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Friesen E, Chisholm M, Dhakal B, Mercredi M, Does MD, Gore JC, Martin M. Modelling white matter microstructure using diffusion OGSE MRI: Model and analysis choices. Magn Reson Imaging 2024; 113:110221. [PMID: 39173962 DOI: 10.1016/j.mri.2024.110221] [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: 07/16/2024] [Revised: 07/31/2024] [Accepted: 08/19/2024] [Indexed: 08/24/2024]
Abstract
Alterations in white matter (WM) microstructure of the central nervous system have been shown to be pathophysiological presentations of various neurodegenerative disorders. Current methods for measuring such WM features require ex vivo tissue samples analyzed using electron microscopy. Magnetic Resonance Imaging (MRI) diffusion-weighted pulse sequences provide a non-invasive tool for estimating such microstructural features in vivo. The current project investigated the use of two methods of analysis, including the ROI-based (Region of Interest, RBA) and voxel-based analysis (VBA), as well as four mathematical models of WM microstructure, including the ActiveAx Frequency-Independent Extra-Axonal Diffusion (AAI), ActiveAx Frequency-Dependent Extra-Axonal Diffusion (AAD), AxCaliber Frequency-Independent Extra-Axonal Diffusion (ACI), and AxCaliber Frequency-Dependent Extra-Axonal Diffusion (ACD) models. Two mice samples imaged at 7 T and 15.2 T were analyzed. Both the AAI and AAD models provide a single value for each of the fit parameters, including mean effective axon diameter AxD¯, packing fraction fin, intra-cellular and Din and extra-cellular Dex diffusion coefficients, as well as the frequency dependence of Dex, βex for the AAD model. The ACI and ACD models provide this, in addition to a distribution of axon diameters for a chosen ROI. VBA extends this, providing a parameter value for each voxel within the selected ROI, at the cost of increased computational load and analysis time. Overall, RBA-ACD and VBA-AAD were found to be optimal for parameter fitting to physically relevant values in a reasonable time frame. A full comparison of each combination of RBA and VBA with AAI, AAD, ACI, and ACD is provided to give the reader sufficient information to make an informed decision of which model is best for their own experiments.
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Affiliation(s)
- Emma Friesen
- Department of Chemistry, University of Winnipeg, Winnipeg, MB, Canada.
| | - Madison Chisholm
- Department of Biology, University of Winnipeg, Winnipeg, MB, Canada.
| | - Bibek Dhakal
- Vanderbilt Institute of Imaging Science, Vanderbilt University Medical Centre, Nashville, TN, USA.
| | - Morgan Mercredi
- Department of Physics, University of Winnipeg, Winnipeg, MB, Canada
| | - Mark D Does
- Vanderbilt Institute of Imaging Science, Vanderbilt University Medical Centre, Nashville, TN, USA.
| | - John C Gore
- Vanderbilt Institute of Imaging Science, Vanderbilt University Medical Centre, Nashville, TN, USA.
| | - Melanie Martin
- Department of Physics, University of Winnipeg, Winnipeg, MB, Canada.
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2
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Zhang J, Wang Y, Shu Z, Ouyang Y, Zhang X, Wang H, Zhang L, Fang S, Ye X, Li J. Tracing volitional recovery in post-stroke akinetic mutism using longitudinal microstructure imaging: Insights from a single case study. Cortex 2024; 180:55-63. [PMID: 39369575 DOI: 10.1016/j.cortex.2024.09.004] [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: 02/20/2024] [Revised: 06/23/2024] [Accepted: 09/06/2024] [Indexed: 10/08/2024]
Abstract
Lesions in the frontal-subcortical circuitry can lead to akinetic mutism (AM) characterized by diminished volition. However, the microstructural changes in the damaged network underlying its recovery remain unknown. Clinical examination and neuropsychological assessment were performed on a patient with post-stroke AM. Multimodal MRI scans were performed at baseline and follow-ups. We used diffusion MRI and biophysical models, specifically utilizing neurite orientation dispersion and density imaging for assessing gray matter microstructure, and fixel-based analysis for the evaluation of white matter. Longitudinal comparisons were performed between the patient and healthy controls. Pronounced recovery of volition was observed after dopamine agonist therapy combined with physical therapy. In addition to infarcts in the bilateral medial cortex, microstructure imaging detected reduced neurite density in extensive areas, specifically in temporal areas and subcortical nuclei, and decreased fiber density of white matter tracts (TFCE-corrected p < .05). Microstructural degeneration in the anterior cingulate cortex and cingulum was relatively persistent (Bonferroni-corrected p < .05). However, most tracts within the frontal-subcortical circuitry showed increased fiber density during the recovery stage. Microstructure of an extensive network may contribute to the disruption and recovery of volition. Fiber density within the frontal-subcortical circuitry could be a promising biomarker indicating volitional recovery.
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Affiliation(s)
- Jie Zhang
- Center for Rehabilitation Medicine, Rehabilitation and Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, China; Wellcome Center for Human Neuroimaging, Department of Imaging Neuroscience, Institute of Neurology, University College London, London, UK
| | - Yingqiao Wang
- Center for Rehabilitation Medicine, Rehabilitation and Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, China
| | - Zhenyu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yao Ouyang
- Center for Rehabilitation Medicine, Rehabilitation and Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, China
| | - Xingru Zhang
- Center for Rehabilitation Medicine, Rehabilitation and Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, China
| | - Huiqi Wang
- Center for Rehabilitation Medicine, Rehabilitation and Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, China
| | - Li Zhang
- Center for Rehabilitation Medicine, Rehabilitation and Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, China
| | - Shan Fang
- Center for Rehabilitation Medicine, Rehabilitation and Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, China
| | - Xiangming Ye
- Center for Rehabilitation Medicine, Rehabilitation and Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, China.
| | - Juebao Li
- Center for Rehabilitation Medicine, Rehabilitation and Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, China.
