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Kanakaraj P, Cai LY, Yao T, Rheault F, Rogers BP, Anderson A, Schilling KG, Landman BA. Efficient approximate signal reconstruction for correction of gradient nonlinearities in diffusion-weighted imaging. Magn Reson Imaging 2023; 102:20-25. [PMID: 36965836 PMCID: PMC10517071 DOI: 10.1016/j.mri.2023.03.014] [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: 12/29/2022] [Revised: 03/20/2023] [Accepted: 03/21/2023] [Indexed: 03/27/2023]
Abstract
In diffusion weighted MRI (DW-MRI), hardware nonlinearities lead to spatial variations in the orientation and magnitude of diffusion weighting. While the correction of these spatial distortions has been well established for analyses of DW-MRI, the existing voxel-wise empirical correction for gradient nonlinearities requires reimplementation of existing models, as the resultant gradients vary by voxel. Herein, we propose a two-step signal approximation after voxel-wise correction of gradient nonlinearity effects in DW-MRI. The proposed technique (1) scales the diffusion signal and (2) resamples the gradient orientations. This results in uniform gradients across the corrected image and provides the key advantage of seamless integration into current diffusion workflows. We investigated the validity of our technique by fitting a multi-compartment neurite orientation dispersion and density imaging (NODDI) model to the empirical correction and proposed approximation in five subjects from the MASiVar pediatric dataset. We evaluated intra-cellular volume fraction (iVF), CSF volume fraction (cVF), and orientation dispersion index (ODI) from NODDI. The Cohen's d of iVF, cVF and ODI between the techniques was <0.2 indicating the proposed technique does not exhibit significant differences from the voxel-wise correction technique. Our two-step signal approximation is an efficient representation of the voxel-wise gradient table correction. Using this approximation, correction of gradient nonlinearities can be easily incorporated into existing diffusion preprocessing pipelines and is implemented in "PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images".
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Affiliation(s)
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Tianyuan Yao
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
| | - Francois Rheault
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Baxter P Rogers
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Adam Anderson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Services, Vanderbilt University Medical Center, Vanderbilt University Medical, Nashville, TN, USA.
| | - Kurt G Schilling
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Services, Vanderbilt University Medical Center, Vanderbilt University Medical, Nashville, TN, USA.
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Laporte JP, Faulkner ME, Gong Z, Akhonda MA, Ferrucci L, Egan JM, Bouhrara M. Hypertensive Adults Exhibit Lower Myelin Content: A Multicomponent Relaxometry and Diffusion Magnetic Resonance Imaging Study. Hypertension 2023; 80:1728-1738. [PMID: 37283066 PMCID: PMC10355798 DOI: 10.1161/hypertensionaha.123.21012] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/11/2023] [Indexed: 06/08/2023]
Abstract
BACKGROUND It is unknown whether hypertension plays any role in cerebral myelination. To fill this knowledge gap, we studied 90 cognitively unimpaired adults, age range 40 to 94 years, who are participants in the Baltimore Longitudinal Study of Aging and the Genetic and Epigenetic Signatures of Translational Aging Laboratory Testing to look for potential associations between hypertension and cerebral myelin content across 14 white matter brain regions. METHODS Myelin content was probed using our advanced multicomponent magnetic resonance relaxometry method of myelin water fraction, a direct and specific magnetic resonance imaging measure of myelin content, and longitudinal and transverse relaxation rates (R1 and R2), 2 highly sensitive magnetic resonance imaging metrics of myelin content. We also applied diffusion tensor imaging magnetic resonance imaging to measure fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity values, which are metrics of cerebral microstructural tissue integrity, to provide context with previous magnetic resonance imaging findings. RESULTS After adjustment of age, sex, systolic blood pressure, smoking status, diabetes status, and cholesterol level, our results indicated that participants with hypertension exhibited lower myelin water fraction, fractional anisotropy, R1 and R2 values and higher mean diffusivity, radial diffusivity, and axial diffusivity values, indicating lower myelin content and higher impairment to the brain microstructure. These associations were significant across several white matter regions, particularly in the corpus callosum, fronto-occipital fasciculus, temporal lobes, internal capsules, and corona radiata. CONCLUSIONS These original findings suggest a direct association between myelin content and hypertension and form the basis for further investigations including longitudinal assessments of this relationship.
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Affiliation(s)
- John P. Laporte
- Laboratory of Clinical Investigation (J.P.L., M.E.F., Z.G., M.A.B.S.A., J.M.E., M.B.), National Institute on Aging, National Institutes of Health, Baltimore, MD
| | - Mary E. Faulkner
- Laboratory of Clinical Investigation (J.P.L., M.E.F., Z.G., M.A.B.S.A., J.M.E., M.B.), National Institute on Aging, National Institutes of Health, Baltimore, MD
| | - Zhaoyuan Gong
- Laboratory of Clinical Investigation (J.P.L., M.E.F., Z.G., M.A.B.S.A., J.M.E., M.B.), National Institute on Aging, National Institutes of Health, Baltimore, MD
| | - Mohammad A.B.S. Akhonda
- Laboratory of Clinical Investigation (J.P.L., M.E.F., Z.G., M.A.B.S.A., J.M.E., M.B.), National Institute on Aging, National Institutes of Health, Baltimore, MD
| | - Luigi Ferrucci
- Translational Gerontology Branch (L.F.), National Institute on Aging, National Institutes of Health, Baltimore, MD
| | - Josephine M. Egan
- Laboratory of Clinical Investigation (J.P.L., M.E.F., Z.G., M.A.B.S.A., J.M.E., M.B.), National Institute on Aging, National Institutes of Health, Baltimore, MD
| | - Mustapha Bouhrara
- Laboratory of Clinical Investigation (J.P.L., M.E.F., Z.G., M.A.B.S.A., J.M.E., M.B.), National Institute on Aging, National Institutes of Health, Baltimore, MD
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Kanakaraj P, Cai LY, Rheault F, Yehe FC, Rogers BP, Schilling KG, Landman BA. Mapping the impact of nonlinear gradient fields with noise on diffusion MRI. Magn Reson Imaging 2023; 98:124-131. [PMID: 36632947 DOI: 10.1016/j.mri.2023.01.004] [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: 10/24/2022] [Accepted: 01/06/2023] [Indexed: 01/11/2023]
Abstract
In diffusion MRI, gradient nonlinearities cause spatial variations in the magnitude and direction of diffusion gradients. Studies have shown artifacts from these distortions can results in biased diffusion tensor information and tractography. Here, we investigate the impact of gradient nonlinearity correction in the presence of noise. We introduced empirically derived gradient nonlinear fields at different signal-to-noise ratio (SNR) levels in two experiments: tensor field simulation and simulation of the brain. For each experiment, this work compares two techniques empirically: voxel-wise gradient table correction and approximate correction by scaling the signal directly. The impact was assessed through diffusion metrics including mean diffusivity (MD), fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and principal eigen vector (V1). The study shows (1) the correction of gradient nonlinearities will not lead to substantively incorrect estimation of diffusion metrics in a linear system, (2) gradient nonlinearity correction does not interact adversely with noise, (3) nonlinearity correction suppresses the impact of nonlinearities in typical SNR data, (4) for SNR below 30, the performance of both the gradient nonlinearity correction techniques were similar, and (5) larger impacts are seen in regions where the gradient nonlinearities are distinct. Thus, this study suggests that there were greater beneficial effects than adverse effects due to the correction of nonlinearities. Additionally, correction of nonlinearities is recommended when region of interests are in areas with pronounced nonlinearities.
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Affiliation(s)
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Francois Rheault
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Fang-Cheng Yehe
- Department of Neurological Surgery, University of Pittsburg, School of Medicine, Pittsburg, PA, USA.
| | - Baxter P Rogers
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Kurt G Schilling
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Services, Vanderbilt University Medical Center, Vanderbilt University Medical, Nashville, TN, USA.
