1
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Zhou M, Stobbe R, Szczepankiewicz F, Budde M, Buck B, Kate M, Lloret M, Fairall P, Butcher K, Shuaib A, Emery D, Nilsson M, Westin CF, Beaulieu C. Tensor-valued diffusion MRI of human acute stroke. Magn Reson Med 2024; 91:2126-2141. [PMID: 38156813 DOI: 10.1002/mrm.29975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 11/18/2023] [Accepted: 11/27/2023] [Indexed: 01/03/2024]
Abstract
PURPOSE Tensor-valued diffusion encoding can disentangle orientation dispersion and subvoxel anisotropy, potentially offering insight into microstructural changes after cerebral ischemia. The purpose was to evaluate tensor-valued diffusion MRI in human acute ischemic stroke, assess potential confounders from diffusion time dependencies, and compare to Monte Carlo diffusion simulations of axon beading. METHODS Linear (LTE) and spherical (STE) b-tensor encoding with inherently different effective diffusion times were acquired in 21 acute ischemic stroke patients between 3 and 57 h post-onset at 3 T in 2.5 min. In an additional 10 patients, STE with 2 LTE yielding different effective diffusion times were acquired for comparison. Diffusional variance decomposition (DIVIDE) was used to estimate microscopic anisotropy (μFA), as well as anisotropic, isotropic, and total diffusional variance (MKA , MKI , MKT ). DIVIDE parameters, and diffusion tensor imaging (DTI)-derived mean diffusivity and fractional anisotropy (FA) were compared in lesion versus contralateral white matter. Monte Carlo diffusion simulations of various cylindrical geometries for all b-tensor protocols were used to interpret parameter measurements. RESULTS MD was ˜40% lower in lesions for all LTE/STE protocols. The DIVIDE parameters varied with effective diffusion time: higher μFA and MKA in lesion versus contralateral white matter for STE with longer effective diffusion time LTE, whereas the shorter effective diffusion time LTE protocol yielded lower μFA and MKA in lesions. Both protocols, regardless of diffusion time, were consistent with simulations of greater beading amplitude and intracellular volume fraction. CONCLUSION DIVIDE parameters depend on diffusion time in acute stroke but consistently indicate neurite beading and larger intracellular volume fraction.
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Affiliation(s)
- Mi Zhou
- Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Robert Stobbe
- Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
- Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | | | - Matthew Budde
- Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Brian Buck
- Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Mahesh Kate
- Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Mar Lloret
- Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Paige Fairall
- Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Ken Butcher
- School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Ashfaq Shuaib
- Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Derek Emery
- Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | - Markus Nilsson
- Clinical Sciences Lund, Lund University, Lund, Scania, Sweden
| | - Carl-Fredrik Westin
- Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Christian Beaulieu
- Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
- Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
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2
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Lee HH, Tian Q, Sheft M, Coronado-Leija R, Ramos-Llorden G, Abdollahzadeh A, Fieremans E, Novikov DS, Huang SY. The effects of axonal beading and undulation on axonal diameter estimation from diffusion MRI: Insights from simulations in human axons segmented from three-dimensional electron microscopy. NMR IN BIOMEDICINE 2024; 37:e5087. [PMID: 38168082 PMCID: PMC10942763 DOI: 10.1002/nbm.5087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 11/19/2023] [Accepted: 11/21/2023] [Indexed: 01/05/2024]
Abstract
The increasing availability of high-performance gradient systems in human MRI scanners has generated great interest in diffusion microstructural imaging applications such as axonal diameter mapping. Practically, sensitivity to axon diameter in diffusion MRI is attained at strong diffusion weightings b , where the deviation from the expected 1 / b scaling in white matter yields a finite transverse diffusivity, which is then translated into an axon diameter estimate. While axons are usually modeled as perfectly straight, impermeable cylinders, local variations in diameter (caliber variation or beading) and direction (undulation) are known to influence axonal diameter estimates and have been observed in microscopy data of human axons. In this study, we performed Monte Carlo simulations of diffusion in axons reconstructed from three-dimensional electron microscopy of a human temporal lobe specimen using simulated sequence parameters matched to the maximal gradient strength of the next-generation Connectome 2.0 human MRI scanner ( ≲ 500 mT/m). We show that axon diameter estimation is accurate for nonbeaded, nonundulating fibers; however, in fibers with caliber variations and undulations, the axon diameter is heavily underestimated due to caliber variations, and this effect overshadows the known overestimation of the axon diameter due to undulations. This unexpected underestimation may originate from variations in the coarse-grained axial diffusivity due to caliber variations. Given that increased axonal beading and undulations have been observed in pathological tissues, such as traumatic brain injury and ischemia, the interpretation of axon diameter alterations in pathology may be significantly confounded.