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Würtemberger U, Rau A, Diebold M, Becker L, Hohenhaus M, Beck J, Reinacher PC, Erny D, Reisert M, Urbach H, Demerath T. Advanced diffusion MRI provides evidence for altered axonal microstructure and gradual peritumoral infiltration in GBM in comparison to brain metastases. Clin Neuroradiol 2024; 34:703-711. [PMID: 38683350 PMCID: PMC11339137 DOI: 10.1007/s00062-024-01416-0] [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: 12/16/2023] [Accepted: 04/15/2024] [Indexed: 05/01/2024]
Abstract
PURPOSE In contrast to peritumoral edema in metastases, GBM is histopathologically characterized by infiltrating tumor cells within the T2 signal alterations. We hypothesized that depending on the distance from the outline of the contrast-enhancing tumor we might reveal imaging evidence of gradual peritumoral infiltration in GBM and predominantly vasogenic edema around metastases. We thus investigated the gradual change of advanced diffusion metrics with the peritumoral zone in metastases and GBM. METHODS In 30 patients with GBM and 28 with brain metastases, peritumoral T2 hyperintensity was segmented in 33% partitions based on the total volume beginning at the enhancing tumor margin and divided into inner, middle and outer zones. Diffusion Tensor Imaging (DTI)-derived fractional anisotropy and mean diffusivity as well as Diffusion Microstructure Imaging (DMI)-based parameters Dax-intra, Dax-extra, V‑CSF and V-intra were employed to assess group-wise differences between inner and outer zones as well as within-group gradients between the inner and outer zones. RESULTS In metastases, fractional anisotropy and Dax-extra were significantly reduced in the inner zone compared to the outer zone (FA p = 0.01; Dax-extra p = 0.03). In GBM, we noted a reduced Dax-extra and significantly lower intraaxonal volume fraction (Dax-extra p = 0.008, V‑intra p = 0.006) accompanied by elevated axial intraaxonal diffusivity in the inner zone (p = 0.035). Between-group comparison of the outer to the inner zones revealed significantly higher gradients in metastases over GBM for FA (p = 0.04) as well as the axial diffusivity in the intra- (p = 0.02) and extraaxonal compartment (p < 0.001). CONCLUSION Our findings provide evidence of gradual alterations within the peritumoral zone of brain tumors. These are compatible with predominant (vasogenic) edema formation in metastases, whereas our findings in GBM are in line with an axonal destructive component in the immediate peritumoral area and evidence of tumor cell infiltration with accentuation in the tumor's vicinity.
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Affiliation(s)
- U Würtemberger
- Department of Neuroradiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany.
- Dept. of Neuroradiology, University Medical Center Freiburg, Breisacher Str. 64, 79106, Freiburg, Germany.
| | - A Rau
- Department of Neuroradiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
- Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
| | - M Diebold
- Institute of Neuropathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
| | - L Becker
- Department of Neuroradiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
| | - M Hohenhaus
- Department of Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
| | - J Beck
- Department of Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
| | - P C Reinacher
- Department of Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
- Fraunhofer Institute for Laser Technology, 52074, Aachen, Germany
| | - D Erny
- Institute of Neuropathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
| | - M Reisert
- Department of Medical Physics, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
- Department of Stereotactic and Functional Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
| | - H Urbach
- Department of Neuroradiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
| | - T Demerath
- Department of Neuroradiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
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4
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Herbet G, Duffau H, Mandonnet E. Predictors of cognition after glioma surgery: connectotomy, structure-function phenotype, plasticity. Brain 2024; 147:2621-2635. [PMID: 38573324 DOI: 10.1093/brain/awae093] [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: 12/02/2023] [Revised: 02/19/2024] [Accepted: 03/09/2024] [Indexed: 04/05/2024] Open
Abstract
Determining preoperatively the maximal extent of resection that would preserve cognitive functions is the core challenge of brain tumour surgery. Over the past decade, the methodological framework to achieve this goal has been thoroughly renewed: the population-level topographically-focused voxel-based lesion-symptom mapping has been progressively overshadowed by machine learning (ML) algorithmics, in which the problem is framed as predicting cognitive outcomes in a patient-specific manner from a typically large set of variables. However, the choice of these predictors is of utmost importance, as they should be both informative and parsimonious. In this perspective, we first introduce the concept of connectotomy: instead of parameterizing resection topography through the status (intact/resected) of a huge number of voxels (or parcels) paving the whole brain in the Cartesian 3D-space, the connectotomy models the resection in the connectivity space, by computing a handful number of networks disconnection indices, measuring how the structural connectivity sustaining each network of interest was hit by the resection. This connectivity-informed reduction of dimensionality is a necessary step for efficiently implementing ML tools, given the relatively small number of patient-examples in available training datasets. We further argue that two other major sources of interindividual variability must be considered to improve the accuracy with which outcomes are predicted: the underlying structure-function phenotype and neuroplasticity, for which we provide an in-depth review and propose new ways of determining relevant predictors. We finally discuss the benefits of our approach for precision surgery of glioma.
