101
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Huang R, Chen Y, Li W, Zhang X. An evidence-based approach to assess the accuracy of diffusion kurtosis imaging in characterization of gliomas. Medicine (Baltimore) 2018; 97:e13068. [PMID: 30383687 PMCID: PMC6221635 DOI: 10.1097/md.0000000000013068] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
OBJECTIVE Accurate and noninvasive pathologic grading of glioma patients before surgery was crucial to guiding clinicians to select appropriate treatment and improve patient prognosis. This study was performed to investigate the potential diagnostic value of diffusion kurtosis imaging (DKI) to distinguish high-grade gliomas (HGGs) from low-grade gliomas (LGGs) based on an evidence-based approach. METHODS Relevant articles that used DKI to distinguish HGG from LGG in Embase, PubMed, China Knowledge Resource Integrated database (CNKI), Web of Knowledge, and Cochrane Libraries databases were electronically searched to April 31, 2018 by 2 reviewers. All analysis was performed by using Meta-disc1.4 and Stata. Influence factors on the diagnostic accuracy were evaluated using meta-regression analysis. RESULTS Five eligible studies were included in this meta-analysis. The pooled sensitivity (SEN) and specificity (SPE) was 91% (confidence interval [CI]: 0.78-0.96; P = .02) and 91% (CI: 0.80-0.97; P = .01). The pooled data showed that diagnostic odds ratio (DOR) of DKI was 79.75 (CI: 31.57-201.45). The area under the curve (AUC) of summary receiver operating characteristic curve was 0.96. There is no evidence that our research has a threshold effect (Spearman correlation coefficient: 0.300, P = .624) and publication bias. Meta regression analysis identified that country, language, field strength, and parameter of magnetic resonance imaging had no significant effect on diagnostic performance. CONCLUSION The present meta-analysis shows that the mean kurtosis values derived from DKI may be useful in characterization of gliomas with high sensitivity and specificity. Taken into consideration the small sample of this study, we need to be cautious when interpreting the results of this study.
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Affiliation(s)
- Ruiyu Huang
- Department of MRI, The Second Affiliated Hospital of Shaanxi University of Chinese Medicine
| | - Yanni Chen
- Department of Radiology, XianYang Rainbow Hospital, XianYang, Shaanxi
| | - Wenfei Li
- Department of Radiology, The First Hospital of Qinhuangdao, Qinhuangdao, Hebei, China
| | - Xvfeng Zhang
- Department of Radiology, XianYang Rainbow Hospital, XianYang, Shaanxi
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102
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Avram AV, Sarlls JE, Basser PJ. Measuring non-parametric distributions of intravoxel mean diffusivities using a clinical MRI scanner. Neuroimage 2018; 185:255-262. [PMID: 30326294 DOI: 10.1016/j.neuroimage.2018.10.030] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2018] [Revised: 09/19/2018] [Accepted: 10/09/2018] [Indexed: 11/17/2022] Open
Abstract
We measure spectra of water mobilities (i.e., mean diffusivities) from intravoxel pools in brain tissues of healthy subjects with a non-parametric approach. Using a single-shot isotropic diffusion encoding (IDE) preparation, we eliminate signal confounds caused by anisotropic diffusion, including microscopic anisotropy, and acquire in vivo diffusion-weighted images (DWIs) over a wide range of diffusion sensitizations. We analyze the measured IDE signal decays using a regularized inverse laplace transform (ILT) to derive a probability distribution of mean diffusivities of tissue water in each voxel. Based on numerical simulations we assess the sensitivity and accuracy of our ILT analysis and optimize an experimental protocol for use with clinical MRI scanners. In vivo spectra of intravoxel mean diffusivities measured in healthy subjects generally show single-peak distributions throughout the brain parenchyma, with small differences in peak location and shape among white matter, cortical and subcortical gray matter, and cerebrospinal fluid. Mean diffusivity distributions (MDDs) with multiple peaks are observed primarily in voxels at tissue interfaces and are likely due to partial volume contributions. To quantify tissue-specific MDDs with improved statistical power, we average voxel-wise normalized MDDs in corresponding regions-of-interest (ROIs). This non-parametric, rotation-invariant assessment of isotropic diffusivities of tissue water may reflect important microstructural information, such as cell packing and cell size, and active physiological processes, such as water transport and exchange, which may enhance biological specificity in the clinical diagnosis and characterization of ischemic stroke, cancer, neuroinflammation, and neurodegenerative disorders and diseases.