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Affiliation(s)
- Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Maxina Sheft
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard–MIT Health Sciences and Technology, Cambridge, Massachusetts, USA
| | - Ricardo Coronado-Leija
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Gabriel Ramos-Llorden
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Ali Abdollahzadeh
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Dmitry S. Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Susie Y. Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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3
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Barakovic M, Pizzolato M, Tax CMW, Rudrapatna U, Magon S, Dyrby TB, Granziera C, Thiran JP, Jones DK, Canales-Rodríguez EJ. Estimating axon radius using diffusion-relaxation MRI: calibrating a surface-based relaxation model with histology. Front Neurosci 2023; 17:1209521. [PMID: 37638307 PMCID: PMC10457121 DOI: 10.3389/fnins.2023.1209521] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 07/24/2023] [Indexed: 08/29/2023] Open
Abstract
Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows non-invasive estimation of axon radius, but accurately estimating the radius of axons in the human brain is challenging. Most axons in the brain have a radius below one micrometer, which falls below the sensitivity limit of dMRI signals even when using the most advanced human MRI scanners. Therefore, new MRI methods that are sensitive to small axon radii are needed. In this proof-of-concept investigation, we examine whether a surface-based axonal relaxation process could mediate a relationship between intra-axonal T2 and T1 times and inner axon radius, as measured using postmortem histology. A unique in vivo human diffusion-T1-T2 relaxation dataset was acquired on a 3T MRI scanner with ultra-strong diffusion gradients, using a strong diffusion-weighting (i.e., b = 6,000 s/mm2) and multiple inversion and echo times. A second reduced diffusion-T2 dataset was collected at various echo times to evaluate the model further. The intra-axonal relaxation times were estimated by fitting a diffusion-relaxation model to the orientation-averaged spherical mean signals. Our analysis revealed that the proposed surface-based relaxation model effectively explains the relationship between the estimated relaxation times and the histological axon radius measured in various corpus callosum regions. Using these histological values, we developed a novel calibration approach to predict axon radius in other areas of the corpus callosum. Notably, the predicted radii and those determined from histological measurements were in close agreement.
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Affiliation(s)
- Muhamed Barakovic
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, United Kingdom
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, Basel, Switzerland
| | - Marco Pizzolato
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Chantal M. W. Tax
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, United Kingdom
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, United Kingdom
| | - Stefano Magon
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, Basel, Switzerland
| | - Tim B. Dyrby
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
- Danish Research Centre for Magnetic Resonance (DRCMR), Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
- Centre d’Imagerie Biomédicale (CIBM), EPFL, Lausanne, Switzerland
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, United Kingdom
| | - Erick J. Canales-Rodríguez
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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4
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Villarreal-Haro JL, Gardier R, Canales-Rodríguez EJ, Fischi-Gomez E, Girard G, Thiran JP, Rafael-Patiño J. CACTUS: a computational framework for generating realistic white matter microstructure substrates. Front Neuroinform 2023; 17:1208073. [PMID: 37603781 PMCID: PMC10434236 DOI: 10.3389/fninf.2023.1208073] [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: 04/18/2023] [Accepted: 07/13/2023] [Indexed: 08/23/2023] Open
Abstract
Monte-Carlo diffusion simulations are a powerful tool for validating tissue microstructure models by generating synthetic diffusion-weighted magnetic resonance images (DW-MRI) in controlled environments. This is fundamental for understanding the link between micrometre-scale tissue properties and DW-MRI signals measured at the millimetre-scale, optimizing acquisition protocols to target microstructure properties of interest, and exploring the robustness and accuracy of estimation methods. However, accurate simulations require substrates that reflect the main microstructural features of the studied tissue. To address this challenge, we introduce a novel computational workflow, CACTUS (Computational Axonal Configurator for Tailored and Ultradense Substrates), for generating synthetic white matter substrates. Our approach allows constructing substrates with higher packing density than existing methods, up to 95% intra-axonal volume fraction, and larger voxel sizes of up to 500μm3 with rich fibre complexity. CACTUS generates bundles with angular dispersion, bundle crossings, and variations along the fibres of their inner and outer radii and g-ratio. We achieve this by introducing a novel global cost function and a fibre radial growth approach that allows substrates to match predefined targeted characteristics and mirror those reported in histological studies. CACTUS improves the development of complex synthetic substrates, paving the way for future applications in microstructure imaging.