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Affiliation(s)
- Guillaume Herbet
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier 34090, France
- Praxiling lab, UMR5267 CNRS & Paul Valéry University, Montpellier 34090, France
- Department of Medicine, University of Montpellier, Montpellier 34090, France
- Institut Universitaire de France, Paris 75000, France
| | - Hugues Duffau
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier 34090, France
- Department of Medicine, University of Montpellier, Montpellier 34090, France
- Team 'Plasticity of Central Nervous System, Stem Cells and Glial Tumors', U1191 Laboratory, Institute of Functional Genomics, National Institute for Health and Medical Research (INSERM), University of Montpellier, Montpellier 34000, France
| | - Emmanuel Mandonnet
- Department of Neurosurgery, Lariboisière Hospital, AP-HP, Paris 75010, France
- Frontlab, CNRS UMR 7225, INSERM U1127, Paris Brain Institute (ICM), Paris 75013, France
- Université de Paris Cité, UFR de médecine, Paris 75005, France
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5
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Bourke G, Wade RG, van Alfen N. Updates in diagnostic tools for diagnosing nerve injury and compressions. J Hand Surg Eur Vol 2024; 49:668-680. [PMID: 38534079 DOI: 10.1177/17531934241238736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
Predicting prognosis after nerve injury and compression can be challenging, even for the experienced clinician. Although thorough clinical assessment can aid diagnosis, we cannot always be precise about long-term functional recovery of either motor or sensory nerves. To evaluate the severity of nerve injury, surgical exploration remains the gold standard, particularly after iatrogenic injury and major nerve injury from trauma, such as brachial plexus injury. Recently, advances in imaging techniques (ultrasound, magnetic resonance imaging [MRI] and MR neurography) along with multimodality assessment, including electrodiagnostic testing, have allowed us to have a better preoperative understanding of nerve continuity and prediction of nerve health and possible recovery. This article outlines the current and potential roles for clinical assessment, exploratory surgery, electrodiagnostic testing ultrasound and MRI in entrapment neuropathies, inflammatory neuritis and trauma. Emphasis is placed on those modalities that are improving in diagnostic accuracy of nerve assessment before any surgical intervention.
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Affiliation(s)
- Gráinne Bourke
- Leeds Institute for Medical Research, University of Leeds, Leeds, UK
- Department of Plastic and Reconstructive Surgery, Leeds Teaching Hospitals Trust, Leeds, UK
| | - Ryckie G Wade
- Leeds Institute for Medical Research, University of Leeds, Leeds, UK
- Department of Plastic and Reconstructive Surgery, Leeds Teaching Hospitals Trust, Leeds, UK
| | - Nens van Alfen
- Department of Neurology, Clinical Neuromuscular Imaging Group, Donders Centre for Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
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6
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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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7
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Reveley C, Ye FQ, Leopold DA. Diffusion kurtosis MRI tracks gray matter myelin content in the primate cerebral cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.08.584058. [PMID: 38496676 PMCID: PMC10942417 DOI: 10.1101/2024.03.08.584058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Diffusion magnetic resonance imaging (dMRI) has been widely employed to model the trajectory of myelinated fiber bundles in white matter. Increasingly, dMRI is also used to assess local tissue properties throughout the brain. In the cerebral cortex, myelin content is a critical indicator of the maturation, regional variation, and disease related degeneration of gray matter tissue. Gray matter myelination can be measured and mapped using several non-diffusion MRI strategies; however, first order diffusion statistics such as fractional anisotropy (FA) show only weak spatial correlation with cortical myelin content. Here we show that a simple higher order diffusion parameter, the mean diffusion kurtosis (MK), is strongly correlated with the laminar and regional variation of myelin in the primate cerebral cortex. We carried out ultra-high resolution, multi-shelled dMRI in ex vivo marmoset monkey brains and compared dMRI parameters from a number of higher order models (diffusion kurtosis, NODDI and MAP MRI) to the distribution of myelin obtained using histological staining, and via Magnetization Transfer Ratio MRI (MTR), a non-diffusion MRI method. In contrast to FA, MK closely matched the myelin content assessed by histology and by MTR in the same sample. The parameter maps from MAP-MRI and NODDI also showed good correspondence with cortical myelin content. The results demonstrate that dMRI can be used to assess the variation of local myelin content in the primate cortical cortex, which may be of great value for assessing tissue integrity and tracking disease in living human patients.
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Affiliation(s)
- Colin Reveley
- Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU, UK
| | - Frank Q Ye
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD
| | - David A Leopold
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD
- Section on Cognitive Neurophysiology and Imaging, Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD
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8
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Würtemberger U, Diebold M, Rau A, Akgün V, Becker L, Beck J, Reinacher PC, Taschner CA, Reisert M, Fehrenbacher L, Erny D, Scherer F, Hohenhaus M, Urbach H, Demerath T. Advanced diffusion imaging reveals microstructural characteristics of primary CNS lymphoma, allowing differentiation from glioblastoma. Neurooncol Adv 2024; 6:vdae093. [PMID: 38946879 PMCID: PMC11214103 DOI: 10.1093/noajnl/vdae093] [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] [Indexed: 07/02/2024] Open
Abstract
Background Primary CNS lymphoma (PCNSL) and glioblastoma (GBM) both represent frequent intracranial malignancies with differing clinical management. However, distinguishing PCNSL from GBM with conventional MRI can be challenging when atypical imaging features are present. We employed advanced dMRI for noninvasive characterization of the microstructure of PCNSL and differentiation from GBM as the most frequent primary brain malignancy. Methods Multiple dMRI metrics including Diffusion Tensor Imaging, Neurite Orientation Dispersion and Density Imaging, and Diffusion Microstructure Imaging were extracted from the contrast-enhancing tumor component in 10 PCNSL and 10 age-matched GBM on 3T MRI. Imaging findings were correlated with cell density and axonal markers obtained from histopathology. Results We found significantly increased intra-axonal volume fractions (V-intra and intracellular volume fraction) and microFA in PCNSL compared to GBM (all P < .001). In contrast, mean diffusivity (MD), axial diffusivity (aD), and microADC (all P < .001), and also free water fractions (V-CSF and V-ISO) were significantly lower in PCNSL (all P < .01). Receiver-operating characteristic analysis revealed high predictive values regarding the presence of a PCNSL for MD, aD, microADC, V-intra, ICVF, microFA, V-CSF, and V-ISO (area under the curve [AUC] in all >0.840, highest for MD and ICVF with an AUC of 0.960). Comparative histopathology between PCNSL and GBM revealed a significantly increased cell density in PCNSL and the presence of axonal remnants in a higher proportion of samples. Conclusions Advanced diffusion imaging enables the characterization of the microstructure of PCNSL and reliably distinguishes PCNSL from GBM. Both imaging and histopathology revealed a relatively increased cell density and a preserved axonal microstructure in PCNSL.