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Affiliation(s)
- Alexandru V Avram
- National Institute of Biomedical Imaging and Bioengineering, National Institute of Health, Bethesda, MD, 20892, USA.
| | - Joelle E Sarlls
- National Institute of Neurological Disorders and Stroke, National Institute of Health, Bethesda, MD, 20892, USA
| | - Peter J Basser
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institute of Health, Bethesda, MD, 20892, USA
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103
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Tang L, Zhou XJ. Diffusion MRI of cancer: From low to high b-values. J Magn Reson Imaging 2018; 49:23-40. [PMID: 30311988 DOI: 10.1002/jmri.26293] [Citation(s) in RCA: 118] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 07/20/2018] [Accepted: 07/23/2018] [Indexed: 12/14/2022] Open
Abstract
Following its success in early detection of cerebral ischemia, diffusion-weighted imaging (DWI) has been increasingly used in cancer diagnosis and treatment evaluation. These applications are propelled by the rapid development of novel diffusion models to extract biologically valuable information from diffusion-weighted MR signals, and significant advances in MR hardware that has enabled image acquisition with high b-values. This article reviews recent technical developments and clinical applications in cancer imaging using DWI, with a special emphasis on high b-value diffusion models. The article is organized in four sections. First, we provide an overview of diffusion models that are relevant to cancer imaging. The model parameters are discussed in relation to three tissue properties-cellularity, vascularity, and microstructures. An emphasis is placed on characterization of microstructural heterogeneity, given its novelty and close relevance to cancer. Second, we illustrate diffusion MR clinical applications in each of the following three categories: 1) cancer detection and diagnosis; 2) cancer grading, staging, and classification; and 3) cancer treatment response prediction and evaluation. Third, we discuss several practical issues, including selection of image acquisition parameters, reproducibility and reliability, motion management, image distortion, etc., that are commonly encountered when applying DWI to cancer in clinical settings. Lastly, we highlight a few ongoing challenges and provide some possible future directions, particularly in the area of establishing standards via well-organized multicenter clinical trials to accelerate clinical translation of advanced DWI techniques to improving cancer care on a large scale. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:23-40.
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Affiliation(s)
- Lei Tang
- Department of Radiology, Peking University Cancer Hospital & Institute, Key laboratory of Carcinogenesis and Translational Research, Beijing, China
| | - Xiaohong Joe Zhou
- Center for MR Research and Departments of Radiology, Neurosurgery, and Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
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104
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Local volume fraction distributions of axons, astrocytes, and myelin in deep subcortical white matter. Neuroimage 2018; 179:275-287. [DOI: 10.1016/j.neuroimage.2018.06.040] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Revised: 05/31/2018] [Accepted: 06/11/2018] [Indexed: 01/28/2023] Open
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105
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Detection of microscopic diffusion anisotropy in human cortical gray matter in vivo with double diffusion encoding. Magn Reson Med 2018; 81:1296-1306. [DOI: 10.1002/mrm.27451] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 06/07/2018] [Accepted: 06/19/2018] [Indexed: 11/07/2022]
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106
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Lin L, Bhawana R, Xue Y, Duan Q, Jiang R, Chen H, Chen X, Sun B, Lin H. Comparative Analysis of Diffusional Kurtosis Imaging, Diffusion Tensor Imaging, and Diffusion-Weighted Imaging in Grading and Assessing Cellular Proliferation of Meningiomas. AJNR Am J Neuroradiol 2018; 39:1032-1038. [PMID: 29748203 DOI: 10.3174/ajnr.a5662] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Accepted: 02/24/2018] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE An accurate evaluation of the World Health Organization grade and cellular proliferation is particularly important in meningiomas. Our aim was to prospectively evaluate and compare diffusional kurtosis imaging, DTI, and DWI metrics in determining the grade and cellular proliferation of meningiomas. MATERIALS AND METHODS Ninety-six consecutive patients with histopathologically confirmed meningiomas were included in this study. Mean kurtosis, radial kurtosis, axial kurtosis, fractional anisotropy, mean diffusivity, and ADC were semiautomatically obtained in the solid components of tumors. Each normalized diffusion value was compared between high-grade meningiomas and low-grade meningiomas using the Mann-Whitney U test. Receiver operating characteristic, multiple logistic regression, and Pearson correlation analysis were used for statistical evaluations. RESULTS Diffusional kurtosis imaging metrics (mean kurtosis, radial kurtosis, and axial kurtosis) were significantly higher in high-grade meningiomas than in low-grade meningiomas (P ≤ .001). Mean diffusivity and ADC were significantly lower in high-grade meningiomas than in low-grade meningiomas (P = .003 and .002). Mean kurtosis had significantly greater area the under curve values than mean diffusivity and fractional anisotropy in differentiating high-grade meningiomas from low-grade meningiomas (P = .038 and .002). Mean kurtosis was the only variable that could be used to independently differentiate high-grade meningiomas and low-grade meningiomas (P < .001). Significant correlations were found between the Ki-67 labeling index and kurtosis metrics (P < .001), as well as for mean diffusivity and ADC (P = .004, and .007). CONCLUSIONS Compared with other diffusion metrics, mean kurtosis may serve as an optimal parameter for evaluating and predicting the meningioma grade. Moreover, diffusion metrics may potentially reflect cellular proliferation.
