparametric distributions P ( D , R 1 ) of diffusion tensors and longitudinal relaxation rates R 1 = 1 / T 1 . Orientation distribution functions (ODFs) of the highly anisotropic components of P ( D , R 1 ) were defined to visualize orientation-specific diffusion-relaxation properties. Finally, Monte Carlo density-peak clustering (MC-DPC) was performed to quantify fiber-specific features and investigate microstructural differences between white matter fiber bundles. RESULTS Parameter maps corresponding to P ( D , R 1 ) 's statistical descriptors were obtained, exhibiting the expected R 1 contrast between brain tissue types. Our ODFs recovered local orientations consistent with the known anatomy and indicated differences in R 1 between major crossing fiber bundles. These differences, confirmed by MC-DPC, were in qualitative agreement with previous model-based works but seem biased by the limitations of our current experimental setup. CONCLUSIONS Our Monte Carlo framework enables the nonparametric estimation of fiber-specific diffusion- T 1 features, thereby showing potential for characterizing developmental or pathological changes in T 1 within a given fiber bundle, and for investigating interbundle T 1 differences.
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Affiliation(s)
- Alexis Reymbaut
- Department of Physical ChemistryLund UniversityLundSweden
- Random Walk Imaging ABLundSweden
| | | | | | - Tim Sprenger
- Karolinska InstituteStockholmSweden
- GE HealthcareStockholmSweden
| | | | | | - Daniel Topgaard
- Department of Physical ChemistryLund UniversityLundSweden
- Random Walk Imaging ABLundSweden
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16
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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: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [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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17
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Leppert IR, Andrews DA, Campbell JSW, Park DJ, Pike GB, Polimeni JR, Tardif CL. Efficient whole-brain tract-specific T 1 mapping at 3T with slice-shuffled inversion-recovery diffusion-weighted imaging. Magn Reson Med 2021; 86:738-753. [PMID: 33749017 DOI: 10.1002/mrm.28734] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 12/31/2020] [Accepted: 01/25/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE Most voxels in white matter contain multiple fiber populations with different orientations and levels of myelination. Conventional T1 mapping measures 1 T1 value per voxel, representing a weighted average of the multiple tract T1 times. Inversion-recovery diffusion-weighted imaging (IR-DWI) allows the T1 times of multiple tracts in a voxel to be disentangled, but the scan time is prohibitively long. Recently, slice-shuffled IR-DWI implementations have been proposed to significantly reduce scan time. In this work, we demonstrate that we can measure tract-specific T1 values in the whole brain using simultaneous multi-slice slice-shuffled IR-DWI at 3T. METHODS We perform simulations to evaluate the accuracy and precision of our crossing fiber IR-DWI signal model for various fiber parameters. The proposed sequence and signal model are tested in a phantom consisting of crossing asparagus pieces doped with gadolinium to vary T1 , and in 2 human subjects. RESULTS Our simulations show that tract-specific T1 times can be estimated within 5% of the nominal fiber T1 values. Tract-specific T1 values were resolved in subvoxel 2 fiber crossings in the asparagus phantom. Tract-specific T1 times were resolved in 2 different tract crossings in the human brain where myelination differences have previously been reported; the crossing of the cingulum and genu of the corpus callosum and the crossing of the corticospinal tract and pontine fibers. CONCLUSION Whole-brain tract-specific T1 mapping is feasible using slice-shuffled IR-DWI at 3T. This technique has the potential to improve the microstructural characterization of specific tracts implicated in neurodevelopment, aging, and demyelinating disorders.
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Affiliation(s)
- Ilana R Leppert
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada
| | - Daniel A Andrews
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada.,Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Jennifer S W Campbell
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada
| | - Daniel J Park
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - G Bruce Pike
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Department of Radiology and Department of Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Christine L Tardif
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada.,Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
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18
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Barakovic M, Tax CMW, Rudrapatna U, Chamberland M, Rafael-Patino J, Granziera C, Thiran JP, Daducci A, Canales-Rodríguez EJ, Jones DK. Resolving bundle-specific intra-axonal T 2 values within a voxel using diffusion-relaxation tract-based estimation. Neuroimage 2021; 227:117617. [PMID: 33301934 PMCID: PMC7615251 DOI: 10.1016/j.neuroimage.2020.117617] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 11/23/2020] [Accepted: 11/29/2020] [Indexed: 02/06/2023] Open
Abstract
At the typical spatial resolution of MRI in the human brain, approximately 60-90% of voxels contain multiple fiber populations. Quantifying microstructural properties of distinct fiber populations within a voxel is therefore challenging but necessary. While progress has been made for diffusion and T1-relaxation properties, how to resolve intra-voxel T2 heterogeneity remains an open question. Here a novel framework, named COMMIT-T2, is proposed that uses tractography-based spatial regularization with diffusion-relaxometry data to estimate multiple intra-axonal T2 values within a voxel. Unlike previously-proposed voxel-based T2 estimation methods, which (when applied in white matter) implicitly assume just one fiber bundle in the voxel or the same T2 for all bundles in the voxel, COMMIT-T2 can recover specific T2 values for each unique fiber population passing through the voxel. In this approach, the number of recovered unique T2 values is not determined by a number of model parameters set a priori, but rather by the number of tractography-reconstructed streamlines passing through the voxel. Proof-of-concept is provided in silico and in vivo, including a demonstration that distinct tract-specific T2 profiles can be recovered even in the three-way crossing of the corpus callosum, arcuate fasciculus, and corticospinal tract. We demonstrate the favourable performance of COMMIT-T2 compared to that of voxelwise approaches for mapping intra-axonal T2 exploiting diffusion, including a direction-averaged method and AMICO-T2, a new extension to the previously-proposed Accelerated Microstructure Imaging via Convex Optimization (AMICO) framework.
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Affiliation(s)
- Muhamed Barakovic
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, UK; Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, UK
| | - Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, UK
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, UK
| | - Jonathan Rafael-Patino
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, 1005 Lausanne, Switzerland
| | | | - Erick J Canales-Rodríguez
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; FIDMAG Germanes Hospitalàries Research Foundation, CIBERSAM, Barcelona, Spain.
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, UK; Mary MacKillop Institute for Health Research, Faculty of Health Sciences, Australian Catholic University, Melbourne, Australia
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19
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Kiselev VG, Körzdörfer G, Gall P. Toward Quantification: Microstructure and Magnetic Resonance Fingerprinting. Invest Radiol 2021; 56:1-9. [PMID: 33186141 DOI: 10.1097/rli.0000000000000738] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Quantitative magnetic resonance imaging (MRI) is a long-standing challenge. We advocate that the origin of the problem is the simplification applied in commonly used models of the MRI signal relation to the target parameters of biological tissues. Two research fields are briefly reviewed as ways to respond to the challenge of quantitative MRI, both experiencing an exponential growth right now. Microstructure MRI strives to build physiology-based models from cells to signal and, given the signal, back to the cells again. Magnetic resonance fingerprinting aims at efficient simultaneous determination of multiple signal parameters. The synergy of these yet disjoined approaches promises truly quantitative MRI with specific target-oriented diagnostic tools rather than universal imaging methods.