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Affiliation(s)
- Urs Würtemberger
- Department of Neuroradiology, Medical Center—University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Martin Diebold
- Institute of Neuropathology, Medical Center—University of Freiburg, University of Freiburg, Freiburg, Germany
- IMM-PACT Clinician Scientist Program, University of Freiburg, Freiburg, Germany
| | - Alexander Rau
- Department of Neuroradiology, Medical Center—University of Freiburg, University of Freiburg, Freiburg, Germany
- Department of Diagnostic and Interventional Radiology, Medical Center—University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Veysel Akgün
- Department of Neuroradiology, Medical Center—University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Lucas Becker
- Department of Neuroradiology, Medical Center—University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Jürgen Beck
- Department of Neurosurgery, Medical Center—University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Peter C Reinacher
- Fraunhofer Institute for Laser Technology, Aachen, Germany
- Department of Stereotactic and Functional Neurosurgery, Medical Center—University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Christian A Taschner
- Department of Neuroradiology, Medical Center—University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Marco Reisert
- Department of Stereotactic and Functional Neurosurgery, Medical Center—University of Freiburg, University of Freiburg, Freiburg, Germany
- Department of Medical Physics, Medical Center—University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Luca Fehrenbacher
- Institute of Neuropathology, Medical Center—University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Daniel Erny
- Institute of Neuropathology, Medical Center—University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Florian Scherer
- Department of Medicine I, Medical Center—University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Marc Hohenhaus
- Department of Neurosurgery, Medical Center—University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Horst Urbach
- Department of Neuroradiology, Medical Center—University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Theo Demerath
- Department of Neuroradiology, Medical Center—University of Freiburg, University of Freiburg, Freiburg, Germany
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9
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Chakwizira A, Zhu A, Foo T, Westin CF, Szczepankiewicz F, Nilsson M. Diffusion MRI with free gradient waveforms on a high-performance gradient system: Probing restriction and exchange in the human brain. Neuroimage 2023; 283:120409. [PMID: 37839729 DOI: 10.1016/j.neuroimage.2023.120409] [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: 04/05/2023] [Revised: 09/29/2023] [Accepted: 10/12/2023] [Indexed: 10/17/2023] Open
Abstract
The dependence of the diffusion MRI signal on the diffusion time carries signatures of restricted diffusion and exchange. Here we seek to highlight these signatures in the human brain by performing experiments using free gradient waveforms designed to be selectively sensitive to the two effects. We examine six healthy volunteers using both strong and ultra-strong gradients (80, 200 and 300 mT/m). In an experiment featuring a large set of 150 gradient waveforms with different sensitivities to restricted diffusion and exchange, our results reveal unique and different time-dependence signatures in grey and white matter. Grey matter was characterised by both restricted diffusion and exchange and white matter predominantly by restricted diffusion. Exchange in grey matter was at least twice as fast as in white matter, across all subjects and all gradient strengths. The cerebellar cortex featured relatively short exchange times (115 ms). Furthermore, we show that gradient waveforms with tailored designs can be used to map exchange in the human brain. We also assessed the feasibility of clinical applications of the method used in this work and found that the exchange-related contrast obtained with a 25-minute protocol at 300 mT/m was preserved in a 4-minute protocol at 300 mT/m and a 10-minute protocol at 80 mT/m. Our work underlines the utility of free waveforms for detecting time dependence signatures due to restricted diffusion and exchange in vivo, which may potentially serve as a tool for studying diseased tissue.
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Affiliation(s)
- Arthur Chakwizira
- Department of Medical Radiation Physics, Clinical Sciences Lund, Lund University, Lund, Sweden.