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Affiliation(s)
- L Lin
- From the Departments of Radiology (L.L., R.B., Y.X., Q.D., R.J., B.S., H.L.)
| | - R Bhawana
- From the Departments of Radiology (L.L., R.B., Y.X., Q.D., R.J., B.S., H.L.)
| | - Y Xue
- From the Departments of Radiology (L.L., R.B., Y.X., Q.D., R.J., B.S., H.L.)
| | - Q Duan
- From the Departments of Radiology (L.L., R.B., Y.X., Q.D., R.J., B.S., H.L.)
| | - R Jiang
- From the Departments of Radiology (L.L., R.B., Y.X., Q.D., R.J., B.S., H.L.)
| | - H Chen
- Pathology (H.C.), Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - X Chen
- Department of Radiology (X.C.), Fujian Cancer Hospital and Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China
| | - B Sun
- From the Departments of Radiology (L.L., R.B., Y.X., Q.D., R.J., B.S., H.L.)
| | - H Lin
- From the Departments of Radiology (L.L., R.B., Y.X., Q.D., R.J., B.S., H.L.)
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107
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Falk Delgado A, Nilsson M, van Westen D, Falk Delgado A. Glioma Grade Discrimination with MR Diffusion Kurtosis Imaging: A Meta-Analysis of Diagnostic Accuracy. Radiology 2018; 287:119-127. [DOI: 10.1148/radiol.2017171315] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Anna Falk Delgado
- From the Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden (Anna Falk Delgado); Department of Neuroradiology, Karolinska University Hospital, Neurocentrum R1, Karolinska Vägen, 17176 Solna, Stockholm, Sweden (Anna Falk Delgado); Departments of Diagnostic Radiology (M.N.) and Clinical Sciences (D.v.W.), Faculty of Medicine, Lund University, Lund, Sweden; and Department of Surgical Sciences, Uppsala University, Uppsala, Sweden (Alberto Falk Delgado)
| | - Markus Nilsson
- From the Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden (Anna Falk Delgado); Department of Neuroradiology, Karolinska University Hospital, Neurocentrum R1, Karolinska Vägen, 17176 Solna, Stockholm, Sweden (Anna Falk Delgado); Departments of Diagnostic Radiology (M.N.) and Clinical Sciences (D.v.W.), Faculty of Medicine, Lund University, Lund, Sweden; and Department of Surgical Sciences, Uppsala University, Uppsala, Sweden (Alberto Falk Delgado)
| | - Danielle van Westen
- From the Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden (Anna Falk Delgado); Department of Neuroradiology, Karolinska University Hospital, Neurocentrum R1, Karolinska Vägen, 17176 Solna, Stockholm, Sweden (Anna Falk Delgado); Departments of Diagnostic Radiology (M.N.) and Clinical Sciences (D.v.W.), Faculty of Medicine, Lund University, Lund, Sweden; and Department of Surgical Sciences, Uppsala University, Uppsala, Sweden (Alberto Falk Delgado)
| | - Alberto Falk Delgado
- From the Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden (Anna Falk Delgado); Department of Neuroradiology, Karolinska University Hospital, Neurocentrum R1, Karolinska Vägen, 17176 Solna, Stockholm, Sweden (Anna Falk Delgado); Departments of Diagnostic Radiology (M.N.) and Clinical Sciences (D.v.W.), Faculty of Medicine, Lund University, Lund, Sweden; and Department of Surgical Sciences, Uppsala University, Uppsala, Sweden (Alberto Falk Delgado)
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108
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Novikov DS, Veraart J, Jelescu IO, Fieremans E. Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI. Neuroimage 2018; 174:518-538. [PMID: 29544816 DOI: 10.1016/j.neuroimage.2018.03.006] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Revised: 02/22/2018] [Accepted: 03/03/2018] [Indexed: 10/17/2022] Open
Abstract
We develop a general analytical and numerical framework for estimating intra- and extra-neurite water fractions and diffusion coefficients, as well as neurite orientational dispersion, in each imaging voxel. By employing a set of rotational invariants and their expansion in the powers of diffusion weighting, we analytically uncover the nontrivial topology of the parameter estimation landscape, showing that multiple branches of parameters describe the measurement almost equally well, with only one of them corresponding to the biophysical reality. A comprehensive acquisition shows that the branch choice varies across the brain. Our framework reveals hidden degeneracies in MRI parameter estimation for neuronal tissue, provides microstructural and orientational maps in the whole brain without constraints or priors, and connects modern biophysical modeling with clinical MRI.