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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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Tax CMW, Bastiani M, Veraart J, Garyfallidis E, Okan Irfanoglu M. What's new and what's next in diffusion MRI preprocessing. Neuroimage 2022; 249:118830. [PMID: 34965454 PMCID: PMC9379864 DOI: 10.1016/j.neuroimage.2021.118830] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/26/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on "what's new" since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on "Mapping the Connectome" in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on "what's next" in dMRI preprocessing.
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Affiliation(s)
- Chantal M W Tax
- Image Sciences Institute, University Medical Center Utrecht, The Netherlands; Cardiff University Brain Research Imaging Centre, School of Physics and Astronomy, Cardiff University, UK.
| | - Matteo Bastiani
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Jelle Veraart
- Center for Biomedical Imaging, New York University Grossman School of Medicine, NY, USA
| | | | - M Okan Irfanoglu
- Quantitative Medical Imaging Section, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
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Wang Y, Fellah S, Fields ME, Guilliams KP, Binkley MM, Eldeniz C, Shimony JS, Reis M, Vo KD, Chen Y, Lee JM, An H, Ford AL. Cerebral Oxygen Metabolic Stress, Microstructural Injury, and Infarction in Adults With Sickle Cell Disease. Neurology 2021; 97:e902-e912. [PMID: 34172536 PMCID: PMC8408504 DOI: 10.1212/wnl.0000000000012404] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 05/26/2021] [Indexed: 12/18/2022] Open
Abstract
OBJECTIVE To determine the patient- and tissue-based relationships between cerebral hemodynamic and oxygen metabolic stress, microstructural injury, and infarct location in adults with sickle cell disease (SCD). METHODS Control and SCD participants underwent brain MRI to quantify cerebral blood flow (CBF), oxygen extraction fraction (OEF), mean diffusivity (MD), and fractional anisotropy (FA) within normal-appearing white matter (NAWM), and infarcts on FLAIR. Multivariable linear regression examined the patient- and voxel-based associations between hemodynamic and metabolic stress (defined as elevated CBF and OEF, respectively), white matter microstructure, and infarct location. RESULTS Of 83 control and SCD participants, adults with SCD demonstrated increased CBF (50.9 vs 38.8 mL/min/100g, p<0.001), increased OEF (0.35 vs 0.25, p<0.001), increased MD (0.76 vs 0.72 x 10-3mm2 s-1, p=0.005), and decreased FA (0.40 vs 0.42, p=0.021) within NAWM compared to controls. In multivariable analysis, increased OEF (β=0.19, p=0.035), but not CBF (β=0.00, p=0.340), independently predicted increased MD in the SCD cohort, while neither were predictors in controls. On voxel-wise regression, the SCD cohort demonstrated widespread OEF elevation, encompassing deep white matter regions of elevated MD and reduced FA, which spatially extended beyond high density infarct locations from the SCD cohort. CONCLUSION Elevated OEF, a putative index of cerebral oxygen metabolic stress, may provide a metric of ischemic vulnerability which could enable individualization of therapeutic strategies in SCD. The patient- and tissue-based relationships between elevated OEF, elevated MD, and cerebral infarcts suggest that oxygen metabolic stress may underlie microstructural injury prior to the development of cerebral infarcts in SCD.
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Affiliation(s)
- Yan Wang
- Department of Neurology, Washington University School of Medicine, St. Louis, MO
| | - Slim Fellah
- Department of Neurology, Washington University School of Medicine, St. Louis, MO
| | - Melanie E Fields
- Division of Pediatric Hematology/Oncology, Washington University School of Medicine, St. Louis, MO
| | - Kristin P Guilliams
- Division of Pediatric Neurology, Washington University School of Medicine, St. Louis, MO
| | - Michael M Binkley
- Department of Neurology, Washington University School of Medicine, St. Louis, MO
| | - Cihat Eldeniz
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO
| | - Martin Reis
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO
| | - Katie D Vo
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO
| | - Yasheng Chen
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO
| | - Jin-Moo Lee
- Department of Neurology, Washington University School of Medicine, St. Louis, MO.,Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO
| | - Hongyu An
- Department of Neurology, Washington University School of Medicine, St. Louis, MO.,Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO
| | - Andria L Ford
- Department of Neurology, Washington University School of Medicine, St. Louis, MO; .,Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO
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Wang F, Dong Z, Tian Q, Liao C, Fan Q, Hoge WS, Keil B, Polimeni JR, Wald LL, Huang SY, Setsompop K. In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution. Sci Data 2021; 8:122. [PMID: 33927203 PMCID: PMC8084962 DOI: 10.1038/s41597-021-00904-z] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 03/26/2021] [Indexed: 01/18/2023] Open
Abstract
We present a whole-brain in vivo diffusion MRI (dMRI) dataset acquired at 760 μm isotropic resolution and sampled at 1260 q-space points across 9 two-hour sessions on a single healthy participant. The creation of this benchmark dataset is possible through the synergistic use of advanced acquisition hardware and software including the high-gradient-strength Connectom scanner, a custom-built 64-channel phased-array coil, a personalized motion-robust head stabilizer, a recently developed SNR-efficient dMRI acquisition method, and parallel imaging reconstruction with advanced ghost reduction algorithm. With its unprecedented resolution, SNR and image quality, we envision that this dataset will have a broad range of investigational, educational, and clinical applications that will advance the understanding of human brain structures and connectivity. This comprehensive dataset can also be used as a test bed for new modeling, sub-sampling strategies, denoising and processing algorithms, potentially providing a common testing platform for further development of in vivo high resolution dMRI techniques. Whole brain anatomical T1-weighted and T2-weighted images at submillimeter scale along with field maps are also made available.
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Affiliation(s)
- Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA.
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA.
| | - Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - W Scott Hoge
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Boris Keil
- Department of Life Science Engineering, Institute of Medical Physics and Radiation Protection, Giessen, Germany
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
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8
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Guo F, de Luca A, Parker G, Jones DK, Viergever MA, Leemans A, Tax CMW. The effect of gradient nonlinearities on fiber orientation estimates from spherical deconvolution of diffusion magnetic resonance imaging data. Hum Brain Mapp 2021; 42:367-383. [PMID: 33035372 PMCID: PMC7776002 DOI: 10.1002/hbm.25228] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 09/22/2020] [Accepted: 09/24/2020] [Indexed: 12/12/2022] Open
Abstract
Gradient nonlinearities in magnetic resonance imaging (MRI) cause spatially varying mismatches between the imposed and the effective gradients and can cause significant biases in rotationally invariant diffusion MRI measures derived from, for example, diffusion tensor imaging. The estimation of the orientational organization of fibrous tissue, which is nowadays frequently performed with spherical deconvolution techniques ideally using higher diffusion weightings, can likewise be biased by gradient nonlinearities. We explore the sensitivity of two established spherical deconvolution approaches to gradient nonlinearities, namely constrained spherical deconvolution (CSD) and damped Richardson-Lucy (dRL). Additionally, we propose an extension of dRL to take into account gradient imperfections, without the need of data interpolation. Simulations show that using the effective b-matrix can improve dRL fiber orientation estimation and reduces angular deviations, while CSD can be more robust to gradient nonlinearity depending on the implementation. Angular errors depend on a complex interplay of many factors, including the direction and magnitude of gradient deviations, underlying microstructure, SNR, anisotropy of the effective response function, and diffusion weighting. Notably, angular deviations can also be observed at lower b-values in contrast to the perhaps common assumption that only high b-value data are affected. In in vivo Human Connectome Project data and acquisitions from an ultrastrong gradient (300 mT/m) scanner, angular differences are observed between applying and not applying the effective gradients in dRL estimation. As even small angular differences can lead to error propagation during tractography and as such impact connectivity analyses, incorporating gradient deviations into the estimation of fiber orientations should make such analyses more reliable.