| | - Ante Zhu
- GE Research, Niskayuna, New York, United States
| | - Thomas Foo
- GE Research, Niskayuna, New York, United States
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Filip Szczepankiewicz
- Department of Medical Radiation Physics, Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Markus Nilsson
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden; Department of Radiology, Skåne University Hospital, Lund, Sweden
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10
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Dai E, Zhu A, Yang GK, Quah K, Tan ET, Fiveland E, Foo TKF, McNab JA. Frequency-dependent diffusion kurtosis imaging in the human brain using an oscillating gradient spin echo sequence and a high-performance head-only gradient. Neuroimage 2023; 279:120328. [PMID: 37586445 PMCID: PMC10529993 DOI: 10.1016/j.neuroimage.2023.120328] [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: 03/15/2023] [Revised: 07/17/2023] [Accepted: 08/12/2023] [Indexed: 08/18/2023] Open
Abstract
Measuring the time/frequency dependence of diffusion MRI is a promising approach to distinguish between the effects of different tissue microenvironments, such as membrane restriction, tissue heterogeneity, and compartmental water exchange. In this study, we measure the frequency dependence of diffusivity (D) and kurtosis (K) with oscillating gradient diffusion encoding waveforms and a diffusion kurtosis imaging (DKI) model in human brains using a high-performance, head-only MAGNUS gradient system, with a combination of b-values, oscillating frequencies (f), and echo time that has not been achieved in human studies before. Frequency dependence of diffusivity and kurtosis are observed in both global and local white matter (WM) and gray matter (GM) regions and characterized with a power-law model ∼Λ*fθ. The frequency dependences of diffusivity and kurtosis (including changes between fmin and fmax, Λ, and θ) vary over different WM and GM regions, indicating potential microstructural differences between regions. A trend of decreasing kurtosis over frequency in the short-time limit is successfully captured for in vivo human brains. The effects of gradient nonlinearity (GNL) on frequency-dependent diffusivity and kurtosis measurements are investigated and corrected. Our results show that the GNL has prominent scaling effects on the measured diffusivity values (3.5∼5.5% difference in the global WM and 6∼8% difference in the global cortex) and subsequently affects the corresponding power-law parameters (Λ, θ) while having a marginal influence on the measured kurtosis values (<0.05% difference) and power-law parameters (Λ, θ). This study expands previous OGSE studies and further demonstrates the translatability of frequency-dependent diffusivity and kurtosis measurements to human brains, which may provide new opportunities to probe human brain microstructure in health and disease.
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Affiliation(s)
- Erpeng Dai
- Department of Radiology, Stanford University, Stanford, CA, USA.
| | | | - Grant K Yang
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Kristin Quah
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Ek T Tan
- Department of Radiology and Imaging, Hospital for Special Surgery, New York, NY, USA
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11
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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 influence of axonal beading and undulation on axonal diameter mapping. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.19.537494. [PMID: 37131702 PMCID: PMC10153226 DOI: 10.1101/2023.04.19.537494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
We consider the effect of non-cylindrical axonal shape on axonal diameter mapping with diffusion MRI. Practical sensitivity to axon diameter is attained at strong diffusion weightings b , where the deviation from the 1 / b scaling yields the finite transverse diffusivity, which is then translated into axon diameter. While axons are usually modeled as perfectly straight, impermeable cylinders, the local variations in diameter (caliber variation or beading) and direction (undulation) have been observed in microscopy data of human axons. Here we quantify the influence of cellular-level features such as caliber variation and undulation on axon diameter estimation. For that, we simulate the diffusion MRI signal in realistic axons segmented from 3-dimensional electron microscopy of a human brain sample. We then create artificial fibers with the same features and tune the amplitude of their caliber variations and undulations. Numerical simulations of diffusion in fibers with such tunable features show that caliber variations and undulations result in under- and over-estimation of axon diameters, correspondingly; this bias can be as large as 100%. 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, MA 02129,USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129,USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Maxina Sheft
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129,USA
- Harvard-MIT Health Sciences and Technology, Cambridge, MA 02139, USA
| | - Ricardo Coronado-Leija
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY 10016, USA
| | - Gabriel Ramos-Llorden
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129,USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Ali Abdollahzadeh
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY 10016, USA
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY 10016, USA
| | - Dmitry S. Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY 10016, USA
| | - Susie Y. Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129,USA
- Harvard Medical School, Boston, MA 02115, USA
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12
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Chakwizira A, Zhu A, Foo T, Westin CF, Szczepankiewicz F, Nilsson M. Diffusion MRI with free gradient waveforms on a high-performance gradient system: Probing restriction and exchange in the human brain. ARXIV 2023:arXiv:2304.02764v1. [PMID: 37064535 PMCID: PMC10104199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
The dependence of the diffusion MRI signal on the diffusion time carries signatures of restricted diffusion and exchange. Here we seek to highlight these signatures in the human brain by performing experiments using free gradient waveforms that are selectively sensitive to the two effects. We examine six healthy volunteers using both strong and ultra-strong gradients (80, 200 and 300 mT/m). In an experiment featuring a large set of gradient waveforms with different sensitivities to restricted diffusion and exchange (150 samples), our results reveal unique time-dependence signatures in grey and white matter, where the former is characterised by both restricted diffusion and exchange and the latter predominantly exhibits restricted diffusion. Furthermore, we show that gradient waveforms with independently varying sensitivities to restricted diffusion and exchange can be used to map exchange in the human brain. We consistently find that exchange in grey matter is at least twice as fast as in white matter, across all subjects and all gradient strengths. The shortest exchange times observed in this study were in the cerebellar cortex (115 ms). We also assess the feasibility of future clinical applications of the method used in this work, where we find that the grey-white matter exchange contrast obtained with a 25-minute 300 mT/m protocol is preserved by a 4-minute 300 mT/m and a 10-minute 80 mT/m protocol. Our work underlines the utility of free waveforms for detecting time-dependence signatures due to restricted diffusion and exchange in vivo, which may potentially serve as a tool for studying diseased tissue.