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Affiliation(s)
- Dmitry S Novikov
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA.
| | - Jelle Veraart
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Ileana O Jelescu
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Centre d'Imagerie Biomédicale, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Els Fieremans
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
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109
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Nilsson M, Larsson J, Lundberg D, Szczepankiewicz F, Witzel T, Westin C, Bryskhe K, Topgaard D. Liquid crystal phantom for validation of microscopic diffusion anisotropy measurements on clinical MRI systems. Magn Reson Med 2018; 79:1817-1828. [PMID: 28686785 PMCID: PMC5756689 DOI: 10.1002/mrm.26814] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Revised: 05/21/2017] [Accepted: 06/08/2017] [Indexed: 01/05/2023]
Abstract
PURPOSE To develop a phantom for validating MRI pulse sequences and data processing methods to quantify microscopic diffusion anisotropy in the human brain. METHODS Using a liquid crystal consisting of water, detergent, and hydrocarbon, we designed a 0.5-L spherical phantom showing the theoretically highest possible degree of microscopic anisotropy. Data were acquired on the Connectome scanner using echo-planar imaging signal readout and diffusion encoding with axisymmetric b-tensors of varying magnitude, anisotropy, and orientation. The mean diffusivity, fractional anisotropy (FA), and microscopic FA (µFA) parameters were estimated. RESULTS The phantom was observed to have values of mean diffusivity similar to brain tissue, and relaxation times compatible with echo-planar imaging echo times on the order of 100 ms. The estimated values of µFA were at the theoretical maximum of 1.0, whereas the values of FA spanned the interval from 0.0 to 0.8 as a result of varying orientational order of the anisotropic domains within each voxel. CONCLUSIONS The proposed phantom can be manufactured by mixing three widely available chemicals in volumes comparable to a human head. The acquired data are in excellent agreement with theoretical predictions, showing that the phantom is ideal for validating methods for measuring microscopic diffusion anisotropy on clinical MRI systems. Magn Reson Med 79:1817-1828, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
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Affiliation(s)
- Markus Nilsson
- Diagnostic Radiology, Department of Clinical SciencesLund UniversityLundSweden
| | - Johan Larsson
- Physical Chemistry, Department of ChemistryLund UniversityLundSweden
| | | | | | - Thomas Witzel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | | | | | - Daniel Topgaard
- Physical Chemistry, Department of ChemistryLund UniversityLundSweden
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110
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Nielsen JS, Dyrby TB, Lundell H. Magnetic resonance temporal diffusion tensor spectroscopy of disordered anisotropic tissue. Sci Rep 2018; 8:2930. [PMID: 29440724 PMCID: PMC5811563 DOI: 10.1038/s41598-018-19475-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Accepted: 12/17/2017] [Indexed: 01/09/2023] Open
Abstract
Molecular diffusion measured with diffusion weighted MRI (DWI) offers a probe for tissue microstructure. However, inferring microstructural properties from conventional DWI data is a complex inverse problem and has to account for heterogeneity in sizes, shapes and orientations of the tissue compartments contained within an imaging voxel. Alternative experimental means for disentangling the signal signatures of such features could provide a stronger link between the data and its interpretation. Double diffusion encoding (DDE) offers the possibility to factor out variation in compartment shapes from orientational dispersion of anisotropic domains by measuring the correlation between diffusivity in multiple directions. Time dependence of the diffusion is another effect reflecting the dimensions and distributions of barriers. In this paper we extend on DDE with a modified version of the oscillating gradient spin echo (OGSE) experiment, giving a basic contrast mechanism closely linked to both the temporal diffusion spectrum and the compartment anisotropy. We demonstrate our new method on post mortem brain tissue and show that we retrieve the correct temporal diffusion tensor spectrum in synthetic data from Monte Carlo simulations of random walks in a range of disordered geometries of different sizes and shapes.
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Affiliation(s)
- Jonathan Scharff Nielsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Copenhagen, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Tim B Dyrby
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Copenhagen, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Copenhagen, Denmark.