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Affiliation(s)
- Juan Luis Villarreal-Haro
- Signal Processing Laboratory (LTS5), École Polytechnique Frale de Lausanne (EPFL), Lausanne, Switzerland
| | - Remy Gardier
- Signal Processing Laboratory (LTS5), École Polytechnique Frale de Lausanne (EPFL), Lausanne, Switzerland
| | - Erick J. Canales-Rodríguez
- Signal Processing Laboratory (LTS5), École Polytechnique Frale de Lausanne (EPFL), Lausanne, Switzerland
| | - Elda Fischi-Gomez
- Signal Processing Laboratory (LTS5), École Polytechnique Frale de Lausanne (EPFL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Gabriel Girard
- Signal Processing Laboratory (LTS5), École Polytechnique Frale de Lausanne (EPFL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
- Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Jean-Philippe Thiran
- Signal Processing Laboratory (LTS5), École Polytechnique Frale de Lausanne (EPFL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Jonathan Rafael-Patiño
- Signal Processing Laboratory (LTS5), École Polytechnique Frale de Lausanne (EPFL), Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
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5
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Gast H, Horowitz A, Krupnik R, Barazany D, Lifshits S, Ben-Amitay S, Assaf Y. A Method for In-Vivo Mapping of Axonal Diameter Distributions in the Human Brain Using Diffusion-Based Axonal Spectrum Imaging (AxSI). Neuroinformatics 2023; 21:469-482. [PMID: 37036548 PMCID: PMC10406702 DOI: 10.1007/s12021-023-09630-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/24/2023] [Indexed: 04/11/2023]
Abstract
In this paper we demonstrate a generalized and simplified pipeline called axonal spectrum imaging (AxSI) for in-vivo estimation of axonal characteristics in the human brain. Whole-brain estimation of the axon diameter, in-vivo and non-invasively, across all fiber systems will allow exploring uncharted aspects of brain structure and function relations with emphasis on connectivity and connectome analysis. While axon diameter mapping is important in and of itself, its correlation with conduction velocity will allow, for the first time, the explorations of information transfer mechanisms within the brain. We demonstrate various well-known aspects of axonal morphometry (e.g., the corpus callosum axon diameter variation) as well as other aspects that are less explored (e.g., axon diameter-based separation of the superior longitudinal fasciculus into segments). Moreover, we have created an MNI based mean axon diameter map over the entire brain for a large cohort of subjects providing the reference basis for future studies exploring relation between axon properties, its connectome representation, and other functional and behavioral aspects of the brain.
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Affiliation(s)
- Hila Gast
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
| | - Assaf Horowitz
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Ronnie Krupnik
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Daniel Barazany
- The Strauss center for neuroimaging, Tel Aviv University, Tel Aviv, Israel
| | - Shlomi Lifshits
- Department of Statistics and Operations Research, Faculty of Exact Sciences, Tel Aviv University, Tel-Aviv, Israel
| | - Shani Ben-Amitay
- School of Neurobiology, Biochemistry and Biophysics, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Yaniv Assaf
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- The Strauss center for neuroimaging, Tel Aviv University, Tel Aviv, Israel
- School of Neurobiology, Biochemistry and Biophysics, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
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6
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Warner W, Palombo M, Cruz R, Callaghan R, Shemesh N, Jones DK, Dell'Acqua F, Ianus A, Drobnjak I. Temporal Diffusion Ratio (TDR) for imaging restricted diffusion: Optimisation and pre-clinical demonstration. Neuroimage 2023; 269:119930. [PMID: 36750150 PMCID: PMC7615244 DOI: 10.1016/j.neuroimage.2023.119930] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 01/12/2023] [Accepted: 02/02/2023] [Indexed: 02/07/2023] Open
Abstract
Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell'Acqua et al., proc. ISMRM 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for informing on pore sizes, in particular the presence of large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion gradient waveforms. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time introduces and optimises the TDR method in simulation for a range of different tissues and scanner constraints and validates it in a pre-clinical demonstration. We consider both substrates containing cylinders and spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration, short diffusion time and high gradient strength with an acquisition with long gradient duration, long diffusion time and low gradient strength, maximises the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for informing on pore sizes and restricted diffusion in general.