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Affiliation(s)
- Fenghua Guo
- Image Sciences InstituteUniversity Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands
| | - Alberto de Luca
- Image Sciences InstituteUniversity Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands
| | - Greg Parker
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff UniversityCardiffUK
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff UniversityCardiffUK
| | - Max A. Viergever
- Image Sciences InstituteUniversity Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands
| | - Alexander Leemans
- Image Sciences InstituteUniversity Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands
| | - Chantal M. W. Tax
- Image Sciences InstituteUniversity Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff UniversityCardiffUK
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9
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Rudrapatna U, Parker GD, Roberts J, Jones DK. A comparative study of gradient nonlinearity correction strategies for processing diffusion data obtained with ultra-strong gradient MRI scanners. Magn Reson Med 2020; 85:1104-1113. [PMID: 33009875 PMCID: PMC8103165 DOI: 10.1002/mrm.28464] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 06/27/2020] [Accepted: 07/13/2020] [Indexed: 11/15/2022]
Abstract
Purpose The analysis of diffusion data obtained under large gradient nonlinearities necessitates corrections during data reconstruction and analysis. While two such preprocessing pipelines have been proposed, no comparative studies assessing their performance exist. Furthermore, both pipelines neglect the impact of subject motion during acquisition, which, in the presence of gradient nonlinearities, induces spatio‐temporal B‐matrix variations. Here, spatio‐temporal B‐matrix tracking (STB) is proposed and its performance compared to established pipelines. Methods Diffusion tensor MRI (DT‐MRI) was performed using a 300 mT/m gradient system. Data were acquired with volunteers positioned in regions with pronounced gradient nonlinearities, and used to compare the performance of six different processing pipelines, including STB. Results Up to 30% errors were observed in DT‐MRI parameter estimates when neglecting gradient nonlinearities. Moreover, the order in which B0 inhomogeneity, eddy current and gradient nonlinearity corrections were performed was found to impact the consistency of parameter estimates significantly. Although, no pipeline emerged as a clear winner, the STB approach seemed to yield the most consistent parameter estimates under large gradient nonlinearities. Conclusions Under large gradient nonlinearities, the choice of preprocessing pipeline significantly impacts the estimated diffusion parameters. Motion‐induced spatio‐temporal B‐matrix variations can lead to systematic bias in the parameter estimates, that can be ameliorated using the proposed STB framework. Click here for author‐reader discussions
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Affiliation(s)
- Umesh Rudrapatna
- Cardiff University Brain Research Imaging CentreCardiff UniversityCardiffUnited Kingdom
| | - Greg D. Parker
- Cardiff University Brain Research Imaging CentreCardiff UniversityCardiffUnited Kingdom
| | - Jamie Roberts
- Royal United Hospitals BathNHS Foundation TrustBathUnited Kingdom
| | - Derek K. Jones
- Cardiff University Brain Research Imaging CentreCardiff UniversityCardiffUnited Kingdom
- Mary MacKillop Institute for Health Research, Faculty of Health SciencesAustralian Catholic UniversityMelbourneVictoriaAustralia
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10
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Mesri HY, David S, Viergever MA, Leemans A. The adverse effect of gradient nonlinearities on diffusion MRI: From voxels to group studies. Neuroimage 2020; 205:116127. [DOI: 10.1016/j.neuroimage.2019.116127] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Revised: 07/20/2019] [Accepted: 08/23/2019] [Indexed: 11/29/2022] Open
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11
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Lee Y, Kettinger AO, Wilm BJ, Deichmann R, Weiskopf N, Lambert C, Pruessmann KP, Nagy Z. A comprehensive approach for correcting voxel-wise b-value errors in diffusion MRI. Magn Reson Med 2019; 83:2173-2184. [PMID: 31840300 PMCID: PMC7065087 DOI: 10.1002/mrm.28078] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 09/23/2019] [Accepted: 10/22/2019] [Indexed: 01/29/2023]
Abstract
PURPOSE In diffusion MRI, the actual b-value played out on the scanner may deviate from the nominal value due to magnetic field imperfections. A simple image-based correction method for this problem is presented. METHODS The apparent diffusion constant (ADC) of a water phantom was measured voxel-wise along 64 diffusion directions at b = 1000 s/mm2 . The true diffusion constant of water was estimated, considering the phantom temperature. A voxel-wise correction factor, providing an effective b-value including any magnetic field deviations, was determined for each diffusion direction by relating the measured ADC to the true diffusion constant. To test the method, the measured b-value map was used to calculate the corrected voxel-wise ADC for additionally acquired diffusion data sets on the same water phantom and data sets acquired on a small water phantom at three different positions. Diffusion tensor was estimated by applying the measured b-value map to phantom and in vivo data sets. RESULTS The b-value-corrected ADC maps of the phantom showed the expected spatial uniformity as well as a marked improvement in consistency across diffusion directions. The b-value correction for the brain data resulted in a 5.8% and 5.5% decrease in mean diffusivity and angular differences of the primary diffusion direction of 2.71° and 0.73° inside gray and white matter, respectively. CONCLUSION The actual b-value deviates significantly from its nominal setting, leading to a spatially variable error in the common diffusion outcome measures. The suggested method measures and corrects these artifacts.
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Affiliation(s)
- Yoojin Lee
- Laboratory for Social and Neural Systems Research (SNS Lab), University of Zurich, Zurich, Switzerland.,Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Adam O Kettinger
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, Hungary.,Department of Nuclear Techniques, Budapest University of Technology and Economics, Budapest, Hungary
| | - Bertram Jakob Wilm
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Ralf Deichmann
- Brain Imaging Centre, Goethe University, Frankfurt, Germany.,Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Nikolaus Weiskopf
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Christian Lambert
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Klaas Paul Pruessmann
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Zoltan Nagy
- Laboratory for Social and Neural Systems Research (SNS Lab), University of Zurich, Zurich, Switzerland.,Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
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12
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Jones DK, Alexander DC, Bowtell R, Cercignani M, Dell'Acqua F, McHugh DJ, Miller KL, Palombo M, Parker GJM, Rudrapatna US, Tax CMW. Microstructural imaging of the human brain with a 'super-scanner': 10 key advantages of ultra-strong gradients for diffusion MRI. Neuroimage 2018; 182:8-38. [PMID: 29793061 DOI: 10.1016/j.neuroimage.2018.05.047] [Citation(s) in RCA: 105] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Revised: 05/17/2018] [Accepted: 05/18/2018] [Indexed: 12/13/2022] Open
Abstract
The key component of a microstructural diffusion MRI 'super-scanner' is a dedicated high-strength gradient system that enables stronger diffusion weightings per unit time compared to conventional gradient designs. This can, in turn, drastically shorten the time needed for diffusion encoding, increase the signal-to-noise ratio, and facilitate measurements at shorter diffusion times. This review, written from the perspective of the UK National Facility for In Vivo MR Imaging of Human Tissue Microstructure, an initiative to establish a shared 300 mT/m-gradient facility amongst the microstructural imaging community, describes ten advantages of ultra-strong gradients for microstructural imaging. Specifically, we will discuss how the increase of the accessible measurement space compared to a lower-gradient systems (in terms of Δ, b-value, and TE) can accelerate developments in the areas of 1) axon diameter distribution mapping; 2) microstructural parameter estimation; 3) mapping micro-vs macroscopic anisotropy features with gradient waveforms beyond a single pair of pulsed-gradients; 4) multi-contrast experiments, e.g. diffusion-relaxometry; 5) tractography and high-resolution imaging in vivo and 6) post mortem; 7) diffusion-weighted spectroscopy of metabolites other than water; 8) tumour characterisation; 9) functional diffusion MRI; and 10) quality enhancement of images acquired on lower-gradient systems. We finally discuss practical barriers in the use of ultra-strong gradients, and provide an outlook on the next generation of 'super-scanners'.