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111
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de Almeida Martins JP, Topgaard D. Multidimensional correlation of nuclear relaxation rates and diffusion tensors for model-free investigations of heterogeneous anisotropic porous materials. Sci Rep 2018; 8:2488. [PMID: 29410433 PMCID: PMC5802831 DOI: 10.1038/s41598-018-19826-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 01/08/2018] [Indexed: 11/25/2022] Open
Abstract
Despite their widespread use in non-invasive studies of porous materials, conventional MRI methods yield ambiguous results for microscopically heterogeneous materials such as brain tissue. While the forward link between microstructure and MRI observables is well understood, the inverse problem of separating the signal contributions from different microscopic pores is notoriously difficult. Here, we introduce an experimental protocol where heterogeneity is resolved by establishing 6D correlations between the individual values of isotropic diffusivity, diffusion anisotropy, orientation of the diffusion tensor, and relaxation rates of distinct populations. Such procedure renders the acquired signal highly specific to the sample's microstructure, and allows characterization of the underlying pore space without prior assumptions on the number and nature of distinct microscopic environments. The experimental feasibility of the suggested method is demonstrated on a sample designed to mimic the properties of nerve tissue. If matched to the constraints of whole body scanners, this protocol could allow for the unconstrained determination of the different types of tissue that compose the living human brain.
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Affiliation(s)
| | - Daniel Topgaard
- Division of Physical Chemistry, Department of Chemistry, Lund University, Lund, Sweden
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112
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Avram AV, Sarlls JE, Hutchinson E, Basser PJ. Efficient experimental designs for isotropic generalized diffusion tensor MRI (IGDTI). Magn Reson Med 2018; 79:180-194. [PMID: 28480613 PMCID: PMC5675833 DOI: 10.1002/mrm.26656] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Revised: 01/31/2017] [Accepted: 02/05/2017] [Indexed: 11/10/2022]
Abstract
PURPOSE We propose a new generalized diffusion tensor imaging (GDTI) experimental design and analysis framework for efficiently measuring orientationally averaged diffusion-weighted images (DWIs), which remove bulk signal modulations attributed to diffusion anisotropy and quantify isotropic higher-order diffusion tensors (HOT). We illustrate how this framework accelerates the clinical measurement of rotation-invariant tissue microstructural parameters derived from HOT, such as the HOT-Trace and the mean t-kurtosis. THEORY AND METHODS For a large range of b-values, we compare orientationally averaged DWIs measured with high angular resolution diffusion imaging to those obtained with the proposed isotropic GDTI (IGDTI) experimental design. We compare rotation-invariant microstructural parameters measured with IGDTI to those derived from HOTs measured explicitly with GDTI. RESULTS In both fixed-brain microimaging and in vivo clinical experiments, IGDTI accurately quantifies mean apparent diffusion coefficient (mADC)-weighted DWIs over a wide range of b-values and allows efficient computation of HOT-derived scalar tissue parameters from a small number of DWIs. CONCLUSIONS IGDTI provides direct and accurate estimates of orientationally averaged tissue water mobilities over a wide range of b-values. This efficient method may enable new, sensitive, and quantitative assessments for clinical applications in which changes in mADC can be observe,d such as detecting and characterizing stroke, cancers, and neurodegenerative diseases. Magn Reson Med 79:180-194, 2018. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.