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Affiliation(s)
- William Warner
- Centre for Medical Image Computing (CMIC), Computer Science Department, University College London, United Kingdom
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
| | - Renata Cruz
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | | | - Noam Shemesh
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Flavio Dell'Acqua
- NatBrainLab, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
| | - Ivana Drobnjak
- Centre for Medical Image Computing (CMIC), Computer Science Department, University College London, United Kingdom.
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7
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Wichtmann BD, Fan Q, Eskandarian L, Witzel T, Attenberger UI, Pieper CC, Schad L, Rosen BR, Wald LL, Huang SY, Nummenmaa A. Linear multi-scale modeling of diffusion MRI data: A framework for characterization of oriented structures across length scales. Hum Brain Mapp 2023; 44:1496-1514. [PMID: 36477997 PMCID: PMC9921225 DOI: 10.1002/hbm.26143] [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: 03/02/2022] [Revised: 10/07/2022] [Accepted: 10/23/2022] [Indexed: 12/12/2022] Open
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) has evolved to provide increasingly sophisticated investigations of the human brain's structural connectome in vivo. Restriction spectrum imaging (RSI) is a method that reconstructs the orientation distribution of diffusion within tissues over a range of length scales. In its original formulation, RSI represented the signal as consisting of a spectrum of Gaussian diffusion response functions. Recent technological advances have enabled the use of ultra-high b-values on human MRI scanners, providing higher sensitivity to intracellular water diffusion in the living human brain. To capture the complex diffusion time dependence of the signal within restricted water compartments, we expand upon the RSI approach to represent restricted water compartments with non-Gaussian response functions, in an extended analysis framework called linear multi-scale modeling (LMM). The LMM approach is designed to resolve length scale and orientation-specific information with greater specificity to tissue microstructure in the restricted and hindered compartments, while retaining the advantages of the RSI approach in its implementation as a linear inverse problem. Using multi-shell, multi-diffusion time DW-MRI data acquired with a state-of-the-art 3 T MRI scanner equipped with 300 mT/m gradients, we demonstrate the ability of the LMM approach to distinguish different anatomical structures in the human brain and the potential to advance mapping of the human connectome through joint estimation of the fiber orientation distributions and compartment size characteristics.
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Affiliation(s)
- Barbara D. Wichtmann
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General HospitalCharlestownMassachusettsUSA
- Department of Diagnostic and Interventional RadiologyUniversity Hospital BonnBonnGermany
| | - Qiuyun Fan
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General HospitalCharlestownMassachusettsUSA
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics EngineeringTianjin UniversityTianjinChina
| | - Laleh Eskandarian
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General HospitalCharlestownMassachusettsUSA
| | | | - Ulrike I. Attenberger
- Department of Diagnostic and Interventional RadiologyUniversity Hospital BonnBonnGermany
| | - Claus C. Pieper
- Department of Diagnostic and Interventional RadiologyUniversity Hospital BonnBonnGermany
| | - Lothar Schad
- Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty MannheimHeidelberg UniversityMannheimGermany
| | - Bruce R. Rosen
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General HospitalCharlestownMassachusettsUSA
| | - Lawrence L. Wald
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General HospitalCharlestownMassachusettsUSA
- Harvard‐MIT Division of Health Sciences and TechnologyMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Susie Y. Huang
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General HospitalCharlestownMassachusettsUSA
- Harvard‐MIT Division of Health Sciences and TechnologyMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Aapo Nummenmaa
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General HospitalCharlestownMassachusettsUSA
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8