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Affiliation(s)
- Arthur Chakwizira
- Medical Radiation Physics, Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Ante Zhu
- GE Research, Niskayuna, New York, USA
| | | | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | | | - Markus Nilsson
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
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13
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Bergamino M, Nelson MR, Numani A, Scarpelli M, Healey D, Fuentes A, Turner G, Stokes AM. Assessment of complementary white matter microstructural changes and grey matter atrophy in a preclinical model of Alzheimer's disease. Magn Reson Imaging 2023; 101:57-66. [PMID: 37028608 DOI: 10.1016/j.mri.2023.03.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/30/2023] [Accepted: 03/31/2023] [Indexed: 04/08/2023]
Abstract
Alzheimer's disease (AD) has been associated with amyloid and tau pathology, as well as neurodegeneration. Beyond these hallmark features, white matter microstructural abnormalities have been observed using MRI. The objective of this study was to assess grey matter atrophy and white matter microstructural changes in a preclinical mouse model of AD (3xTg-AD) using voxel-based morphometry (VBM) and free-water (FW) diffusion tensor imaging (FW-DTI). Compared to controls, lower grey matter density was observed in the 3xTg-AD model, corresponding to the small clusters in the caudate-putamen, hypothalamus, and cortex. DTI-based fractional anisotropy (FA) was decreased in the 3xTg model, while the FW index was increased. Notably, the largest clusters for both FW-FA and FW index were in the fimbria, with other regions including the anterior commissure, corpus callosum, forebrain septum, and internal capsule. Additionally, the presence of amyloid and tau in the 3xTg model was confirmed with histopathology, with significantly higher levels observed across many regions of the brain. Taken together, these results are consistent with subtle neurodegenerative and white matter microstructural changes in the 3xTg-AD model that manifest as increased FW, decreased FW-FA, and decreased grey matter density.
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Affiliation(s)
- Maurizio Bergamino
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Megan R Nelson
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Asfia Numani
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Matthew Scarpelli
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Deborah Healey
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Alberto Fuentes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Gregory Turner
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Ashley M Stokes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA.
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14
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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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15
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Jelescu IO, de Skowronski A, Geffroy F, Palombo M, Novikov DS. Neurite Exchange Imaging (NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange. Neuroimage 2022; 256:119277. [PMID: 35523369 PMCID: PMC10363376 DOI: 10.1016/j.neuroimage.2022.119277] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 04/26/2022] [Accepted: 05/01/2022] [Indexed: 01/18/2023] Open
Abstract
Biophysical models of diffusion in white matter have been center-stage over the past two decades and are essentially based on what is now commonly referred to as the "Standard Model" (SM) of non-exchanging anisotropic compartments with Gaussian diffusion. In this work, we focus on diffusion MRI in gray matter, which requires rethinking basic microstructure modeling blocks. In particular, at least three contributions beyond the SM need to be considered for gray matter: water exchange across the cell membrane - between neurites and the extracellular space; non-Gaussian diffusion along neuronal and glial processes - resulting from structural disorder; and signal contribution from soma. For the first contribution, we propose Neurite Exchange Imaging (NEXI) as an extension of the SM of diffusion, which builds on the anisotropic Kärger model of two exchanging compartments. Using datasets acquired at multiple diffusion weightings (b) and diffusion times (t) in the rat brain in vivo, we investigate the suitability of NEXI to describe the diffusion signal in the gray matter, compared to the other two possible contributions. Our results for the diffusion time window 20-45 ms show minimal diffusivity time-dependence and more pronounced kurtosis decay with time, which is well fit by the exchange model. Moreover, we observe lower signal for longer diffusion times at high b. In light of these observations, we identify exchange as the mechanism that best explains these signal signatures in both low-b and high-b regime, and thereby propose NEXI as the minimal model for gray matter microstructure mapping. We finally highlight multi-b multi-t acquisition protocols as being best suited to estimate NEXI model parameters reliably. Using this approach, we estimate the inter-compartment water exchange time to be 15 - 60 ms in the rat cortex and hippocampus in vivo, which is of the same order or shorter than the diffusion time in typical diffusion MRI acquisitions. This suggests water exchange as an essential component for interpreting diffusion MRI measurements in gray matter.
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Affiliation(s)
- Ileana O Jelescu
- CIBM Center for Biomedical Imaging, Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland; School of Biology and Medicine, University of Lausanne (UNIL), Lausanne, Switzerland.
| | - Alexandre de Skowronski
- CIBM Center for Biomedical Imaging, Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | | | - Marco Palombo
- School of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK; School of Computer Science and Informatics, Cardiff University, Cardiff, UK; Department of Computer Science, Centre for Medical Image Computing, University College London, London, UK
| | - Dmitry S Novikov
- Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA
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16
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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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17
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Olesen JL, Østergaard L, Shemesh N, Jespersen SN. Diffusion time dependence, power-law scaling, and exchange in gray matter. Neuroimage 2022; 251:118976. [PMID: 35168088 PMCID: PMC8961002 DOI: 10.1016/j.neuroimage.2022.118976] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 12/24/2021] [Accepted: 02/04/2022] [Indexed: 12/27/2022] Open
Abstract
Characterizing neural tissue microstructure is a critical goal for future neuroimaging. Diffusion MRI (dMRI) provides contrasts that reflect diffusing spins' interactions with myriad microstructural features of biological systems. However, the specificity of dMRI remains limited due to the ambiguity of its signals vis-à-vis the underlying microstructure. To improve specificity, biophysical models of white matter (WM) typically express dMRI signals according to the Standard Model (SM) and have more recently in gray matter (GM) taken spherical compartments into account (the SANDI model) in attempts to represent cell soma. The validity of the assumptions underlying these models, however, remains largely undetermined, especially in GM. To validate these assumptions experimentally, observing their unique, functional properties, such as the b-1/2 power-law associated with one-dimensional diffusion, has emerged as a fruitful strategy. The absence of this signature in GM, in turn, has been explained by neurite water exchange, non-linear morphology, and/or by obscuring soma signal contributions. Here, we present diffusion simulations in realistic neurons demonstrating that curvature and branching does not destroy the stick power-law behavior in impermeable neurites, but also that their signal is drowned by the soma signal under typical experimental conditions. Nevertheless, by studying the GM dMRI signal's behavior as a function of diffusion weighting as well as time, we identify an attainable experimental regime in which the neurite signal dominates. Furthermore, we find that exchange-driven time dependence produces a signal behavior opposite to that which would be expected from restricted diffusion, thereby providing a functional signature that disambiguates the two effects. We present data from dMRI experiments in ex vivo rat brain at ultrahigh field of 16.4T and observe a time dependence that is consistent with substantial exchange but also with a GM stick power-law. The first finding suggests significant water exchange between neurites and the extracellular space while the second suggests a small sub-population of impermeable neurites. To quantify these observations, we harness the Kärger exchange model and incorporate the corresponding signal time dependence in the SM and SANDI models.