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Affiliation(s)
- D K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK; School of Psychology, Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, 3065, Australia.
| | - D C Alexander
- Centre for Medical Image Computing (CMIC), Department of Computer Science, UCL (University College London), Gower Street, London, UK; Clinical Imaging Research Centre, National University of Singapore, Singapore
| | - R Bowtell
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - M Cercignani
- Department of Psychiatry, Brighton and Sussex Medical School, Brighton, UK
| | - F Dell'Acqua
- Natbrainlab, Department of Neuroimaging, King's College London, London, UK
| | - D J McHugh
- Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK; CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge and Manchester, UK
| | - K L Miller
- Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK
| | - M Palombo
- Centre for Medical Image Computing (CMIC), Department of Computer Science, UCL (University College London), Gower Street, London, UK
| | - G J M Parker
- Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK; CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge and Manchester, UK; Bioxydyn Ltd., Manchester, UK
| | - U S Rudrapatna
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - C M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
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13
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David G, Freund P, Mohammadi S. The efficiency of retrospective artifact correction methods in improving the statistical power of between-group differences in spinal cord DTI. Neuroimage 2017; 158:296-307. [PMID: 28669912 PMCID: PMC6168644 DOI: 10.1016/j.neuroimage.2017.06.051] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Revised: 06/19/2017] [Accepted: 06/21/2017] [Indexed: 11/10/2022] Open
Abstract
Diffusion tensor imaging (DTI) is a promising approach for investigating the white matter microstructure of the spinal cord. However, it suffers from severe susceptibility, physiological, and instrumental artifacts present in the cord. Retrospective correction techniques are popular approaches to reduce these artifacts, because they are widely applicable and do not increase scan time. In this paper, we present a novel outlier rejection approach (reliability masking) which is designed to supplement existing correction approaches by excluding irreversibly corrupted and thus unreliable data points from the DTI index maps. Then, we investigate how chains of retrospective correction techniques including (i) registration, (ii) registration and robust fitting, and (iii) registration, robust fitting, and reliability masking affect the statistical power of a previously reported finding of lower fractional anisotropy values in the posterior column and lateral corticospinal tracts in cervical spondylotic myelopathy (CSM) patients. While established post-processing steps had small effect on the statistical power of the clinical finding (slice-wise registration: −0.5%, robust fitting: +0.6%), adding reliability masking to the post-processing chain increased it by 4.7%. Interestingly, reliability masking and registration affected the t-score metric differently: while the gain in statistical power due to reliability masking was mainly driven by decreased variability in both groups, registration slightly increased variability. In conclusion, reliability masking is particularly attractive for neuroscience and clinical research studies, as it increases statistical power by reducing group variability and thus provides a cost-efficient alternative to increasing the group size. A novel outlier rejection technique (reliability masking) is introduced. Standard artifact correction has little effect on the statistical power of between-group differences. Reliability masking improves the statistical power of between-group differences. This improvement is driven by decreased group-level variability.
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Affiliation(s)
- Gergely David
- Spinal Cord Injury Center Balgrist, Balgrist University Hospital, Zurich, Switzerland; Department of Systems Neuroscience, Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Patrick Freund
- Spinal Cord Injury Center Balgrist, Balgrist University Hospital, Zurich, Switzerland; Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom; Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London, London, United Kingdom; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Siawoosh Mohammadi
- Department of Systems Neuroscience, Medical Center Hamburg-Eppendorf, Hamburg, Germany; Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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14
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Mohammadi S, Carey D, Dick F, Diedrichsen J, Sereno MI, Reisert M, Callaghan MF, Weiskopf N. Whole-Brain In-vivo Measurements of the Axonal G-Ratio in a Group of 37 Healthy Volunteers. Front Neurosci 2015; 9:441. [PMID: 26640427 PMCID: PMC4661323 DOI: 10.3389/fnins.2015.00441] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Accepted: 11/03/2015] [Indexed: 12/13/2022] Open
Abstract
The g-ratio, quantifying the ratio between the inner and outer diameters of a fiber, is an important microstructural characteristic of fiber pathways and is functionally related to conduction velocity. We introduce a novel method for estimating the MR g-ratio non-invasively across the whole brain using high-fidelity magnetization transfer (MT) imaging and single-shell diffusion MRI. These methods enabled us to map the MR g-ratio in vivo across the brain's prominent fiber pathways in a group of 37 healthy volunteers and to estimate the inter-subject variability. Effective correction of susceptibility-related distortion artifacts was essential before combining the MT and diffusion data, in order to reduce partial volume and edge artifacts. The MR g-ratio is in good qualitative agreement with histological findings despite the different resolution and spatial coverage of MRI and histology. The MR g-ratio holds promise as an important non-invasive biomarker due to its microstructural and functional relevance in neurodegeneration.
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Affiliation(s)
- Siawoosh Mohammadi
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf Hamburg, Germany ; Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, UK
| | - Daniel Carey
- Birkbeck/UCL Centre for NeuroImaging, Birkbeck College London, UK
| | - Fred Dick
- Birkbeck/UCL Centre for NeuroImaging, Birkbeck College London, UK
| | - Joern Diedrichsen
- UCL Institute of Cognitive Neurology, University College London London, UK
| | - Martin I Sereno
- Birkbeck/UCL Centre for NeuroImaging, Birkbeck College London, UK
| | - Marco Reisert
- Medical Physics, Department of Radiology, University Medical Center Freiburg Freiburg, Germany
| | - Martina F Callaghan
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, UK
| | - Nikolaus Weiskopf
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, UK ; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany
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15
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Hansen B, Lund TE, Sangill R, Stubbe E, Finsterbusch J, Jespersen SN. Experimental considerations for fast kurtosis imaging. Magn Reson Med 2015; 76:1455-1468. [PMID: 26608731 DOI: 10.1002/mrm.26055] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Revised: 10/22/2015] [Accepted: 10/24/2015] [Indexed: 12/18/2022]
Abstract
PURPOSE The clinical use of kurtosis imaging is impeded by long acquisitions and postprocessing. Recently, estimation of mean kurtosis tensor W¯ and mean diffusivity ( D¯) was made possible from 13 distinct diffusion weighted MRI acquisitions (the 1-3-9 protocol) with simple postprocessing. Here, we analyze the effects of noise and nonideal diffusion encoding, and propose a new correction strategy. We also present a 1-9-9 protocol with increased robustness to experimental imperfections and minimal additional scan time. This refinement does not affect computation time and also provides a fast estimate of fractional anisotropy (FA). THEORY AND METHODS 1-3-9/1-9-9 data are acquired in rat and human brains, and estimates of D¯, FA, W¯ from human brains are compared with traditional estimates from an extensive diffusion kurtosis imaging data set. Simulations are used to evaluate the influence of noise and diffusion encodings deviating from the scheme, and the performance of the correction strategy. Optimal b-values are determined from simulations and data. RESULTS Accuracy and precision in D¯ and W¯ are comparable to nonlinear least squares estimation, and is improved with the 1-9-9 protocol. The compensation strategy vastly improves parameter estimation in nonideal data. CONCLUSION The framework offers a robust and compact method for estimating several diffusion metrics. The protocol is easily implemented. Magn Reson Med 76:1455-1468, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Affiliation(s)
- Brian Hansen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Clinical Institute, Aarhus University, Aarhus, Denmark
| | - Torben E Lund
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Clinical Institute, Aarhus University, Aarhus, Denmark
| | - Ryan Sangill
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Clinical Institute, Aarhus University, Aarhus, Denmark
| | - Ebbe Stubbe
- Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Jürgen Finsterbusch
- Institut für Systemische Neurowissenschaften, Universitätsklinikum Hamburg-Eppendorf, Germany
| | - Sune Nørhøj Jespersen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Clinical Institute, Aarhus University, Aarhus, Denmark. .,Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark.