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Borsos KB, Tse DHY, Dubovan PI, Baron CA. Tuned bipolar oscillating gradients for mapping frequency dispersion of diffusion kurtosis in the human brain. Magn Reson Med 2023; 89:756-766. [PMID: 36198030 DOI: 10.1002/mrm.29473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 09/06/2022] [Accepted: 09/08/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE Oscillating gradient spin-echo (OGSE) sequences have demonstrated an ability to probe time-dependent microstructural features, although they often suffer from low SNR due to increased TEs. In this work we introduce frequency-tuned bipolar (FTB) gradients as a variation of oscillating gradients with reduced TE and demonstrate their utility by mapping the frequency dispersion of kurtosis in human subjects. METHODS An FTB oscillating gradient waveform is presented that provides encoding of 1.5 net oscillation periods, thereby reducing the TE of the acquisition. Simulations were performed to determine an optimal protocol based on the SNR of kurtosis frequency dispersion-defined as the difference in kurtosis between pulsed and oscillating gradient acquisitions. Healthy human subjects were scanned at 7T using pulsed gradient and an optimized 23 Hz FTB protocol, which featured a maximum b-value of 2500 s/mm2 . In addition, to directly compare existing methods, measurements using traditional cosine OGSE were also acquired. RESULTS FTB oscillating gradients demonstrated equivalent frequency-dependent diffusion measurements compared with cosine-modulated OGSE while enabling a significant reduction in TE. Optimization and in vivo results suggest that FTB gradients provide increased SNR of kurtosis dispersion maps compared with traditional cosine OGSE. The optimized FTB gradient protocol demonstrated consistent reductions in apparent kurtosis values and increased diffusivity in generated frequency dispersion maps. CONCLUSIONS This work presents an alternative to traditional cosine OGSE sequences, enabling more time-efficient acquisitions of frequency-dependent diffusion quantities as demonstrated through in vivo kurtosis frequency dispersion maps.
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Affiliation(s)
- Kevin B Borsos
- Department of Medical Biophysics, Western University, London, Ontario, Canada.,Center for Functional and Metabolic Mapping, Western University, London, Ontario, Canada
| | - Desmond H Y Tse
- Center for Functional and Metabolic Mapping, Western University, London, Ontario, Canada
| | - Paul I Dubovan
- Department of Medical Biophysics, Western University, London, Ontario, Canada.,Center for Functional and Metabolic Mapping, Western University, London, Ontario, Canada
| | - Corey A Baron
- Department of Medical Biophysics, Western University, London, Ontario, Canada.,Center for Functional and Metabolic Mapping, Western University, London, Ontario, Canada.,Imaging Laboratories, Robarts Research Institute, London, Ontario, Canada
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9
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Alexandris AS, Wang Y, Frangakis CE, Lee Y, Ryu J, Alam Z, Koliatsos VE. Long-Term Changes in Axon Calibers after Injury: Observations on the Mouse Corticospinal Tract. Int J Mol Sci 2022; 23:7391. [PMID: 35806394 PMCID: PMC9266552 DOI: 10.3390/ijms23137391] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/24/2022] [Accepted: 06/29/2022] [Indexed: 02/01/2023] Open
Abstract
White matter pathology is common across a wide spectrum of neurological diseases. Characterizing this pathology is important for both a mechanistic understanding of neurological diseases as well as for the development of neuroimaging biomarkers. Although axonal calibers can vary by orders of magnitude, they are tightly regulated and related to neuronal function, and changes in axon calibers have been reported in several diseases and their models. In this study, we utilize the impact acceleration model of traumatic brain injury (IA-TBI) to assess early and late changes in the axon diameter distribution (ADD) of the mouse corticospinal tract using Airyscan and electron microscopy. We find that axon calibers follow a lognormal distribution whose parameters significantly change after injury. While IA-TBI leads to 30% loss of corticospinal axons by day 7 with a bias for larger axons, at 21 days after injury we find a significant redistribution of axon frequencies that is driven by a reduction in large-caliber axons in the absence of detectable degeneration. We postulate that changes in ADD features may reflect a functional adaptation of injured neural systems. Moreover, we find that ADD features offer an accurate way to discriminate between injured and non-injured mice. Exploring injury-related ADD signatures by histology or new emerging neuroimaging modalities may offer a more nuanced and comprehensive way to characterize white matter pathology and may also have the potential to generate novel biomarkers of injury.