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Affiliation(s)
- Valerij G Kiselev
- From the Medical Physics, Department of Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg
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Fair MJ, Liao C, Manhard MK, Setsompop K. Diffusion-PEPTIDE: Distortion- and blurring-free diffusion imaging with self-navigated motion-correction and relaxometry capabilities. Magn Reson Med 2020; 85:2417-2433. [PMID: 33314281 DOI: 10.1002/mrm.28579] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 10/11/2020] [Accepted: 10/12/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE To implement the time-resolved relaxometry PEPTIDE technique into a diffusion acquisition to provide self-navigated, distortion- and blurring-free diffusion imaging that is robust to motion, while simultaneously providing T2 and T 2 ∗ mapping. THEORY AND METHODS The PEPTIDE readout was implemented into a spin-echo diffusion acquisition, enabling reconstruction of a time-series of T2 - and T 2 ∗ -weighted images, free from conventional echo planar imaging (EPI) distortion and blurring, for each diffusion-encoding. Robustness of PEPTIDE to motion and shot-to-shot phase variation was examined through a deliberate motion-corrupted diffusion experiment. Two diffusion-relaxometry in vivo brain protocols were also examined: (1)1 × 1 × 3 mm3 across 32 diffusion directions in 20 min, (2)1.5 × 1.5 × 3.0 mm3 across 6 diffusion-weighted images in 3.4 min. T2 , T 2 ∗ , and diffusion parameter maps were calculated from these data. As initial exploration of the rich diffusion-relaxometry data content for use in multi-compartment modeling, PEPTIDE data were acquired of a gadolinium-doped asparagus phantom. These datasets contained two compartments with different relaxation parameters and different diffusion orientation properties, and T2 relaxation variations across these diffusion directions were explored. RESULTS Diffusion-PEPTIDE showed the capability to provide high quality diffusion images and T2 and T 2 ∗ maps from both protocols. The reconstructions were distortion-free, avoided potential resolution losses exceeding 100% in equivalent EPI acquisitions, and showed tolerance to nearly 30° of rotational motion. Expected variation in T2 values as a function of diffusion direction was observed in the two-compartment asparagus phantom (P < .01), demonstrating potential to explore diffusion-PEPTIDE data for multi-compartment modeling. CONCLUSIONS Diffusion-PEPTIDE provides highly robust diffusion and relaxometry data and offers potential for future applications in diffusion-relaxometry multi-compartment modeling.
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Affiliation(s)
- Merlin J Fair
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Mary Kate Manhard
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.,Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
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21
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Golub M, Neto Henriques R, Gouveia Nunes R. Free-water DTI estimates from single b-value data might seem plausible but must be interpreted with care. Magn Reson Med 2020; 85:2537-2551. [PMID: 33270935 DOI: 10.1002/mrm.28599] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 10/19/2020] [Accepted: 10/23/2020] [Indexed: 01/18/2023]
Abstract
PURPOSE Free-water elimination DTI (FWE-DTI) has been used widely to distinguish increases of free-water partial-volume effects from tissue's diffusion in healthy aging and degenerative diseases. Because the FWE-DTI fitting is only well-posed for multishell acquisitions, a regularized gradient descent (RGD) method was proposed to enable application to single-shell data, more common in the clinic. However, the validity of the RGD method has been poorly assessed. This study aims to quantify the specificity of FWE-DTI procedures on single-shell and multishell data. METHODS Different FWE-DTI fitting procedures were tested on an open-source in vivo diffusion data set and single-shell and multishell synthetic signals, including the RGD and standard nonlinear least-squares methods. Single-voxel simulations were carried out to compare initialization approaches. A multivoxel phantom simulation was performed to evaluate the effect of spatial regularization when comparing between methods. To test the algorithms' specificity, phantoms with two different types of lesions were simulated: with altered mean diffusivity or with modified free water. RESULTS Plausible parameter maps were obtained with RGD from single-shell in vivo data. The plausibility of these maps was shown to be determined by the initialization. Tests with simulated lesions inserted into the in vivo data revealed that the RGD approach cannot distinguish free water from tissue mean-diffusivity alterations, contrarily to the nonlinear least-squares algorithm. CONCLUSION The RGD FWE-DTI method has limited specificity; thus, its results from single-shell data should be carefully interpreted. When possible, multishell acquisitions and the nonlinear least-squares approach should be preferred instead.
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Affiliation(s)
- Marc Golub
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | | | - Rita Gouveia Nunes
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
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22
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Novikov DS. The present and the future of microstructure MRI: From a paradigm shift to normal science. J Neurosci Methods 2020; 351:108947. [PMID: 33096152 DOI: 10.1016/j.jneumeth.2020.108947] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 08/29/2020] [Accepted: 09/10/2020] [Indexed: 12/29/2022]
Abstract
The aspiration of imaging tissue microstructure with MRI is to uncover micrometer-scale tissue features within millimeter-scale imaging voxels, in vivo. This kind of super-resolution has fueled a paradigm shift within the biomedical imaging community. However, what feels like an ongoing revolution in MRI, has been conceptually experienced in physics decades ago; from this point of view, our current developments can be seen as Thomas Kuhn's "normal science" stage of progress. While the concept of model-based quantification below the nominal imaging resolution is not new, its possibilities in neuroscience and neuroradiology are only beginning to be widely appreciated. This disconnect calls for communicating the progress of tissue microstructure MR imaging to its potential users. Here, a number of recent research developments are outlined in terms of the overarching concept of coarse-graining the tissue structure over an increasing diffusion length. A variety of diffusion models and phenomena are summarized on the phase diagram of diffusion MRI, with the unresolved problems and future directions corresponding to its unexplored domains.
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Affiliation(s)
- Dmitry S Novikov
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA.
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23
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Tournier JD, Christiaens D, Hutter J, Price AN, Cordero-Grande L, Hughes E, Bastiani M, Sotiropoulos SN, Smith SM, Rueckert D, Counsell SJ, Edwards AD, Hajnal JV. A data-driven approach to optimising the encoding for multi-shell diffusion MRI with application to neonatal imaging. NMR IN BIOMEDICINE 2020; 33:e4348. [PMID: 32632961 PMCID: PMC7116416 DOI: 10.1002/nbm.4348] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 04/23/2020] [Accepted: 05/18/2020] [Indexed: 06/11/2023]
Abstract
Diffusion MRI has the potential to provide important information about the connectivity and microstructure of the human brain during normal and abnormal development, noninvasively and in vivo. Recent developments in MRI hardware and reconstruction methods now permit the acquisition of large amounts of data within relatively short scan times. This makes it possible to acquire more informative multi-shell data, with diffusion sensitisation applied along many directions over multiple b-value shells. Such schemes are characterised by the number of shells acquired, and the specific b-value and number of directions sampled for each shell. However, there is currently no clear consensus as to how to optimise these parameters. In this work, we propose a means of optimising multi-shell acquisition schemes by estimating the information content of the diffusion MRI signal, and optimising the acquisition parameters for sensitivity to the observed effects, in a manner agnostic to any particular diffusion analysis method that might subsequently be applied to the data. This method was used to design the acquisition scheme for the neonatal diffusion MRI sequence used in the developing Human Connectome Project (dHCP), which aims to acquire high quality data and make it freely available to the research community. The final protocol selected by the algorithm, and currently in use within the dHCP, consists of 20 b=0 images and diffusion-weighted images at b = 400, 1000 and 2600 s/mm2 with 64, 88 and 128 directions per shell, respectively.