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16
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Abstract
We present an implementation of a recently developed noise reduction algorithm for dMRI data, called multi-shell position orientation adaptive smoothing (msPOAS), as a toolbox for SPM. The method intrinsically adapts to the structures of different size and shape in dMRI and hence avoids blurring typically observed in non-adaptive smoothing. We give examples for the usage of the toolbox and explain the determination of experiment-dependent parameters for an optimal performance of msPOAS.
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17
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Berl MM, Walker L, Modi P, Irfanoglu MO, Sarlls JE, Nayak A, Pierpaoli C. Investigation of vibration-induced artifact in clinical diffusion-weighted imaging of pediatric subjects. Hum Brain Mapp 2015; 36:4745-57. [PMID: 26350492 DOI: 10.1002/hbm.22846] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2014] [Revised: 05/04/2015] [Accepted: 05/08/2015] [Indexed: 11/07/2022] Open
Abstract
It has been reported that mechanical vibrations of the magnetic resonance imaging scanner could produce spurious signal dropouts in diffusion-weighted images resulting in artifactual anisotropy in certain regions of the brain with red appearance in the Directionally Encoded Color maps. We performed a review of the frequency of this artifact across pediatric studies, noting differences by scanner manufacturer, acquisition protocol, as well as weight and position of the subject. We also evaluated the ability of automated and quantitative methods to detect this artifact. We found that the artifact may be present in over 50% of data in certain protocols and is not limited to one scanner manufacturer. While a specific scanner had the highest incidence, low body weight and positioning were also associated with appearance of the artifact for both scanner types evaluated, making children potentially more susceptible than adults. Visual inspection remains the best method for artifact identification. Software for automated detection showed very low sensitivity (10%). The artifact may present inconsistently in longitudinal studies. We discuss a published case report that has been widely cited and used as evidence to set policy about diagnostic criteria for determining vegetative state. That report attributed longitudinal changes in anisotropy to white matter plasticity without considering the possibility that the changes were caused by this artifact. Our study underscores the need to check for the presence of this artifact in clinical studies, analyzes circumstances for when it may be more likely to occur, and suggests simple strategies to identify and potentially avoid its effects.
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Affiliation(s)
- Madison M Berl
- Division of Pediatric Neuropsychology, Washington, District of Columbia, Children's Research Institute, Children's National Health System, Washington, DC
| | - Lindsay Walker
- Program on Pediatric Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD
| | - Pooja Modi
- Program on Pediatric Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD
| | - M Okan Irfanoglu
- Program on Pediatric Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD.,Henry Jackson Foundation, Bethesda, Maryland
| | - Joelle E Sarlls
- NMRF, NINDS, National Institutes of Health, Bethesda, Maryland
| | - Amritha Nayak
- Program on Pediatric Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD.,Henry Jackson Foundation, Bethesda, Maryland
| | - Carlo Pierpaoli
- Program on Pediatric Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD.,Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland
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18
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Goveas J, O'Dwyer L, Mascalchi M, Cosottini M, Diciotti S, De Santis S, Passamonti L, Tessa C, Toschi N, Giannelli M. Diffusion-MRI in neurodegenerative disorders. Magn Reson Imaging 2015; 33:853-76. [PMID: 25917917 DOI: 10.1016/j.mri.2015.04.006] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2014] [Revised: 04/18/2015] [Accepted: 04/19/2015] [Indexed: 12/11/2022]
Abstract
The ability to image the whole brain through ever more subtle and specific methods/contrasts has come to play a key role in understanding the basis of brain abnormalities in several diseases. In magnetic resonance imaging (MRI), "diffusion" (i.e. the random, thermally-induced displacements of water molecules over time) represents an extraordinarily sensitive contrast mechanism, and the exquisite structural detail it affords has proven useful in a vast number of clinical as well as research applications. Since diffusion-MRI is a truly quantitative imaging technique, the indices it provides can serve as potential imaging biomarkers which could allow early detection of pathological alterations as well as tracking and possibly predicting subtle changes in follow-up examinations and clinical trials. Accordingly, diffusion-MRI has proven useful in obtaining information to better understand the microstructural changes and neurophysiological mechanisms underlying various neurodegenerative disorders. In this review article, we summarize and explore the main applications, findings, perspectives as well as challenges and future research of diffusion-MRI in various neurodegenerative disorders including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease and degenerative ataxias.
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Affiliation(s)
- Joseph Goveas
- Department of Psychiatry and Behavioral Medicine, and Institute for Health and Society, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Laurence O'Dwyer
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University, Frankfurt, Germany
| | - Mario Mascalchi
- Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy; Quantitative and Functional Neuroradiology Research Program at Meyer Children and Careggi Hospitals of Florence, Florence, Italy
| | - Mirco Cosottini
- Department of Translational Research and New Surgical and Medical Technologies, University of Pisa, Pisa, Italy; Unit of Neuroradiology, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Pisa, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy
| | - Silvia De Santis
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Luca Passamonti
- Institute of Bioimaging and Molecular Physiology, National Research Council, Catanzaro, Italy; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Carlo Tessa
- Division of Radiology, "Versilia" Hospital, AUSL 12 Viareggio, Lido di Camaiore, Italy
| | - Nicola Toschi
- Department of Biomedicine and Prevention, Medical Physics Section, University of Rome "Tor Vergata", Rome, Italy; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Pisa, Italy.
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19
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Todd N, Josephs O, Callaghan MF, Lutti A, Weiskopf N. Prospective motion correction of 3D echo-planar imaging data for functional MRI using optical tracking. Neuroimage 2015; 113:1-12. [PMID: 25783205 PMCID: PMC4441089 DOI: 10.1016/j.neuroimage.2015.03.013] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2014] [Revised: 02/09/2015] [Accepted: 03/08/2015] [Indexed: 12/04/2022] Open
Abstract
We evaluated the performance of an optical camera based prospective motion correction (PMC) system in improving the quality of 3D echo-planar imaging functional MRI data. An optical camera and external marker were used to dynamically track the head movement of subjects during fMRI scanning. PMC was performed by using the motion information to dynamically update the sequence's RF excitation and gradient waveforms such that the field-of-view was realigned to match the subject's head movement. Task-free fMRI experiments on five healthy volunteers followed a 2 × 2 × 3 factorial design with the following factors: PMC on or off; 3.0 mm or 1.5 mm isotropic resolution; and no, slow, or fast head movements. Visual and motor fMRI experiments were additionally performed on one of the volunteers at 1.5 mm resolution comparing PMC on vs PMC off for no and slow head movements. Metrics were developed to quantify the amount of motion as it occurred relative to k-space data acquisition. The motion quantification metric collapsed the very rich camera tracking data into one scalar value for each image volume that was strongly predictive of motion-induced artifacts. The PMC system did not introduce extraneous artifacts for the no motion conditions and improved the time series temporal signal-to-noise by 30% to 40% for all combinations of low/high resolution and slow/fast head movement relative to the standard acquisition with no prospective correction. The numbers of activated voxels (p < 0.001, uncorrected) in both task-based experiments were comparable for the no motion cases and increased by 78% and 330%, respectively, for PMC on versus PMC off in the slow motion cases. The PMC system is a robust solution to decrease the motion sensitivity of multi-shot 3D EPI sequences and thereby overcome one of the main roadblocks to their widespread use in fMRI studies. Data quality was assessed in terms of tSNR, RMSE, and task fMRI t-statistics. Correction improved tSNR by 30% to 40%. Percent reduction in RMSE as a function of motion level was characterized. Number of active voxels for motor and visual tasks increased significantly.