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Affiliation(s)
- Athanasios S. Alexandris
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA; (Y.W.); (Y.L.); (J.R.); (Z.A.)
| | - Yiqing Wang
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA; (Y.W.); (Y.L.); (J.R.); (Z.A.)
| | | | - Youngrim Lee
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA; (Y.W.); (Y.L.); (J.R.); (Z.A.)
| | - Jiwon Ryu
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA; (Y.W.); (Y.L.); (J.R.); (Z.A.)
| | - Zahra Alam
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA; (Y.W.); (Y.L.); (J.R.); (Z.A.)
| | - Vassilis E. Koliatsos
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA; (Y.W.); (Y.L.); (J.R.); (Z.A.)
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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10
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Mollon JD, Takahashi C, Danilova MV. What kind of network is the brain? Trends Cogn Sci 2022; 26:312-324. [PMID: 35216895 DOI: 10.1016/j.tics.2022.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 01/23/2022] [Accepted: 01/27/2022] [Indexed: 11/27/2022]
Abstract
The different areas of the cerebral cortex are linked by a network of white matter, comprising the myelinated axons of pyramidal cells. Is this network a neural net, in the sense that representations of the world are embodied in the structure of the net, its pattern of nodes, and connections? Or is it a communications network, where the same physical substrate carries different information from moment to moment? This question is part of the larger question of whether the brain is better modeled by connectionism or by symbolic artificial intelligence (AI), but we review it in the specific context of the psychophysics of stimulus comparison and the format and protocol of information transmission over the long-range tracts of the brain.
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Affiliation(s)
- John D Mollon
- Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK; I.P. Pavlov Institute of Physiology, St. Petersburg, Russia.
| | - Chie Takahashi
- Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK
| | - Marina V Danilova
- Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK; I.P. Pavlov Institute of Physiology, St. Petersburg, Russia
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11
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Tian Q, Fan Q, Witzel T, Polackal MN, Ohringer NA, Ngamsombat C, Russo AW, Machado N, Brewer K, Wang F, Setsompop K, Polimeni JR, Keil B, Wald LL, Rosen BR, Klawiter EC, Nummenmaa A, Huang SY. Comprehensive diffusion MRI dataset for in vivo human brain microstructure mapping using 300 mT/m gradients. Sci Data 2022; 9:7. [PMID: 35042861 PMCID: PMC8766594 DOI: 10.1038/s41597-021-01092-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 10/25/2021] [Indexed: 12/27/2022] Open
Abstract
Strong gradient systems can improve the signal-to-noise ratio of diffusion MRI measurements and enable a wider range of acquisition parameters that are beneficial for microstructural imaging. We present a comprehensive diffusion MRI dataset of 26 healthy participants acquired on the MGH-USC 3 T Connectome scanner equipped with 300 mT/m maximum gradient strength and a custom-built 64-channel head coil. For each participant, the one-hour long acquisition systematically sampled the accessible diffusion measurement space, including two diffusion times (19 and 49 ms), eight gradient strengths linearly spaced between 30 mT/m and 290 mT/m for each diffusion time, and 32 or 64 uniformly distributed directions. The diffusion MRI data were preprocessed to correct for gradient nonlinearity, eddy currents, and susceptibility induced distortions. In addition, scan/rescan data from a subset of seven individuals were also acquired and provided. The MGH Connectome Diffusion Microstructure Dataset (CDMD) may serve as a test bed for the development of new data analysis methods, such as fiber orientation estimation, tractography and microstructural modelling.
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Affiliation(s)
- Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Thomas Witzel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Maya N Polackal
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
| | - Ned A Ohringer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
| | - Chanon Ngamsombat
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
| | - Andrew W Russo
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States
| | - Natalya Machado
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States
| | - Kristina Brewer
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Boris Keil
- Department of Life Science Engineering, Institute of Medical Physics and Radiation Protection, Giessen, Germany
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Eric C Klawiter
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States.
- Harvard Medical School, Boston, Massachusetts, United States.
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States.