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Affiliation(s)
- Jacques-Donald Tournier
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
| | - Daan Christiaens
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
| | - Jana Hutter
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
| | - Anthony N Price
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
| | - Lucilio Cordero-Grande
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
| | - Emer Hughes
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Stamatios N Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Imperial College London, London, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
| | - Joseph V Hajnal
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
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24
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Grussu F, Battiston M, Veraart J, Schneider T, Cohen-Adad J, Shepherd TM, Alexander DC, Fieremans E, Novikov DS, Gandini Wheeler-Kingshott CAM. Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising. Neuroimage 2020; 217:116884. [PMID: 32360689 PMCID: PMC7378937 DOI: 10.1016/j.neuroimage.2020.116884] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 03/18/2020] [Accepted: 04/23/2020] [Indexed: 12/11/2022] Open
Abstract
Multi-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed in vivo by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We propose an approach to overcome these limitations, by implementing state-of-the-art microstructural MRI of the spinal cord with a unified signal readout in vivo (i.e. with matched spatial encoding parameters across a range of imaging contrasts). We base our acquisition on single-shot echo planar imaging with reduced field-of-view, and obtain data from two different vendors (vendor 1: Philips Achieva; vendor 2: Siemens Prisma). Importantly, the unified acquisition allows us to compare signal and noise across contrasts, thus enabling overall quality enhancement via multi-contrast image denoising methods. As a proof-of-concept, here we provide a demonstration with one such method, known as Marchenko-Pastur (MP) Principal Component Analysis (PCA) denoising. MP-PCA is a singular value (SV) decomposition truncation approach that relies on redundant acquisitions, i.e. such that the number of measurements is large compared to the number of components that are maintained in the truncated SV decomposition. Here we used in vivo and synthetic data to test whether a unified readout enables more efficient MP-PCA denoising of less redundant acquisitions, since these can be denoised jointly with more redundant ones. We demonstrate that a unified readout provides robust multi-parametric maps, including diffusion and kurtosis tensors from diffusion MRI, myelin metrics from two-pool magnetisation transfer, and T1 and T2 from relaxometry. Moreover, we show that MP-PCA improves the quality of our multi-contrast acquisitions, since it reduces the coefficient of variation (i.e. variability) by up to 17% for mean kurtosis, 8% for bound pool fraction (myelin-sensitive), and 13% for T1, while enabling more efficient denoising of modalities limited in redundancy (e.g. relaxometry). In conclusion, multi-parametric spinal cord qMRI with unified readout is feasible and provides robust microstructural metrics with matched resolution and distortions, whose quality benefits from multi-contrast denoising methods such as MP-PCA.
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Affiliation(s)
- Francesco Grussu
- Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
| | - Marco Battiston
- Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Jelle Veraart
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | | | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, Canada
| | - Timothy M Shepherd
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK; Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
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25
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Lampinen B, Szczepankiewicz F, Mårtensson J, van Westen D, Hansson O, Westin CF, Nilsson M. Towards unconstrained compartment modeling in white matter using diffusion-relaxation MRI with tensor-valued diffusion encoding. Magn Reson Med 2020; 84:1605-1623. [PMID: 32141131 DOI: 10.1002/mrm.28216] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 01/27/2020] [Accepted: 01/28/2020] [Indexed: 01/05/2023]
Abstract
PURPOSE To optimize diffusion-relaxation MRI with tensor-valued diffusion encoding for precise estimation of compartment-specific fractions, diffusivities, and T2 values within a two-compartment model of white matter, and to explore the approach in vivo. METHODS Sampling protocols featuring different b-values (b), b-tensor shapes (bΔ ), and echo times (TE) were optimized using Cramér-Rao lower bounds (CRLB). Whole-brain data were acquired in children, adults, and elderly with white matter lesions. Compartment fractions, diffusivities, and T2 values were estimated in a model featuring two microstructural compartments represented by a "stick" and a "zeppelin." RESULTS Precise parameter estimates were enabled by sampling protocols featuring seven or more "shells" with unique b/bΔ /TE-combinations. Acquisition times were approximately 15 minutes. In white matter of adults, the "stick" compartment had a fraction of approximately 0.5 and, compared with the "zeppelin" compartment, featured lower isotropic diffusivities (0.6 vs. 1.3 μm2 /ms) but higher T2 values (85 vs. 65 ms). Children featured lower "stick" fractions (0.4). White matter lesions exhibited high "zeppelin" isotropic diffusivities (1.7 μm2 /ms) and T2 values (150 ms). CONCLUSIONS Diffusion-relaxation MRI with tensor-valued diffusion encoding expands the set of microstructure parameters that can be precisely estimated and therefore increases their specificity to biological quantities.
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Affiliation(s)
- Björn Lampinen
- Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Filip Szczepankiewicz
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden.,Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Johan Mårtensson
- Clinical Sciences Lund, Department of Logopedics, Phoniatrics and Audiology, Lund University, Lund, Sweden
| | | | - Oskar Hansson
- Clinical Sciences Malmö, Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Carl-Fredrik Westin
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Markus Nilsson
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
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26
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de Almeida Martins J, Tax C, Szczepankiewicz F, Jones D, Westin CF, Topgaard D. Transferring principles of solid-state and Laplace NMR to the field of in vivo brain MRI. MAGNETIC RESONANCE (GOTTINGEN, GERMANY) 2020; 1:27-43. [PMID: 37904884 PMCID: PMC10500744 DOI: 10.5194/mr-1-27-2020] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 02/18/2020] [Indexed: 11/01/2023]
Abstract
Magnetic resonance imaging (MRI) is the primary method for noninvasive investigations of the human brain in health, disease, and development but yields data that are difficult to interpret whenever the millimeter-scale voxels contain multiple microscopic tissue environments with different chemical and structural properties. We propose a novel MRI framework to quantify the microscopic heterogeneity of the living human brain as spatially resolved five-dimensional relaxation-diffusion distributions by augmenting a conventional diffusion-weighted imaging sequence with signal encoding principles from multidimensional solid-state nuclear magnetic resonance (NMR) spectroscopy, relaxation-diffusion correlation methods from Laplace NMR of porous media, and Monte Carlo data inversion. The high dimensionality of the distribution space allows resolution of multiple microscopic environments within each heterogeneous voxel as well as their individual characterization with novel statistical measures that combine the chemical sensitivity of the relaxation rates with the link between microstructure and the anisotropic diffusivity of tissue water. The proposed framework is demonstrated on a healthy volunteer using both exhaustive and clinically viable acquisition protocols.