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Affiliation(s)
- Nick Todd
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK.
| | - Oliver Josephs
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK
| | - Martina F Callaghan
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK
| | - Antoine Lutti
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK; Laboratoire de Recherche en Neuroimagerie, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Nikolaus Weiskopf
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK
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20
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Mohammadi S, Tabelow K, Ruthotto L, Feiweier T, Polzehl J, Weiskopf N. High-resolution diffusion kurtosis imaging at 3T enabled by advanced post-processing. Front Neurosci 2015; 8:427. [PMID: 25620906 PMCID: PMC4285740 DOI: 10.3389/fnins.2014.00427] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2014] [Accepted: 12/05/2014] [Indexed: 12/13/2022] Open
Abstract
Diffusion Kurtosis Imaging (DKI) is more sensitive to microstructural differences and can be related to more specific micro-scale metrics (e.g., intra-axonal volume fraction) than diffusion tensor imaging (DTI), offering exceptional potential for clinical diagnosis and research into the white and gray matter. Currently DKI is acquired only at low spatial resolution (2–3 mm isotropic), because of the lower signal-to-noise ratio (SNR) and higher artifact level associated with the technically more demanding DKI. Higher spatial resolution of about 1 mm is required for the characterization of fine white matter pathways or cortical microstructure. We used restricted-field-of-view (rFoV) imaging in combination with advanced post-processing methods to enable unprecedented high-quality, high-resolution DKI (1.2 mm isotropic) on a clinical 3T scanner. Post-processing was advanced by developing a novel method for Retrospective Eddy current and Motion ArtifacT Correction in High-resolution, multi-shell diffusion data (REMATCH). Furthermore, we applied a powerful edge preserving denoising method, denoted as multi-shell orientation-position-adaptive smoothing (msPOAS). We demonstrated the feasibility of high-quality, high-resolution DKI and its potential for delineating highly myelinated fiber pathways in the motor cortex. REMATCH performs robustly even at the low SNR level of high-resolution DKI, where standard EC and motion correction failed (i.e., produced incorrectly aligned images) and thus biased the diffusion model fit. We showed that the combination of REMATCH and msPOAS increased the contrast between gray and white matter in mean kurtosis (MK) maps by about 35% and at the same time preserves the original distribution of MK values, whereas standard Gaussian smoothing strongly biases the distribution.
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Affiliation(s)
- Siawoosh Mohammadi
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London London, UK ; Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf Hamburg, Germany
| | - Karsten Tabelow
- Stochastic Algorithms and Nonparametric Statistics, Weierstrass Institute for Applied Analysis and Stochastics Berlin, Germany
| | - Lars Ruthotto
- Department of Earth, Ocean and Atmospheric Sciences, The University of British Columbia Vancouver, BC, Canada
| | | | - Jörg Polzehl
- Stochastic Algorithms and Nonparametric Statistics, Weierstrass Institute for Applied Analysis and Stochastics Berlin, Germany
| | - Nikolaus Weiskopf
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London London, UK
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Becker SMA, Tabelow K, Mohammadi S, Weiskopf N, Polzehl J. Adaptive smoothing of multi-shell diffusion weighted magnetic resonance data by msPOAS. Neuroimage 2014; 95:90-105. [PMID: 24680711 PMCID: PMC4073655 DOI: 10.1016/j.neuroimage.2014.03.053] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2013] [Revised: 01/06/2014] [Accepted: 03/18/2014] [Indexed: 11/08/2022] Open
Abstract
We present a novel multi-shell position-orientation adaptive smoothing (msPOAS) method for diffusion weighted magnetic resonance data. Smoothing in voxel and diffusion gradient space is embedded in an iterative adaptive multiscale approach. The adaptive character avoids blurring of the inherent structures and preserves discontinuities. The simultaneous treatment of all q-shells improves the stability compared to single-shell approaches such as the original POAS method. The msPOAS implementation simplifies and speeds up calculations, compared to POAS, facilitating its practical application. Simulations and heuristics support the face validity of the technique and its rigorousness. The characteristics of msPOAS were evaluated on single and multi-shell diffusion data of the human brain. Significant reduction in noise while preserving the fine structure was demonstrated for diffusion weighted images, standard DTI analysis and advanced diffusion models such as NODDI. MsPOAS effectively improves the poor signal-to-noise ratio in highly diffusion weighted multi-shell diffusion data, which is required by recent advanced diffusion micro-structure models. We demonstrate the superiority of the new method compared to other advanced denoising methods. Method for structure preserving smoothing multi-shell dMRI data Does not rely on any dMRI diffusion model Outperforms naive single-shell POAS and other approaches Feasible for real data application Implemented within a freely available package dti for the R Language
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Affiliation(s)
- S M A Becker
- Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany
| | - K Tabelow
- Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany.
| | - S Mohammadi
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, United Kingdom
| | - N Weiskopf
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, United Kingdom
| | - J Polzehl
- Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany
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22
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Giannelli M, Sghedoni R, Iacconi C, Iori M, Traino AC, Guerrisi M, Mascalchi M, Toschi N, Diciotti S. MR scanner systems should be adequately characterized in diffusion-MRI of the breast. PLoS One 2014; 9:e86280. [PMID: 24489711 PMCID: PMC3904912 DOI: 10.1371/journal.pone.0086280] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2013] [Accepted: 12/12/2013] [Indexed: 12/13/2022] Open
Abstract
Breast imaging represents a relatively recent and promising field of application of quantitative diffusion-MRI techniques. In view of the importance of guaranteeing and assessing its reliability in clinical as well as research settings, the aim of this study was to specifically characterize how the main MR scanner system-related factors affect quantitative measurements in diffusion-MRI of the breast. In particular, phantom acquisitions were performed on three 1.5 T MR scanner systems by different manufacturers, all equipped with a dedicated multi-channel breast coil as well as acquisition sequences for diffusion-MRI of the breast. We assessed the accuracy, inter-scan and inter-scanner reproducibility of the mean apparent diffusion coefficient measured along the main orthogonal directions (<ADC>) as well as of diffusion-tensor imaging (DTI)-derived mean diffusivity (MD) measurements. Additionally, we estimated spatial non-uniformity of <ADC> (NU<ADC>) and MD (NUMD) maps. We showed that the signal-to-noise ratio as well as overall calibration of high strength diffusion gradients system in typical acquisition sequences for diffusion-MRI of the breast varied across MR scanner systems, introducing systematic bias in the measurements of diffusion indices. While <ADC> and MD values were not appreciably different from each other, they substantially varied across MR scanner systems. The mean of the accuracies of measured <ADC> and MD was in the range [−2.3%,11.9%], and the mean of the coefficients of variation for <ADC> and MD measurements across MR scanner systems was 6.8%. The coefficient of variation for repeated measurements of both <ADC> and MD was < 1%, while NU<ADC> and NUMD values were <4%. Our results highlight that MR scanner system-related factors can substantially affect quantitative diffusion-MRI of the breast. Therefore, a specific quality control program for assessing and monitoring the performance of MR scanner systems for diffusion-MRI of the breast is highly recommended at every site, especially in multicenter and longitudinal studies.