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Affiliation(s)
- Alexandru V. Avram
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Joelle E. Sarlls
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Elizabeth Hutchinson
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Peter J. Basser
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
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113
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Yang G, Tian Q, Leuze C, Wintermark M, McNab JA. Double diffusion encoding MRI for the clinic. Magn Reson Med 2017; 80:507-520. [PMID: 29266375 DOI: 10.1002/mrm.27043] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Revised: 11/17/2017] [Accepted: 11/18/2017] [Indexed: 12/13/2022]
Abstract
PURPOSE The purpose of this study is to develop double diffusion encoding (DDE) MRI methods for clinical use. Microscopic diffusion anisotropy measurements from DDE promise greater specificity to changes in tissue microstructure compared with conventional diffusion tensor imaging, but implementation of DDE sequences on whole-body MRI scanners is challenging because of the limited gradient strengths and lengthy acquisition times. METHODS A custom single-refocused DDE sequence was implemented on a 3T whole-body scanner. The DDE gradient orientation scheme and sequence parameters were optimized based on a Gaussian diffusion assumption. Using an optimized 5-min DDE acquisition, microscopic fractional anisotropy (μFA) maps were acquired for the first time in multiple sclerosis patients. RESULTS Based on simulations and in vivo human measurements, six parallel and six orthogonal diffusion gradient pairs were found to be the minimum number of diffusion gradient pairs necessary to produce a rotationally invariant measurement of μFA. Simulations showed that optimal precision and accuracy of μFA measurements were obtained using b-values between 1500 and 3000 s/mm2 . The μFA maps showed improved delineation of multiple sclerosis lesions compared with conventional fractional anisotropy and distinct contrast from T2 -weighted fluid attenuated inversion recovery and T1 -weighted imaging. CONCLUSION The μFA maps can be measured using DDE in a clinical setting and may provide new opportunities for characterizing multiple sclerosis lesions and other types of tissue degeneration. Magn Reson Med 80:507-520, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Grant Yang
- Department of Electrical Engineering, Stanford University, Stanford, California, USA.,Department of Radiology, Stanford University, Stanford, California, USA
| | - Qiyuan Tian
- Department of Electrical Engineering, Stanford University, Stanford, California, USA.,Department of Radiology, Stanford University, Stanford, California, USA
| | - Christoph Leuze
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Max Wintermark
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Jennifer A McNab
- Department of Radiology, Stanford University, Stanford, California, USA
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114
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Chuhutin A, Hansen B, Jespersen SN. Precision and accuracy of diffusion kurtosis estimation and the influence of b-value selection. NMR IN BIOMEDICINE 2017; 30:10.1002/nbm.3777. [PMID: 28841758 PMCID: PMC5715207 DOI: 10.1002/nbm.3777] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Revised: 06/14/2017] [Accepted: 07/03/2017] [Indexed: 05/22/2023]
Abstract
Diffusion kurtosis imaging (DKI) is an extension of diffusion tensor imaging that accounts for leading non-Gaussian diffusion effects. In DKI studies, a wide range of different gradient strengths (b-values) is used, which is known to affect the estimated diffusivity and kurtosis parameters. Hence there is a need to assess the accuracy and precision of the estimated parameters as a function of b-value. This work examines the error in the estimation of mean of the kurtosis tensor (MKT) with respect to the ground truth, using simulations based on a biophysical model for both gray (GM) and white (WM) matter. Model parameters are derived from densely sampled experimental data acquired in ex vivo rat brain and in vivo human brain. Additionally, the variability of MKT is studied using the experimental data. Prevalent fitting protocols are implemented and investigated. The results show strong dependence on the maximum b-value of both net relative error and standard deviation of error for all of the employed fitting protocols. The choice of b-values with minimum MKT estimation error and standard deviation of error was found to depend on the protocol type and the tissue. Protocols that utilize two terms of the cumulant expansion (DKI) were found to achieve minimum error in GM at b-values less than 1 ms/μm2 , whereas maximal b-values of about 2.5 ms/μm2 were found to be optimal in WM. Protocols including additional higher order terms of the cumulant expansion were found to provide higher accuracy for the more commonly used b-value regime in GM, but were associated with higher error in WM. Averaged over multiple voxels, a net average error of around 15% for both WM and GM was observed for the optimal b-value choice. These results suggest caution when using DKI generated metrics for microstructural modeling and when comparing results obtained using different fitting techniques and b-values.
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Affiliation(s)
- Andrey Chuhutin
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Brian Hansen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Sune Nørhøj Jespersen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
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115
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Cancer Metabolism and Tumor Heterogeneity: Imaging Perspectives Using MR Imaging and Spectroscopy. CONTRAST MEDIA & MOLECULAR IMAGING 2017; 2017:6053879. [PMID: 29114178 PMCID: PMC5654284 DOI: 10.1155/2017/6053879] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2017] [Revised: 07/31/2017] [Accepted: 08/27/2017] [Indexed: 12/26/2022]
Abstract
Cancer cells reprogram their metabolism to maintain viability via genetic mutations and epigenetic alterations, expressing overall dynamic heterogeneity. The complex relaxation mechanisms of nuclear spins provide unique and convertible tissue contrasts, making magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) pertinent imaging tools in both clinics and research. In this review, we summarized MR methods that visualize tumor characteristics and its metabolic phenotypes on an anatomical, microvascular, microstructural, microenvironmental, and metabolomics scale. The review will progress from the utilities of basic spin-relaxation contrasts in cancer imaging to more advanced imaging methods that measure tumor-distinctive parameters such as perfusion, water diffusion, magnetic susceptibility, oxygenation, acidosis, redox state, and cell death. Analytical methods to assess tumor heterogeneity are also reviewed in brief. Although the clinical utility of tumor heterogeneity from imaging is debatable, the quantification of tumor heterogeneity using functional and metabolic MR images with development of robust analytical methods and improved MR methods may offer more critical roles of tumor heterogeneity data in clinics. MRI/MRS can also provide insightful information on pharmacometabolomics, biomarker discovery, disease diagnosis and prognosis, and treatment response. With these future directions in mind, we anticipate the widespread utilization of these MR-based techniques in studying in vivo cancer biology to better address significant clinical needs.