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Affiliation(s)
- João P. de Almeida Martins
- Division of Physical Chemistry, Department of Chemistry, Lund
University, Lund, Sweden
- Random Walk Imaging AB, Lund, Sweden
| | - Chantal M. W. Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff
University, Cardiff, UK
| | - Filip Szczepankiewicz
- Harvard Medical School, Boston, MA, USA
- Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff
University, Cardiff, UK
- Mary MacKillop Institute for Health Research, Australian Catholic
University, Melbourne, Australia
| | - Carl-Fredrik Westin
- Harvard Medical School, Boston, MA, USA
- Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Daniel Topgaard
- Division of Physical Chemistry, Department of Chemistry, Lund
University, Lund, Sweden
- Random Walk Imaging AB, Lund, Sweden
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27
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Assaf Y. Imaging laminar structures in the gray matter with diffusion MRI. Neuroimage 2019; 197:677-688. [DOI: 10.1016/j.neuroimage.2017.12.096] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Revised: 12/15/2017] [Accepted: 12/30/2017] [Indexed: 01/08/2023] Open
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28
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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: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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 T 2 * 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 T 2 * -ADC spectra using an inverse Laplace transform. RESULTS T 2 * -ADC spectra demonstrate clear quantitative separation between normal and dysfunctional placentas. CONCLUSIONS Combined T 2 * -diffusivity MRI is promising for assessing fetal and maternal health during pregnancy. The T 2 * -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 ScienceUniversity College LondonLondonUnited Kingdom
| | - Jana Hutter
- Biomedical Engineering DepartmentKing’s College LondonLondonUnited Kingdom
- Centre for the Developing BrainKing’s College LondonLondonUnited Kingdom
| | - Marco Palombo
- Centre for Medical Image Computing and Department of Computer ScienceUniversity College LondonLondonUnited Kingdom
| | - Laurence H. Jackson
- Biomedical Engineering DepartmentKing’s College LondonLondonUnited Kingdom
- Centre for the Developing BrainKing’s College LondonLondonUnited Kingdom
| | - Alison Ho
- Women’s Health DepartmentKing’s College LondonLondonUnited Kingdom
| | - Eleftheria Panagiotaki
- Centre for Medical Image Computing and Department of Computer ScienceUniversity College LondonLondonUnited Kingdom
| | - Lucy C. Chappell
- Women’s Health DepartmentKing’s College LondonLondonUnited Kingdom
| | - Mary A. Rutherford
- Centre for the Developing BrainKing’s College LondonLondonUnited Kingdom
| | - Joseph V. Hajnal
- Biomedical Engineering DepartmentKing’s College LondonLondonUnited Kingdom
- Centre for the Developing BrainKing’s College LondonLondonUnited Kingdom
| | - Daniel C. Alexander
- Centre for Medical Image Computing and Department of Computer ScienceUniversity College LondonLondonUnited Kingdom
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Tournier JD. Diffusion MRI in the brain - Theory and concepts. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2019; 112-113:1-16. [PMID: 31481155 DOI: 10.1016/j.pnmrs.2019.03.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 03/05/2019] [Accepted: 03/07/2019] [Indexed: 06/10/2023]
Abstract
Over the past two decades, diffusion MRI has become an essential tool in neuroimaging investigations. This is due to its sensitivity to the motion of water molecules as they diffuse through the microstructural environment, allowing diffusion MRI to be used as a 'probe' of tissue microstructure. Furthermore, this sensitivity is strongly direction-dependent, notably in brain white matter, due to the alignment of structures that restrict or hinder the motion of water molecules, notably axonal membranes. This provides a means of inferring the orientation of fibres in vivo, and by use of appropriate fibre-tracking algorithms, of delineating the path of white matter tracts in the brain. The ability to perform so-called tractography in humans in vivo non-invasively is unique to diffusion MRI, and is now used in applications such as neurosurgery planning and more broadly within investigations of brain connectomics. This review describes the theory and concepts of diffusion MRI and describes its most important areas of application in the brain, with a strong focus on tractography.
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Affiliation(s)
- J-Donald Tournier
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London SE1 7EH, UK; Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London SE1 7EH, UK.
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30
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Novikov DS, Fieremans E, Jespersen SN, Kiselev VG. Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation. NMR IN BIOMEDICINE 2019; 32:e3998. [PMID: 30321478 PMCID: PMC6481929 DOI: 10.1002/nbm.3998] [Citation(s) in RCA: 261] [Impact Index Per Article: 52.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Revised: 06/11/2018] [Accepted: 06/28/2018] [Indexed: 05/18/2023]
Abstract
We review, systematize and discuss models of diffusion in neuronal tissue, by putting them into an overarching physical context of coarse-graining over an increasing diffusion length scale. From this perspective, we view research on quantifying brain microstructure as occurring along three major avenues. The first avenue focusses on transient, or time-dependent, effects in diffusion. These effects signify the gradual coarse-graining of tissue structure, which occurs qualitatively differently in different brain tissue compartments. We show that transient effects contain information about the relevant length scales for neuronal tissue, such as the packing correlation length for neuronal fibers, as well as the degree of structural disorder along the neurites. The second avenue corresponds to the long-time limit, when the observed signal can be approximated as a sum of multiple nonexchanging anisotropic Gaussian components. Here, the challenge lies in parameter estimation and in resolving its hidden degeneracies. The third avenue employs multiple diffusion encoding techniques, able to access information not contained in the conventional diffusion propagator. We conclude with our outlook on future directions that could open exciting possibilities for designing quantitative markers of tissue physiology and pathology, based on methods of studying mesoscopic transport in disordered systems.
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Affiliation(s)
- Dmitry S. Novikov
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, USA
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, USA
| | - Sune N. Jespersen
- CFIN/MINDLab, Department of Clinical Medicine and Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Valerij G. Kiselev
- Medical Physics, Deptartment of Radiology, Faculty of Medicine, University of Freiburg, Germany
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31
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Pietsch M, Christiaens D, Hutter J, Cordero-Grande L, Price AN, Hughes E, Edwards AD, Hajnal JV, Counsell SJ, Tournier JD. A framework for multi-component analysis of diffusion MRI data over the neonatal period. Neuroimage 2019; 186:321-337. [PMID: 30391562 PMCID: PMC6347572 DOI: 10.1016/j.neuroimage.2018.10.060] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 10/17/2018] [Accepted: 10/22/2018] [Indexed: 12/11/2022] Open
Abstract
We describe a framework for creating a time-resolved group average template of the developing brain using advanced multi-shell high angular resolution diffusion imaging data, for use in group voxel or fixel-wise analysis, atlas-building, and related applications. This relies on the recently proposed multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) technique. We decompose the signal into one isotropic component and two anisotropic components, with response functions estimated from cerebrospinal fluid and white matter in the youngest and oldest participant groups, respectively. We build an orientationally-resolved template of those tissue components from data acquired from 113 babies between 33 and 44 weeks postmenstrual age, imaged as part of the Developing Human Connectome Project. These data were split into weekly groups, and registered to the corresponding group average templates using a previously-proposed non-linear diffeomorphic registration framework, designed to align orientation density functions (ODF). This framework was extended to allow the use of the multiple contrasts provided by the multi-tissue decomposition, and shown to provide superior alignment. Finally, the weekly templates were registered to the same common template to facilitate investigations into the evolution of the different components as a function of age. The resulting multi-tissue atlas provides insights into brain development and accompanying changes in microstructure, and forms the basis for future longitudinal investigations into healthy and pathological white matter maturation.