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Affiliation(s)
- Marco Giannelli
- Medical Physics Unit, Pisa University Hospital “Azienda Ospedaliero-Universitaria Pisana”, Pisa, Italy
- * E-mail:
| | - Roberto Sghedoni
- Department of Oncology and Advanced Techniques, Medical Physics Unit, IRCCS-Arcispedale Santa Maria Nuova, Reggio Emilia, Italy
| | - Chiara Iacconi
- Division of Radiology, Breast Unit, Massa Hospital, Azienda USL Massa e Carrara, Massa, Italy
| | - Mauro Iori
- Department of Oncology and Advanced Techniques, Medical Physics Unit, IRCCS-Arcispedale Santa Maria Nuova, Reggio Emilia, Italy
| | - Antonio Claudio Traino
- Medical Physics Unit, Pisa University Hospital “Azienda Ospedaliero-Universitaria Pisana”, Pisa, Italy
| | - Maria Guerrisi
- Department of Biomedicine and Prevention, Medical Physics Section, University of Rome “Tor Vergata”, Rome, Italy
| | - Mario Mascalchi
- Department of Clinical and Experimental Biomedical Sciences, University of Florence, Florence, Italy
| | - Nicola Toschi
- Department of Biomedicine and Prevention, Medical Physics Section, University of Rome “Tor Vergata”, Rome, Italy
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Stefano Diciotti
- Department of Clinical and Experimental Biomedical Sciences, University of Florence, Florence, Italy
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Cesena, Italy
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23
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Zhang B, Xu Y, Zhu B, Kantarci K. The role of diffusion tensor imaging in detecting microstructural changes in prodromal Alzheimer's disease. CNS Neurosci Ther 2013; 20:3-9. [PMID: 24330534 DOI: 10.1111/cns.12166] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2013] [Revised: 07/23/2013] [Accepted: 07/23/2013] [Indexed: 12/13/2022] Open
Abstract
The MRI technique diffusion tensor imaging (DTI) is reviewed along with microstructural changes associated with prodromal Alzheimer's disease (AD) as a potential biomarker for clinical applications. The prodromal stage of AD is characterized by mild cognitive impairment (MCI), representing a transitional state between normal aging and AD. Microstructural abnormalities on DTI are promising in vivo biomarkers of gray and white matter changes associated with the progression of AD pathology. Elevated mean diffusivity and decreased fractional anisotropy are consistently found in prodromal AD, and even in cognitively normal elderly who progress to MCI. However, quality of parameter maps may be affected by artifacts of motion, susceptibility, and eddy current-induced distortions. The DTI maps are typically analyzed by region-of-interest or voxel-based analytic techniques such as tract-based spatial statistics. DTI-based index of diffusivity is complementary to macrostructural gray matter changes in the hippocampus in detecting prodromal AD. Breakdown of structural connectivity measured with DTI may impact cognitive performance during early AD. Furthermore, assessment of hippocampal connections may help in understanding the cerebral organization and remodeling associated with treatment response.
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Affiliation(s)
- Bing Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China; Department of Radiology, Mayo Clinic, Rochester, MN, USA
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Weiskopf N, Suckling J, Williams G, Correia MM, Inkster B, Tait R, Ooi C, Bullmore ET, Lutti A. Quantitative multi-parameter mapping of R1, PD(*), MT, and R2(*) at 3T: a multi-center validation. Front Neurosci 2013; 7:95. [PMID: 23772204 PMCID: PMC3677134 DOI: 10.3389/fnins.2013.00095] [Citation(s) in RCA: 348] [Impact Index Per Article: 31.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2013] [Accepted: 05/18/2013] [Indexed: 02/02/2023] Open
Abstract
Multi-center studies using magnetic resonance imaging facilitate studying small effect sizes, global population variance and rare diseases. The reliability and sensitivity of these multi-center studies crucially depend on the comparability of the data generated at different sites and time points. The level of inter-site comparability is still controversial for conventional anatomical T1-weighted MRI data. Quantitative multi-parameter mapping (MPM) was designed to provide MR parameter measures that are comparable across sites and time points, i.e., 1 mm high-resolution maps of the longitudinal relaxation rate (R1 = 1/T1), effective proton density (PD(*)), magnetization transfer saturation (MT) and effective transverse relaxation rate (R2(*) = 1/T2(*)). MPM was validated at 3T for use in multi-center studies by scanning five volunteers at three different sites. We determined the inter-site bias, inter-site and intra-site coefficient of variation (CoV) for typical morphometric measures [i.e., gray matter (GM) probability maps used in voxel-based morphometry] and the four quantitative parameters. The inter-site bias and CoV were smaller than 3.1 and 8%, respectively, except for the inter-site CoV of R2(*) (<20%). The GM probability maps based on the MT parameter maps had a 14% higher inter-site reproducibility than maps based on conventional T1-weighted images. The low inter-site bias and variance in the parameters and derived GM probability maps confirm the high comparability of the quantitative maps across sites and time points. The reliability, short acquisition time, high resolution and the detailed insights into the brain microstructure provided by MPM makes it an efficient tool for multi-center imaging studies.
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Affiliation(s)
- Nikolaus Weiskopf
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College LondonLondon, UK,*Correspondence: Nikolaus Weiskopf, Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK e-mail:
| | - John Suckling
- Department of Psychiatry, University of CambridgeCambridge, UK,Behavioural and Clinical Neuroscience Institute, University of CambridgeCambridge, UK,Cambridgeshire and Peterborough NHS Foundation TrustCambridge, UK
| | - Guy Williams
- Behavioural and Clinical Neuroscience Institute, University of CambridgeCambridge, UK,Department of Clinical Neuroscience, Wolfson Brain Imaging Centre, University of CambridgeCambridge, UK
| | | | - Becky Inkster
- Department of Psychiatry, University of CambridgeCambridge, UK
| | - Roger Tait
- Behavioural and Clinical Neuroscience Institute, University of CambridgeCambridge, UK
| | - Cinly Ooi
- Department of Psychiatry, University of CambridgeCambridge, UK,Behavioural and Clinical Neuroscience Institute, University of CambridgeCambridge, UK
| | - Edward T. Bullmore
- Department of Psychiatry, University of CambridgeCambridge, UK,Behavioural and Clinical Neuroscience Institute, University of CambridgeCambridge, UK,Cambridgeshire and Peterborough NHS Foundation TrustCambridge, UK,GlaxoSmithKline, Clinical Unit Cambridge, Addenbrooke's HospitalCambridge, UK
| | - Antoine Lutti
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College LondonLondon, UK,Laboratoire de recherche en neuroimagerie, Département des neurosciences cliniques, CHUV, University of LausanneLausanne, Switzerland
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25
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Mohammadi S, Freund P, Feiweier T, Curt A, Weiskopf N. The impact of post-processing on spinal cord diffusion tensor imaging. Neuroimage 2013; 70:377-85. [PMID: 23298752 PMCID: PMC3605597 DOI: 10.1016/j.neuroimage.2012.12.058] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2012] [Revised: 12/20/2012] [Accepted: 12/22/2012] [Indexed: 01/19/2023] Open
Abstract
Diffusion tensor imaging (DTI) provides information about the microstructure in the brain and spinal cord. While new neuroimaging techniques have significantly advanced the accuracy and sensitivity of DTI of the brain, the quality of spinal cord DTI data has improved less. This is in part due to the small size of the spinal cord (ca. 1cm diameter) and more severe instrumental (e.g. eddy current) and physiological (e.g. cardiac pulsation) artefacts present in spinal cord DTI. So far, the improvements in image quality and resolution have resulted from cardiac gating and new acquisition approaches (e.g. reduced field-of-view techniques). The use of retrospective correction methods is not well established for spinal cord DTI. The aim of this paper is to develop an improved post-processing pipeline tailored for DTI data of the spinal cord with increased quality. For this purpose, we compared two eddy current and motion correction approaches using three-dimensional affine (3D-affine) and slice-wise registrations. We also introduced a new robust-tensor-fitting method that controls for whole-volume outliers. Although in general 3D-affine registration improves data quality, occasionally it can lead to misregistrations and biassed tensor estimates. The proposed robust tensor fitting reduced misregistration-related bias and yielded more reliable tensor estimates. Overall, the combination of slice-wise motion correction, eddy current correction, and robust tensor fitting yielded the best results. It increased the contrast-to-noise ratio (CNR) in FA maps by about 30% and reduced intra-subject variation in fractional anisotropy (FA) maps by 18%. The higher quality of FA maps allows for a better distinction between grey and white matter without increasing scan time and is compatible with any multi-directional DTI acquisition scheme.