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Affiliation(s)
- Jonas L Olesen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Leif Østergaard
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Sune N Jespersen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark.
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18
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Novello L, Henriques RN, Ianuş A, Feiweier T, Shemesh N, Jovicich J. In vivo Correlation Tensor MRI reveals microscopic kurtosis in the human brain on a clinical 3T scanner. Neuroimage 2022; 254:119137. [PMID: 35339682 DOI: 10.1016/j.neuroimage.2022.119137] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 02/17/2022] [Accepted: 03/22/2022] [Indexed: 12/15/2022] Open
Abstract
Diffusion MRI (dMRI) has become one of the most important imaging modalities for noninvasively probing tissue microstructure. Diffusional Kurtosis MRI (DKI) quantifies the degree of non-gaussian diffusion, which in turn has been shown to increase sensitivity towards, e.g., disease and orientation mapping in neural tissue. However, the specificity of DKI is limited as different sources can contribute to the total intravoxel diffusional kurtosis, including: variance in diffusion tensor magnitudes (Kiso), variance due to diffusion anisotropy (Kaniso), and microscopic kurtosis (μK) related to restricted diffusion, microstructural disorder, and/or exchange. Interestingly, μK is typically ignored in diffusion MRI signal modeling as it is assumed to be negligible in neural tissues. However, recently, Correlation Tensor MRI (CTI) based on Double-Diffusion-Encoding (DDE) was introduced for kurtosis source separation, revealing non negligible μK in preclinical imaging. Here, we implemented CTI for the first time on a clinical 3T scanner and investigated the sources of total kurtosis in healthy subjects. A robust framework for kurtosis source separation in humans is introduced, followed by estimation of μK (and the other kurtosis sources) in the healthy brain. Using this clinical CTI approach, we find that μK significantly contributes to total diffusional kurtosis both in gray and white matter tissue but, as expected, not in the ventricles. The first μK maps of the human brain are presented, revealing that the spatial distribution of μK provides a unique source of contrast, appearing different from isotropic and anisotropic kurtosis counterparts. Moreover, group average templates of these kurtosis sources have been generated for the first time, which corroborated our findings at the underlying individual-level maps. We further show that the common practice of ignoring μK and assuming the multiple gaussian component approximation for kurtosis source estimation introduces significant bias in the estimation of other kurtosis sources and, perhaps even worse, compromises their interpretation. Finally, a twofold acceleration of CTI is discussed in the context of potential future clinical applications. We conclude that CTI has much potential for future in vivo microstructural characterizations in healthy and pathological tissue.
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Affiliation(s)
- Lisa Novello
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, Italy.
| | | | - Andrada Ianuş
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | | | - Noam Shemesh
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Jorge Jovicich
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, Italy
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19
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Wang W, Metzler R, Cherstvy AG. Anomalous diffusion, aging, and nonergodicity of scaled Brownian motion with fractional Gaussian noise: overview of related experimental observations and models. Phys Chem Chem Phys 2022; 24:18482-18504. [DOI: 10.1039/d2cp01741e] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
How does a systematic time-dependence of the diffusion coefficient $D (t)$ affect the ergodic and statistical characteristics of fractional Brownian motion (FBM)? Here, we examine how the behavior of the...
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20
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Arefin TM, Lee CH, White JD, Zhang J, Kaffman A. Macroscopic Structural and Connectome Mapping of the Mouse Brain Using Diffusion Magnetic Resonance Imaging. Bio Protoc 2021; 11:e4221. [PMID: 34909442 PMCID: PMC8635841 DOI: 10.21769/bioprotoc.4221] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 08/11/2021] [Accepted: 08/16/2021] [Indexed: 01/08/2023] Open
Abstract
Translational work in rodents elucidates basic mechanisms that drive complex behaviors relevant to psychiatric and neurological conditions. Nonetheless, numerous promising studies in rodents later fail in clinical trials, highlighting the need for improving the translational utility of preclinical studies in rodents. Imaging of small rodents provides an important strategy to address this challenge, as it enables a whole-brain unbiased search for structural and dynamic changes that can be directly compared to human imaging. The functional significance of structural changes identified using imaging can then be further investigated using molecular and genetic tools available for the mouse. Here, we describe a pipeline for unbiased search and characterization of structural changes and network properties, based on diffusion MRI data covering the entire mouse brain at an isotropic resolution of 100 µm. We first used unbiased whole-brain voxel-based analyses to identify volumetric and microstructural alterations in the brain of adult mice exposed to unpredictable postnatal stress (UPS), which is a mouse model of complex early life stress (ELS). Brain regions showing structural abnormalities were used as nodes to generate a grid for assessing structural connectivity and network properties based on graph theory. The technique described here can be broadly applied to understand brain connectivity in other mouse models of human disorders, as well as in genetically modified mouse strains. Graphic abstract: Pipeline for characterizing structural connectome in the mouse brain using diffusion magnetic resonance imaging. Scale bar = 1 mm.