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Affiliation(s)
- Maximilian Pietsch
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK.
| | - Daan Christiaens
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Anthony N Price
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Emer Hughes
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
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32
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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] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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33
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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] [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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34
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Molina‐Romero M, Gómez PA, Sperl JI, Czisch M, Sämann PG, Jones DK, Menzel MI, Menze BH. A diffusion model-free framework with echo time dependence for free-water elimination and brain tissue microstructure characterization. Magn Reson Med 2018; 80:2155-2172. [PMID: 29573009 PMCID: PMC6790970 DOI: 10.1002/mrm.27181] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 01/18/2018] [Accepted: 02/24/2018] [Indexed: 12/19/2022]
Abstract
PURPOSE The compartmental nature of brain tissue microstructure is typically studied by diffusion MRI, MR relaxometry or their correlation. Diffusion MRI relies on signal representations or biophysical models, while MR relaxometry and correlation studies are based on regularized inverse Laplace transforms (ILTs). Here we introduce a general framework for characterizing microstructure that does not depend on diffusion modeling and replaces ill-posed ILTs with blind source separation (BSS). This framework yields proton density, relaxation times, volume fractions, and signal disentanglement, allowing for separation of the free-water component. THEORY AND METHODS Diffusion experiments repeated for several different echo times, contain entangled diffusion and relaxation compartmental information. These can be disentangled by BSS using a physically constrained nonnegative matrix factorization. RESULTS Computer simulations, phantom studies, together with repeatability and reproducibility experiments demonstrated that BSS is capable of estimating proton density, compartmental volume fractions and transversal relaxations. In vivo results proved its potential to correct for free-water contamination and to estimate tissue parameters. CONCLUSION Formulation of the diffusion-relaxation dependence as a BSS problem introduces a new framework for studying microstructure compartmentalization, and a novel tool for free-water elimination.
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Affiliation(s)
- Miguel Molina‐Romero
- Department of Computer ScienceTechnical University of MunichGarchingGermany
- GE Global Research EuropeGarchingGermany
| | - Pedro A. Gómez
- Department of Computer ScienceTechnical University of MunichGarchingGermany
- GE Global Research EuropeGarchingGermany
| | | | | | | | - Derek K. Jones
- CUBRIC, Cardiff UniversityCardiffUK
- School of Psychology, Faculty of Health SciencesAustralian Catholic UniversityMelbourneAustralia
| | | | - Bjoern H. Menze
- Department of Computer ScienceTechnical University of MunichGarchingGermany
- Institute for Advanced StudyTechnical University of MunichGarchingGermany
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35
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Abstract
The emergence of multiparametric diffusion models combining diffusion and relaxometry measurements provides powerful new ways to explore tissue microstructure, with the potential to provide new insights into tissue structure and function. However, their ability to provide rich analyses and the potential for clinical translation critically depends on the availability of efficient, integrated, multi-dimensional acquisitions. We propose a fully integrated sequence simultaneously sampling the acquisition parameter spaces required for T1 and T2* relaxometry and diffusion MRI. Slice-level interleaved diffusion encoding, multiple spin/gradient echoes and slice-shuffling are combined for higher efficiency, sampling flexibility and enhanced internal consistency. In-vivo data was successfully acquired on healthy adult brains. Obtained parametric maps as well as clustering results demonstrate the potential of the technique to provide eloquent data with an acceleration of roughly 20 compared to conventionally used approaches. The proposed integrated acquisition, which we call ZEBRA, offers significant acceleration and flexibility compared to existing diffusion-relaxometry studies, and thus facilitates wider use of these techniques both for research-driven and clinical applications.
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36
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Dyrby TB, Innocenti GM, Bech M, Lundell H. Validation strategies for the interpretation of microstructure imaging using diffusion MRI. Neuroimage 2018; 182:62-79. [PMID: 29920374 DOI: 10.1016/j.neuroimage.2018.06.049] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Revised: 06/08/2018] [Accepted: 06/15/2018] [Indexed: 12/19/2022] Open
Abstract
Extracting microanatomical information beyond the image resolution of MRI would provide valuable tools for diagnostics and neuroscientific research. A number of mathematical models already suggest microstructural interpretations of diffusion MRI (dMRI) data. Examples of such microstructural features could be cell bodies and neurites, e.g. the axon's diameter or their orientational distribution for global connectivity analysis using tractography, and have previously only been possible to access through conventional histology of post mortem tissue or invasive biopsies. The prospect of gaining the same knowledge non-invasively from the whole living human brain could push the frontiers for the diagnosis of neurological and psychiatric diseases. It could also provide a general understanding of the development and natural variability in the healthy brain across a population. However, due to a limited image resolution, most of the dMRI measures are indirect estimations and may depend on the whole chain from experimental parameter settings to model assumptions and implementation. Here, we review current literature in this field and highlight the integrative work across anatomical length scales that is needed to validate and trust a new dMRI method. We encourage interdisciplinary collaborations and data sharing in regards to applying and developing new validation techniques to improve the specificity of future dMRI methods.
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Affiliation(s)
- Tim B Dyrby
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.
| | - Giorgio M Innocenti
- Karolinska Institutet, Department of Neuroscience, Stockholm, Sweden; Brain and Mind Institute, Swiss Federal Institute of Technology in Lausanne, Lausanne, Switzerland
| | - Martin Bech
- Medical Radiation Physics, Lund University, Lund, Sweden
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
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37
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Novikov DS, Veraart J, Jelescu IO, Fieremans E. Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI. Neuroimage 2018; 174:518-538. [PMID: 29544816 DOI: 10.1016/j.neuroimage.2018.03.006] [Citation(s) in RCA: 138] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Revised: 02/22/2018] [Accepted: 03/03/2018] [Indexed: 10/17/2022] Open
Abstract
We develop a general analytical and numerical framework for estimating intra- and extra-neurite water fractions and diffusion coefficients, as well as neurite orientational dispersion, in each imaging voxel. By employing a set of rotational invariants and their expansion in the powers of diffusion weighting, we analytically uncover the nontrivial topology of the parameter estimation landscape, showing that multiple branches of parameters describe the measurement almost equally well, with only one of them corresponding to the biophysical reality. A comprehensive acquisition shows that the branch choice varies across the brain. Our framework reveals hidden degeneracies in MRI parameter estimation for neuronal tissue, provides microstructural and orientational maps in the whole brain without constraints or priors, and connects modern biophysical modeling with clinical MRI.