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12
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Rahman N, Xu K, Omer M, Budde MD, Brown A, Baron CA. Test-retest reproducibility of in vivo oscillating gradient and microscopic anisotropy diffusion MRI in mice at 9.4 Tesla. PLoS One 2021; 16:e0255711. [PMID: 34739479 PMCID: PMC8570471 DOI: 10.1371/journal.pone.0255711] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 10/22/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND AND PURPOSE Microstructure imaging with advanced diffusion MRI (dMRI) techniques have shown increased sensitivity and specificity to microstructural changes in various disease and injury models. Oscillating gradient spin echo (OGSE) dMRI, implemented by varying the oscillating gradient frequency, and microscopic anisotropy (μA) dMRI, implemented via tensor valued diffusion encoding, may provide additional insight by increasing sensitivity to smaller spatial scales and disentangling fiber orientation dispersion from true microstructural changes, respectively. The aims of this study were to characterize the test-retest reproducibility of in vivo OGSE and μA dMRI metrics in the mouse brain at 9.4 Tesla and provide estimates of required sample sizes for future investigations. METHODS Twelve adult C57Bl/6 mice were scanned twice (5 days apart). Each imaging session consisted of multifrequency OGSE and μA dMRI protocols. Metrics investigated included μA, linear diffusion kurtosis, isotropic diffusion kurtosis, and the diffusion dispersion rate (Λ), which explores the power-law frequency dependence of mean diffusivity. The dMRI metric maps were analyzed with mean region-of-interest (ROI) and whole brain voxel-wise analysis. Bland-Altman plots and coefficients of variation (CV) were used to assess the reproducibility of OGSE and μA metrics. Furthermore, we estimated sample sizes required to detect a variety of effect sizes. RESULTS Bland-Altman plots showed negligible biases between test and retest sessions. ROI-based CVs revealed high reproducibility for most metrics (CVs < 15%). Voxel-wise CV maps revealed high reproducibility for μA (CVs ~ 10%), but low reproducibility for OGSE metrics (CVs ~ 50%). CONCLUSION Most of the μA dMRI metrics are reproducible in both ROI-based and voxel-wise analysis, while the OGSE dMRI metrics are only reproducible in ROI-based analysis. Given feasible sample sizes (10-15), μA metrics and OGSE metrics may provide sensitivity to subtle microstructural changes (4-8%) and moderate changes (> 6%), respectively.
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Affiliation(s)
- Naila Rahman
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Kathy Xu
- Translational Neuroscience Group, Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Mohammad Omer
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Matthew D. Budde
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Arthur Brown
- Translational Neuroscience Group, Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
- Department of Anatomy and Cell Biology, University of Western Ontario, London, Ontario, Canada
| | - Corey A. Baron
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
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13
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Barakovic M, Girard G, Schiavi S, Romascano D, Descoteaux M, Granziera C, Jones DK, Innocenti GM, Thiran JP, Daducci A. Bundle-Specific Axon Diameter Index as a New Contrast to Differentiate White Matter Tracts. Front Neurosci 2021; 15:646034. [PMID: 34211362 PMCID: PMC8239216 DOI: 10.3389/fnins.2021.646034] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 05/17/2021] [Indexed: 12/30/2022] Open
Abstract
In the central nervous system of primates, several pathways are characterized by different spectra of axon diameters. In vivo methods, based on diffusion-weighted magnetic resonance imaging, can provide axon diameter index estimates non-invasively. However, such methods report voxel-wise estimates, which vary from voxel-to-voxel for the same white matter bundle due to partial volume contributions from other pathways having different microstructure properties. Here, we propose a novel microstructure-informed tractography approach, COMMITAxSize, to resolve axon diameter index estimates at the streamline level, thus making the estimates invariant along trajectories. Compared to previously proposed voxel-wise methods, our formulation allows the estimation of a distinct axon diameter index value for each streamline, directly, furnishing a complementary measure to the existing calculation of the mean value along the bundle. We demonstrate the favourable performance of our approach comparing our estimates with existing histologically-derived measurements performed in the corpus callosum and the posterior limb of the internal capsule. Overall, our method provides a more robust estimation of the axon diameter index of pathways by jointly estimating the microstructure properties of the tissue and the macroscopic organisation of the white matter connectivity.
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Affiliation(s)
- Muhamed Barakovic
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- 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
| | - Gabriel Girard
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- CIBM Center for BioMedical Imaging, Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Simona Schiavi
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Computer Science, University of Verona, Verona, Italy
| | - David Romascano
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - 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
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia
| | - Giorgio M. Innocenti
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Brain and Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- CIBM Center for BioMedical Imaging, Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
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