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Affiliation(s)
- Siawoosh Mohammadi
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, UK.
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26
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Wang LH, Bucelli RC, Patrick E, Rajderkar D, Alvarez Iii E, Lim MM, Debruin G, Sharma V, Dahiya S, Schmidt RE, Benzinger TS, Ward BA, Ances BM. Role of magnetic resonance imaging, cerebrospinal fluid, and electroencephalogram in diagnosis of sporadic Creutzfeldt-Jakob disease. J Neurol 2012; 260:498-506. [PMID: 22968768 DOI: 10.1007/s00415-012-6664-6] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2012] [Revised: 07/25/2012] [Accepted: 08/25/2012] [Indexed: 11/28/2022]
Abstract
Sporadic Creutzfeldt-Jakob disease (sCJD) is a rapidly progressive dementia (RPD) that can be difficult to identify antemortem, with definitive diagnosis requiring tissue confirmation. We describe the clinical, magnetic resonance imaging (MRI), cerebrospinal fluid (CSF), and electroencephalogram (EEG) measures of a small cohort of 30 patients evaluated for RPD. Clinical and diagnostic measures were cross-sectionally obtained from 17 sCJD patients (15 definite, two probable), 13 non-prion rapidly progressive dementia patients (npRPD), and 18 unimpaired controls. In a subset of patients (nine sCJD and nine npRPD) diffusion tensor imaging (DTI) measures [fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD)] were also obtained for the caudate, corpus callosum, posterior limb of the internal capsule, pulvinar, precuneus, and frontal lobe. Differences among groups were assessed by an analysis of variance. Compared to npRPD individuals, sCJD patients had cerebellar dysfunction, significantly higher CSF tau, "positive" CSF 14-3-3, and hyperintensities on diffusion-weighted imaging (DWI) that met previously established imaging criteria for sCJD. EEG changes were similar for the two groups. In addition, sCJD patients had significant decreases in DTI measures (MD, AD, RD but not FA) within the caudate and pulvinar compared to either npRPD patients or unimpaired controls. Our results confirm that CSF abnormalities and MRI (especially DWI) can assist in distinguishing sCJD patients from npRPD patients. Future longitudinal studies using multiple measures (including CSF and MRI) are needed for evaluating pathological changes seen in sCJD patients.
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Affiliation(s)
- Leo H Wang
- Department of Neurology, Washington University School of Medicine, Box 8111, 660 South Euclid Avenue, Saint Louis, MO 63110, USA
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27
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Mohammadi S, Hutton C, Nagy Z, Josephs O, Weiskopf N. Retrospective correction of physiological noise in DTI using an extended tensor model and peripheral measurements. Magn Reson Med 2012; 70:358-69. [PMID: 22936599 PMCID: PMC3792745 DOI: 10.1002/mrm.24467] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2012] [Revised: 07/23/2012] [Accepted: 08/01/2012] [Indexed: 11/07/2022]
Abstract
Diffusion tensor imaging is widely used in research and clinical applications, but this modality is highly sensitive to artefacts. We developed an easy-to-implement extension of the original diffusion tensor model to account for physiological noise in diffusion tensor imaging using measures of peripheral physiology (pulse and respiration), the so-called extended tensor model. Within the framework of the extended tensor model two types of regressors, which respectively modeled small (linear) and strong (nonlinear) variations in the diffusion signal, were derived from peripheral measures. We tested the performance of four extended tensor models with different physiological noise regressors on nongated and gated diffusion tensor imaging data, and compared it to an established data-driven robust fitting method. In the brainstem and cerebellum the extended tensor models reduced the noise in the tensor-fit by up to 23% in accordance with previous studies on physiological noise. The extended tensor model addresses both large-amplitude outliers and small-amplitude signal-changes. The framework of the extended tensor model also facilitates further investigation into physiological noise in diffusion tensor imaging. The proposed extended tensor model can be readily combined with other artefact correction methods such as robust fitting and eddy current correction.
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Affiliation(s)
- Siawoosh Mohammadi
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK.
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28
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De Santis S, Evans CJ, Jones DK. RAPID: A routine assurance pipeline for imaging of diffusion. Magn Reson Med 2012; 70:490-6. [DOI: 10.1002/mrm.24465] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2012] [Revised: 07/02/2012] [Accepted: 07/26/2012] [Indexed: 11/11/2022]
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29
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Freund P, Curt A, Friston K, Thompson A. Tracking changes following spinal cord injury: insights from neuroimaging. Neuroscientist 2012; 19:116-28. [PMID: 22730072 PMCID: PMC4107798 DOI: 10.1177/1073858412449192] [Citation(s) in RCA: 67] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Traumatic spinal cord injury is often disabling and recovery of function is limited. As a
consequence of damage, both spinal cord and brain undergo anatomical and functional
changes. Besides clinical measures of recovery, biomarkers that can detect early
anatomical and functional changes might be useful in determining clinical outcome—during
the course of rehabilitation and recovery—as well as furnishing a tool to evaluate novel
treatment interventions and their mechanisms of action. Recent evidence suggests an
interesting three-way relationship between neurological deficit and changes in the spinal
cord and of the brain and that, importantly, noninvasive magnetic resonance imaging
techniques, both structural and functional, provide a sensitive tool to lay out these
interactions. This review describes recent findings from multimodal imaging studies of
remote anatomical changes (i.e., beyond the lesion site), cortical reorganization, and
their relationship to clinical disability. These developments in this field may improve
our understanding of effects on the nervous system that are attributable to the injury
itself and will allow their distinction from changes that result from rehabilitation
(i.e., functional retraining) and from interventions affecting the nervous system directly
(i.e., neuroprotection or regeneration).
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Affiliation(s)
- Patrick Freund
- Department of Brain Repair & Rehabilitation, UCL Institute of Neurology, UCL, London, UK.
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30
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Mohammadi S, Keller SS, Glauche V, Kugel H, Jansen A, Hutton C, Flöel A, Deppe M. The influence of spatial registration on detection of cerebral asymmetries using voxel-based statistics of fractional anisotropy images and TBSS. PLoS One 2012; 7:e36851. [PMID: 22679481 PMCID: PMC3367973 DOI: 10.1371/journal.pone.0036851] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2011] [Accepted: 04/07/2012] [Indexed: 02/02/2023] Open
Abstract
The sensitivity of diffusion tensor imaging (DTI) for detecting microstructural white matter alterations has motivated the application of voxel-based statistics (VBS) to fractional anisotropy (FA) images (FA-VBS). However, detected group differences may depend on the spatial registration method used. The objective of this study was to investigate the influence of spatial registration on detecting cerebral asymmetries in FA-VBS analyses with reference to data obtained using Tract-Based Spatial Statistics (TBSS). In the first part of this study we performed FA-VBS analyses using three single-contrast and one multi-contrast registration: (i) whole-brain registration based on T2 contrast, (ii) whole-brain registration based on FA contrast, (iii) individual-hemisphere registration based on FA contrast, and (iv) a combination of (i) and (iii). We then compared the FA-VBS results with those obtained from TBSS. We found that the FA-VBS results depended strongly on the employed registration approach, with the best correspondence between FA-VBS and TBSS results when approach (iv), the “multi-contrast individual-hemisphere” method was employed. In the second part of the study, we investigated the spatial distribution of residual misregistration for each registration approach and the effect on FA-VBS results. For the FA-VBS analyses using the three single-contrast registration methods, we identified FA asymmetries that were (a) located in regions prone to misregistrations, (b) not detected by TBSS, and (c) specific to the applied registration approach. These asymmetries were considered candidates for apparent FA asymmetries due to systematic misregistrations associated with the FA-VBS approach. Finally, we demonstrated that the “multi-contrast individual-hemisphere” approach showed the least residual spatial misregistrations and thus might be most appropriate for cerebral FA-VBS analyses.
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