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Affiliation(s)
- Dmitry S Novikov
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA.
| | - Jelle Veraart
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Ileana O Jelescu
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Centre d'Imagerie Biomédicale, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Els Fieremans
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
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38
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de Almeida Martins JP, Topgaard D. Multidimensional correlation of nuclear relaxation rates and diffusion tensors for model-free investigations of heterogeneous anisotropic porous materials. Sci Rep 2018; 8:2488. [PMID: 29410433 PMCID: PMC5802831 DOI: 10.1038/s41598-018-19826-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 01/08/2018] [Indexed: 11/25/2022] Open
Abstract
Despite their widespread use in non-invasive studies of porous materials, conventional MRI methods yield ambiguous results for microscopically heterogeneous materials such as brain tissue. While the forward link between microstructure and MRI observables is well understood, the inverse problem of separating the signal contributions from different microscopic pores is notoriously difficult. Here, we introduce an experimental protocol where heterogeneity is resolved by establishing 6D correlations between the individual values of isotropic diffusivity, diffusion anisotropy, orientation of the diffusion tensor, and relaxation rates of distinct populations. Such procedure renders the acquired signal highly specific to the sample's microstructure, and allows characterization of the underlying pore space without prior assumptions on the number and nature of distinct microscopic environments. The experimental feasibility of the suggested method is demonstrated on a sample designed to mimic the properties of nerve tissue. If matched to the constraints of whole body scanners, this protocol could allow for the unconstrained determination of the different types of tissue that compose the living human brain.
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Affiliation(s)
| | - Daniel Topgaard
- Division of Physical Chemistry, Department of Chemistry, Lund University, Lund, Sweden
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39
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Does MD. Inferring brain tissue composition and microstructure via MR relaxometry. Neuroimage 2018; 182:136-148. [PMID: 29305163 DOI: 10.1016/j.neuroimage.2017.12.087] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 12/25/2017] [Accepted: 12/27/2017] [Indexed: 11/28/2022] Open
Abstract
MRI relaxometry is sensitive to a variety of tissue characteristics in a complex manner, which makes it both attractive and challenging for characterizing tissue. This article reviews the most common water proton relaxometry measures, T1, T2, and T2*, and reports on their development and current potential to probe the composition and microstructure of brain tissue. The development of these relaxometry measures is challenged by the need for suitably accurate tissue models, as well as robust acquisition and analysis methodologies. MRI relaxometry has been established as a tool for characterizing neural tissue, particular with respect to myelination, and the potential for further development exists.
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Affiliation(s)
- Mark D Does
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA.
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40
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Diffusion tensor imaging and tractography of the white matter in normal aging: The rate-of-change differs between segments within tracts. Magn Reson Imaging 2018; 45:113-119. [DOI: 10.1016/j.mri.2017.03.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Revised: 02/21/2017] [Accepted: 03/25/2017] [Indexed: 12/13/2022]
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41
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Kim D, Doyle EK, Wisnowski JL, Kim JH, Haldar JP. Diffusion-relaxation correlation spectroscopic imaging: A multidimensional approach for probing microstructure. Magn Reson Med 2017; 78:2236-2249. [PMID: 28317261 PMCID: PMC5605406 DOI: 10.1002/mrm.26629] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Revised: 12/19/2016] [Accepted: 01/10/2017] [Indexed: 12/16/2022]
Abstract
PURPOSE To propose and evaluate a novel multidimensional approach for imaging subvoxel tissue compartments called Diffusion-Relaxation Correlation Spectroscopic Imaging. THEORY AND METHODS Multiexponential modeling of MR diffusion or relaxation data is commonly used to infer the many different microscopic tissue compartments that contribute signal to macroscopic MR imaging voxels. However, multiexponential estimation is known to be difficult and ill-posed. Observing that this ill-posedness is theoretically reduced in higher dimensions, diffusion-relaxation correlation spectroscopic imaging uses a novel multidimensional imaging experiment that jointly encodes diffusion and relaxation information, and then uses a novel constrained reconstruction technique to generate a multidimensional diffusion-relaxation correlation spectrum for every voxel. The peaks of the multidimensional spectrum are expected to correspond to the distinct tissue microenvironments that are present within each macroscopic imaging voxel. RESULTS Using numerical simulations, experiment data from a custom-built phantom, and experiment data from a mouse model of traumatic spinal cord injury, diffusion-relaxation correlation spectroscopic imaging is demonstrated to provide substantially better multicompartment resolving power compared to conventional diffusion- and relaxation-based methods. CONCLUSION The diffusion-relaxation correlation spectroscopic imaging approach provides powerful new capabilities for resolving the different components of multicompartment tissue models, and can be leveraged to significantly expand the insights provided by MRI in studies of tissue microstructure. Magn Reson Med 78:2236-2249, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Daeun Kim
- Electrical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Eamon K. Doyle
- Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
- Cardiology, Children’s Hospital Los Angeles, Los Angeles, CA, USA
| | | | - Joong Hee Kim
- Neurology and Radiology, Washington University, St. Louis, MO, USA
| | - Justin P. Haldar
- Electrical Engineering, University of Southern California, Los Angeles, CA, USA
- Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
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42
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Dean DC, Planalp EM, Wooten W, Adluru N, Kecskemeti SR, Frye C, Schmidt CK, Schmidt NL, Styner MA, Goldsmith HH, Davidson RJ, Alexander AL. Mapping White Matter Microstructure in the One Month Human Brain. Sci Rep 2017; 7:9759. [PMID: 28852074 PMCID: PMC5575288 DOI: 10.1038/s41598-017-09915-6] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 08/01/2017] [Indexed: 11/24/2022] Open
Abstract
White matter microstructure, essential for efficient and coordinated transmission of neural communications, undergoes pronounced development during the first years of life, while deviations to this neurodevelopmental trajectory likely result in alterations of brain connectivity relevant to behavior. Hence, systematic evaluation of white matter microstructure in the normative brain is critical for a neuroscientific approach to both typical and atypical early behavioral development. However, few studies have examined the infant brain in detail, particularly in infants under 3 months of age. Here, we utilize quantitative techniques of diffusion tensor imaging and neurite orientation dispersion and density imaging to investigate neonatal white matter microstructure in 104 infants. An optimized multiple b-value diffusion protocol was developed to allow for successful acquisition during non-sedated sleep. Associations between white matter microstructure measures and gestation corrected age, regional asymmetries, infant sex, as well as newborn growth measures were assessed. Results highlight changes of white matter microstructure during the earliest periods of development and demonstrate differential timing of developing regions and regional asymmetries. Our results contribute to a growing body of research investigating the neurobiological changes associated with neurodevelopment and suggest that characteristics of white matter microstructure are already underway in the weeks immediately following birth.
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Affiliation(s)
- D C Dean
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA.