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Affiliation(s)
- Tanzil Mahmud Arefin
- Bernard Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, USA
| | - Choong Heon Lee
- Bernard Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, USA
| | - Jordon D. White
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Jiangyang Zhang
- Bernard Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, USA
| | - Arie Kaffman
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
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21
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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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22
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Cheng H, Calhoun V. Exploring microstructure with diffusion-weighted imaging: From acquisition to modeling. J Neurosci Methods 2021; 363:109335. [PMID: 34450220 DOI: 10.1016/j.jneumeth.2021.109335] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Hu Cheng
- Imaging Research Facility, Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA.
| | - Vince Calhoun
- Tri-institutional center for Translational Research in Neuroimaging and Data Science (TReNDS) Center, Georgia Institute of Technology, Emory University, Georgia State University, Atlanta 30303, GA, USA.
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23
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Yang JYM, Yeh CH, Poupon C, Calamante F. Diffusion MRI tractography for neurosurgery: the basics, current state, technical reliability and challenges. Phys Med Biol 2021; 66. [PMID: 34157706 DOI: 10.1088/1361-6560/ac0d90] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/22/2021] [Indexed: 01/20/2023]
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is currently the only imaging technique that allows for non-invasive delineation and visualisation of white matter (WM) tractsin vivo,prompting rapid advances in related fields of brain MRI research in recent years. One of its major clinical applications is for pre-surgical planning and intraoperative image guidance in neurosurgery, where knowledge about the location of WM tracts nearby the surgical target can be helpful to guide surgical resection and optimise post-surgical outcomes. Surgical injuries to these WM tracts can lead to permanent neurological and functional deficits, making the accuracy of tractography reconstructions paramount. The quality of dMRI tractography is influenced by many modifiable factors, ranging from MRI data acquisition through to the post-processing of tractography output, with the potential of error propagation based on decisions made at each and subsequent processing steps. Research over the last 25 years has significantly improved the anatomical accuracy of tractography. An updated review about tractography methodology in the context of neurosurgery is now timely given the thriving research activities in dMRI, to ensure more appropriate applications in the clinical neurosurgical realm. This article aims to review the dMRI physics, and tractography methodologies, highlighting recent advances to provide the key concepts of tractography-informed neurosurgery, with a focus on the general considerations, the current state of practice, technical challenges, potential advances, and future demands to this field.
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Affiliation(s)
- Joseph Yuan-Mou Yang
- Department of Neurosurgery, The Royal Children's Hospital, Melbourne, Australia.,Neuroscience Research, Murdoch Children's Research Institute, Melbourne, Australia.,Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan, Taiwan.,Department of Child and Adolescent Psychiatry, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Cyril Poupon
- NeuroSpin, Frédéric Joliot Life Sciences Institute, CEA, CNRS, Paris-Saclay University, Gif-sur-Yvette, France
| | - Fernando Calamante
- The University of Sydney, Sydney Imaging, Sydney, Australia.,The University of Sydney, School of Biomedical Engineering, Sydney, Australia
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24
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Lee HH, Fieremans E, Novikov DS. Realistic Microstructure Simulator (RMS): Monte Carlo simulations of diffusion in three-dimensional cell segmentations of microscopy images. J Neurosci Methods 2020; 350:109018. [PMID: 33279478 DOI: 10.1016/j.jneumeth.2020.109018] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 11/16/2020] [Accepted: 11/29/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Monte Carlo simulations of diffusion are commonly used as a model validation tool as they are especially suitable for generating the diffusion MRI signal in complicated tissue microgeometries. NEW METHOD Here we describe the details of implementing Monte Carlo simulations in three-dimensional (3d) voxelized segmentations of cells in microscopy images. Using the concept of the corner reflector, we largely reduce the computational load of simulating diffusion within and exchange between multiple cells. Precision is further achieved by GPU-based parallel computations. RESULTS Our simulation of diffusion in white matter axons segmented from a mouse brain demonstrates its value in validating biophysical models. Furthermore, we provide the theoretical background for implementing a discretized diffusion process, and consider the finite-step effects of the particle-membrane reflection and permeation events, needed for efficient simulation of interactions with irregular boundaries, spatially variable diffusion coefficient, and exchange. COMPARISON WITH EXISTING METHODS To our knowledge, this is the first Monte Carlo pipeline for MR signal simulations in a substrate composed of numerous realistic cells, accounting for their permeable and irregularly-shaped membranes. CONCLUSIONS The proposed RMS pipeline makes it possible to achieve fast and accurate simulations of diffusion in realistic tissue microgeometry, as well as the interplay with other MR contrasts. Presently, RMS focuses on simulations of diffusion, exchange, and T1 and T2 NMR relaxation in static tissues, with a possibility to straightforwardly account for susceptibility-induced T2* effects and flow.
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Affiliation(s)
- Hong-Hsi Lee
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA.
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA
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