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Rydhög AS, Szczepankiewicz F, Wirestam R, Ahlgren A, Westin CF, Knutsson L, Pasternak O. Separating blood and water: Perfusion and free water elimination from diffusion MRI in the human brain. Neuroimage 2017; 156:423-434. [PMID: 28412443 PMCID: PMC5548601 DOI: 10.1016/j.neuroimage.2017.04.023] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2016] [Revised: 04/07/2017] [Accepted: 04/08/2017] [Indexed: 12/21/2022] Open
Abstract
The assessment of the free water fraction in the brain provides important information about extracellular processes such as atrophy and neuroinflammation in various clinical conditions as well as in normal development and aging. Free water estimates from diffusion MRI are assumed to account for freely diffusing water molecules in the extracellular space, but may be biased by other pools of molecules in rapid random motion, such as the intravoxel incoherent motion (IVIM) of blood, where water molecules perfuse in the randomly oriented capillary network. The goal of this work was to separate the signal contribution of the perfusing blood from that of free-water and of other brain diffusivities. The influence of the vascular compartment on the estimation of the free water fraction and other diffusivities was investigated by simulating perfusion in diffusion MRI data. The perfusion effect in the simulations was significant, especially for the estimation of the free water fraction, and was maintained as long as low b-value data were included in the analysis. Two approaches to reduce the perfusion effect were explored in this study: (i) increasing the minimal b-value used in the fitting, and (ii) using a three-compartment model that explicitly accounts for water molecules in the capillary blood. Estimation of the model parameters while excluding low b-values reduced the perfusion effect but was highly sensitive to noise. The three-compartment model fit was more stable and additionally, provided an estimation of the volume fraction of the capillary blood compartment. The three-compartment model thus disentangles the effects of free water diffusion and perfusion, which is of major clinical importance since changes in these components in the brain may indicate different pathologies, i.e., those originating from the extracellular space, such as neuroinflammation and atrophy, and those related to the vascular space, such as vasodilation, vasoconstriction and capillary density. Diffusion MRI data acquired from a healthy volunteer, using multiple b-shells, demonstrated an expected non-zero contribution from the blood fraction, and indicated that not accounting for the perfusion effect may explain the overestimation of the free water fraction evinced in previous studies. Finally, the applicability of the method was demonstrated with a dataset acquired using a clinically feasible protocol with shorter acquisition time and fewer b-shells.
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Affiliation(s)
- Anna S Rydhög
- Department of Medical Radiation Physics, Lund University, Barngatan 2B, SE-221 85 Lund, Sweden.
| | - Filip Szczepankiewicz
- Department of Medical Radiation Physics, Lund University, Barngatan 2B, SE-221 85 Lund, Sweden.
| | - Ronnie Wirestam
- Department of Medical Radiation Physics, Lund University, Barngatan 2B, SE-221 85 Lund, Sweden.
| | - André Ahlgren
- Department of Medical Radiation Physics, Lund University, Barngatan 2B, SE-221 85 Lund, Sweden.
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 1249 Boylston St, Boston, MA 02215, USA.
| | - Linda Knutsson
- Department of Medical Radiation Physics, Lund University, Barngatan 2B, SE-221 85 Lund, Sweden; The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine, 600 N. Wolf Street, Park 311, Baltimore, MD 21287, USA.
| | - Ofer Pasternak
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 1249 Boylston St, Boston, MA 02215, USA; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, 1249 Boylston St, Boston, MA 02215, USA.