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA.
| | - E M Planalp
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
- Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - W Wooten
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA
| | - N Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - S R Kecskemeti
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - C Frye
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA
| | - C K Schmidt
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA
| | - N L Schmidt
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA
| | - M A Styner
- Department of Psychiatry, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - H H Goldsmith
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
- Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - R J Davidson
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA
- Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Psychiatry, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - A L Alexander
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
- Department of Psychiatry, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
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43
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Campbell JSW, Leppert IR, Narayanan S, Boudreau M, Duval T, Cohen-Adad J, Pike GB, Stikov N. Promise and pitfalls of g-ratio estimation with MRI. Neuroimage 2017; 182:80-96. [PMID: 28822750 DOI: 10.1016/j.neuroimage.2017.08.038] [Citation(s) in RCA: 82] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Revised: 07/28/2017] [Accepted: 08/12/2017] [Indexed: 12/13/2022] Open
Abstract
The fiber g-ratio is the ratio of the inner to the outer diameter of the myelin sheath of a myelinated axon. It has a limited dynamic range in healthy white matter, as it is optimized for speed of signal conduction, cellular energetics, and spatial constraints. In vivo imaging of the g-ratio in health and disease would greatly increase our knowledge of the nervous system and our ability to diagnose, monitor, and treat disease. MRI based g-ratio imaging was first conceived in 2011, and expanded to be feasible in full brain white matter with preliminary results in 2013. This manuscript reviews the growing g-ratio imaging literature and speculates on future applications. It details the methodology for imaging the g-ratio with MRI, and describes the known pitfalls and challenges in doing so.
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Affiliation(s)
- Jennifer S W Campbell
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada; NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada.
| | - Ilana R Leppert
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sridar Narayanan
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Mathieu Boudreau
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Tanguy Duval
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montréal, QC, Canada
| | | | - Nikola Stikov
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada; Montreal Heart Institute, Université de Montréal, Montréal, QC, Canada
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44
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Mohammadi S, Weiskopf N. [Computational neuroanatomy and microstructure imaging using magnetic resonance imaging]. DER NERVENARZT 2017; 88:839-849. [PMID: 28721539 DOI: 10.1007/s00115-017-0373-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND Current computational neuroanatomy focuses on morphological measurements of the brain using standard magnetic resonance imaging (MRI) techniques. In comparison quantitative MRI (qMRI) typically provides a better tissue contrast and also greatly improves the sensitivity and specificity with respect to the microstructural characteristics of tissue. OBJECTIVE Current methodological developments in qMRI are presented, which go beyond morphology because this provides standardized measurements of the microstructure of the brain. The concept of in-vivo histology is introduced, based on biophysical modelling of qMRI data (hMRI) for determination of quantitative histology-like markers of the microstructure. RESULTS The qMRI metrics can be used as direct biomarkers of the microstructural mechanisms driving observed morphological findings. The hMRI metrics utilize biophysical models of the MRI signal in order to determine 3‑dimensional maps of histology-like measurements in the white matter. CONCLUSION Non-invasive brain tissue characterization using qMRI or hMRI has significant implications for both scientific and clinical applications. Both approaches improve the comparability across sites and time points, facilitate multicenter and longitudinal studies as well as standardized diagnostics. The hMRI is expected to shed new light on the relationship between brain microstructure, function and behavior both in health and disease. In the future hMRI will play an indispensable role in the field of computational neuroanatomy.
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Affiliation(s)
- S Mohammadi
- Institut für systemische Neurowissenschaften, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
- Max-Planck-Institut für Kognitions- und Neurowissenschaften, Stephanstr. 1a, 04103, Leipzig, Deutschland
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, Großbritannien
| | - N Weiskopf
- Max-Planck-Institut für Kognitions- und Neurowissenschaften, Stephanstr. 1a, 04103, Leipzig, Deutschland.
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, Großbritannien.
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45
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Knight MJ, Dillon S, Jarutyte L, Kauppinen RA. Magnetic Resonance Relaxation Anisotropy: Physical Principles and Uses in Microstructure Imaging. Biophys J 2017; 112:1517-1528. [PMID: 28402893 PMCID: PMC5390049 DOI: 10.1016/j.bpj.2017.02.026] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Revised: 02/15/2017] [Accepted: 02/21/2017] [Indexed: 11/29/2022] Open
Abstract
Magnetic resonance imaging (MRI) provides an excellent means of studying tissue microstructure noninvasively since the microscopic tissue environment is imprinted on the MRI signal even at macroscopic voxel level. Mesoscopic variations in magnetic field, created by microstructure, influence the transverse relaxation time (T2) in an orientation-dependent fashion (T2 is anisotropic). However, predicting the effects of microstructure upon MRI observables is challenging and requires theoretical insight. We provide a formalism for calculating the effects upon T2 of tissue microstructure, using a model of cylindrical magnetic field perturbers. In a cohort of clinically healthy adults, we show that the angular information in spin-echo T2 is consistent with this model. We show that T2 in brain white matter of nondemented volunteers follows a U-shaped trajectory with age, passing its minimum at an age of ∼30 but that this depends on the particular white matter tract. The anisotropy of T2 also interacts with age and declines with increasing age. Late-myelinating white matter is more susceptible to age-related change than early-myelinating white matter, consistent with the retrogenesis hypothesis. T2 mapping may therefore be incorporated into microstructural imaging.
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Affiliation(s)
- Michael J Knight
- School of Experimental Psychology, University of Bristol, Bristol, United Kingdom.
| | - Serena Dillon
- ReMemBr group, Institute for Clinical Neurosciences, University of Bristol, Bristol, United Kingdom
| | - Lina Jarutyte
- School of Experimental Psychology, University of Bristol, Bristol, United Kingdom
| | - Risto A Kauppinen
- School of Experimental Psychology, University of Bristol, Bristol, United Kingdom; Clinical Research and Imaging Centre, University of Bristol, Bristol, United Kingdom
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Lampinen B, Szczepankiewicz F, Mårtensson J, van Westen D, Sundgren PC, Nilsson M. Neurite density imaging versus imaging of microscopic anisotropy in diffusion MRI: A model comparison using spherical tensor encoding. Neuroimage 2017; 147:517-531. [DOI: 10.1016/j.neuroimage.2016.11.053] [Citation(s) in RCA: 155] [Impact Index Per Article: 22.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2016] [Revised: 11/01/2016] [Accepted: 11/21/2016] [Indexed: 11/30/2022] Open
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Dubois J, Adibpour P, Poupon C, Hertz-Pannier L, Dehaene-Lambertz G. MRI and M/EEG studies of the White Matter Development in Human Fetuses and Infants: Review and Opinion. Brain Plast 2016; 2:49-69. [PMID: 29765848 PMCID: PMC5928537 DOI: 10.3233/bpl-160031] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Already during the last trimester of gestation, functional responses are recorded in foetuses and preterm newborns, attesting an already complex cerebral architecture. Then throughout childhood, anatomical connections are further refined but at different rates and over asynchronous periods across functional networks. Concurrently, infants gradually achieve new psychomotor and cognitive skills. Only the recent use of non-invasive techniques such as magnetic resonance imaging (MRI) and magneto- and electroencephalography (M/EEG) has opened the possibility to understand the relationships between brain maturation and skills development in vivo. In this review, we describe how these techniques have been applied to study the white matter maturation. At the structural level, the early architecture and myelination of bundles have been assessed with diffusion and relaxometry MRI, recently integrated in multi-compartment models and multi-parametric approaches. Nevertheless, technical limitations prevent us to map major developmental mechanisms such as fibers growth and pruning, and the progressive maturation at the bundle scale in case of mixing trajectories. At the functional level, M/EEG have been used to record different visual, somatosensory and auditory evoked responses. Because the conduction velocity of neural impulses increases with the myelination of connections, major changes in the components latency are observed throughout development. But so far, only a few studies have related structural and functional markers of white matter myelination. Such multi-modal approaches will be a major challenge in future research, not only to understand normal development, but also to characterize early mechanisms of pathologies and the influence of fetal and perinatal interventions on later outcome.
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Affiliation(s)
- Jessica Dubois
- INSERM, UMR992; CEA, NeuroSpin Center; University Paris Saclay, Gif-sur-Yvette, France
| | - Parvaneh Adibpour
- INSERM, UMR992; CEA, NeuroSpin Center; University Paris Saclay, Gif-sur-Yvette, France
| | - Cyril Poupon
- CEA, NeuroSpin Center, UNIRS; University Paris Saclay, Gif-sur-Yvette, France
| | - Lucie Hertz-Pannier
- CEA, NeuroSpin Center, UNIACT; University Paris Saclay, Gif-sur-Yvette, France; INSERM, UMR1129; University Paris Descartes, Paris, France
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48
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De Santis S, Assaf Y, Jeurissen B, Jones DK, Roebroeck A. T1 relaxometry of crossing fibres in the human brain. Neuroimage 2016; 141:133-142. [PMID: 27444568 PMCID: PMC5035137 DOI: 10.1016/j.neuroimage.2016.07.037] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Revised: 07/13/2016] [Accepted: 07/15/2016] [Indexed: 12/13/2022] Open
Abstract
A comprehensive tract-based characterisation of white matter should include the ability to quantify myelin and axonal attributes irrespective of the complexity of fibre organisation within the voxel. Recently, a new experimental framework that combines inversion recovery and diffusion MRI, called inversion recovery diffusion tensor imaging (IR-DTI), was introduced and applied in an animal study. IR-DTI provides the ability to assign to each unique fibre population within a voxel a specific value of the longitudinal relaxation time, T1, which is a proxy for myelin content. Here, we apply the IR-DTI approach to the human brain in vivo on 7 healthy subjects for the first time. We demonstrate that the approach is able to measure differential tract properties in crossing fibre areas, reflecting the different myelination of tracts. We also show that tract-specific T1 has less inter-subject variability compared to conventional T1 in areas of crossing fibres, suggesting increased specificity to distinct fibre populations. Finally we show in simulations that changes in myelination selectively affecting one fibre bundle in crossing fibre areas can potentially be detected earlier using IR-DTI. We apply the inversion recovery DTI approach to the human brain in vivo for the first time. We demonstrate that IR-DTI can measure tract-specific T1 in crossing fibres. IR-DTI T1 has less inter-subject variability compared to conventional T1 in crossing fibres. Changes in myelination affecting one fibre in crossing fibres can be detected earlier using IR-DTI.
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Affiliation(s)
- Silvia De Santis
- CUBRIC, School of Psychology, Cardiff University, Cardiff CF24 4HQ,UK; Maastricht University, Maastricht, The Netherlands.
| | - Yaniv Assaf
- Department of Neurobiology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Ben Jeurissen
- iMinds-Vision Lab, Dept. of Physics, University of Antwerp, Antwerp, Belgium
| | - Derek K Jones
- CUBRIC, School of Psychology, Cardiff University, Cardiff CF24 4HQ,UK; Neuroscience & Mental Health Research Institute, Cardiff University, CF10 3AT,UK
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Keedwell PA, Doidge AN, Meyer M, Lawrence N, Lawrence AD, Jones DK. Subgenual Cingulum Microstructure Supports Control of Emotional Conflict. Cereb Cortex 2016; 26:2850-62. [PMID: 27048427 PMCID: PMC4869815 DOI: 10.1093/cercor/bhw030] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Major depressive disorder (MDD) is associated with specific difficulties in attentional disengagement from negatively valenced material. Diffusion MRI studies have demonstrated altered white matter microstructure in the subgenual cingulum bundle (CB) in individuals with MDD, though the functional significance of these alterations has not been examined formally. This study explored whether individual differences in selective attention to negatively valenced stimuli are related to interindividual differences in subgenual CB microstructure. Forty-six individuals (21 with remitted MDD, 25 never depressed) completed an emotional Stroop task, using happy and angry distractor faces overlaid by pleasant or unpleasant target words and a control gender-based Stroop task. CBs were reconstructed in 38 individuals using diffusion-weighted imaging and tractography, and mean fractional anisotropy (FA) computed for the subgenual, retrosplenial, and parahippocampal subdivisions. No significant correlations were found between FA and performance in the control gender-based Stroop task in any CB region. However, the degree of interference produced by angry face distractors on time to identify pleasant words (emotional conflict) correlated selectively with FA in the subgenual CB (r = -0.53; P = 0.01). Higher FA was associated with reduced interference, irrespective of a diagnosis of MDD, suggesting that subgenual CB microstructure is functionally relevant for regulating attentional bias toward negative interpersonal stimuli.
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Affiliation(s)
- Paul A. Keedwell
- MRC Centre for Psychiatric Genetics and Genomics
- Neuroscience and Mental Health Research Institute
- Centre for Affective Disorders, Institute of Psychiatry, London SE5 8AF, UK
| | - Amie N. Doidge
- Neuroscience and Mental Health Research Institute
- School of Psychology and Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
- School of Psychology, University of Exeter, Devon, UK
| | - Marcel Meyer
- School of Psychology and Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
- Experimental Psychology and Methods, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | | | - Andrew D. Lawrence
- Neuroscience and Mental Health Research Institute
- School of Psychology and Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Derek K. Jones
- Neuroscience and Mental Health Research Institute
- School of Psychology and Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
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Abstract
Progress in magnetic resonance imaging (MRI) now makes it possible to identify the major white matter tracts in the living human brain. These tracts are important because they carry many of the signals communicated between different brain regions. MRI methods coupled with biophysical modeling can measure the tissue properties and structural features of the tracts that impact our ability to think, feel, and perceive. This review describes the fundamental ideas of the MRI methods used to identify the major white matter tracts in the living human brain.
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Affiliation(s)
- Brian A Wandell
- Department of Psychology and Stanford Neurosciences Institute, Stanford University, Stanford, California 94305;
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