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117
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Cousineau M, Jodoin PM, Garyfallidis E, Côté MA, Morency FC, Rozanski V, Grand’Maison M, Bedell BJ, Descoteaux M. A test-retest study on Parkinson's PPMI dataset yields statistically significant white matter fascicles. Neuroimage Clin 2017; 16:222-233. [PMID: 28794981 PMCID: PMC5547250 DOI: 10.1016/j.nicl.2017.07.020] [Citation(s) in RCA: 83] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2017] [Revised: 07/13/2017] [Accepted: 07/22/2017] [Indexed: 12/13/2022]
Abstract
In this work, we propose a diffusion MRI protocol for mining Parkinson's disease diffusion MRI datasets and recover robust disease-specific biomarkers. Using advanced high angular resolution diffusion imaging (HARDI) crossing fiber modeling and tractography robust to partial volume effects, we automatically dissected 50 white matter (WM) fascicles. These fascicles connect deep nuclei (thalamus, putamen, pallidum) to different cortical functional areas (associative, motor, sensorimotor, limbic), basal forebrain and substantia nigra. Then, among these 50 candidate WM fascicles, only the ones that passed a test-retest reproducibility procedure qualified for further tractometry analysis. Leveraging the unique 2-timepoints test-retest Parkinson's Progression Markers Initiative (PPMI) dataset of over 600 subjects, we found statistically significant differences in tract profiles along the subcortico-cortical pathways between Parkinson's disease patients and healthy controls. In particular, significant increases in FA, apparent fiber density, tract-density and generalized FA were detected in some locations of the nigro-subthalamo-putaminal-thalamo-cortical pathway. This connection is one of the major motor circuits balancing the coordination of motor output. Detailed and quantifiable knowledge on WM fascicles in these areas is thus essential to improve the quality and outcome of Deep Brain Stimulation, and to target new WM locations for investigation.
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Affiliation(s)
- Martin Cousineau
- Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Pierre-Marc Jodoin
- Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
- Imeka Solutions Inc., Sherbrooke, QC, Canada
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, School of Informatics and Computing, Indiana University, Bloomington, USA
| | - Marc-Alexandre Côté
- Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | | | - Verena Rozanski
- Department of Neurology, Klinikum Grosshadern, University of Munich, Germany
| | | | - Barry J. Bedell
- Biospective Inc., Montréal, QC, Canada
- McGill University, Montréal, QC, Canada
| | - Maxime Descoteaux
- Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
- Imeka Solutions Inc., Sherbrooke, QC, Canada
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118
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Shaw CB, Hui ES, Helpern JA, Jensen JH. Tensor estimation for double-pulsed diffusional kurtosis imaging. NMR IN BIOMEDICINE 2017; 30:e3722. [PMID: 28328072 DOI: 10.1002/nbm.3722] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2016] [Revised: 02/08/2017] [Accepted: 02/09/2017] [Indexed: 06/06/2023]
Abstract
Double-pulsed diffusional kurtosis imaging (DP-DKI) represents the double diffusion encoding (DDE) MRI signal in terms of six-dimensional (6D) diffusion and kurtosis tensors. Here a method for estimating these tensors from experimental data is described. A standard numerical algorithm for tensor estimation from conventional (i.e. single diffusion encoding) diffusional kurtosis imaging (DKI) data is generalized to DP-DKI. This algorithm is based on a weighted least squares (WLS) fit of the signal model to the data combined with constraints designed to minimize unphysical parameter estimates. The numerical algorithm then takes the form of a quadratic programming problem. The principal change required to adapt the conventional DKI fitting algorithm to DP-DKI is replacing the three-dimensional diffusion and kurtosis tensors with the 6D tensors needed for DP-DKI. In this way, the 6D diffusion and kurtosis tensors for DP-DKI can be conveniently estimated from DDE data by using constrained WLS, providing a practical means for condensing DDE measurements into well-defined mathematical constructs that may be useful for interpreting and applying DDE MRI. Data from healthy volunteers for brain are used to demonstrate the DP-DKI tensor estimation algorithm. In particular, representative parametric maps of selected tensor-derived rotational invariants are presented.
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Affiliation(s)
- Calvin B Shaw
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Edward S Hui
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, SAR, China
| | - Joseph A Helpern
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
- Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, USA
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Jens H Jensen
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
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119
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Nilsson M, Lasič S, Drobnjak I, Topgaard D, Westin C. Resolution limit of cylinder diameter estimation by diffusion MRI: The impact of gradient waveform and orientation dispersion. NMR IN BIOMEDICINE 2017; 30:e3711. [PMID: 28318071 PMCID: PMC5485041 DOI: 10.1002/nbm.3711] [Citation(s) in RCA: 97] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Revised: 01/16/2017] [Accepted: 01/20/2017] [Indexed: 05/20/2023]
Abstract
Diffusion MRI has been proposed as a non-invasive technique for axonal diameter mapping. However, accurate estimation of small diameters requires strong gradients, which is a challenge for the transition of the technique from preclinical to clinical MRI scanners, since these have weaker gradients. In this work, we develop a framework to estimate the lower bound for accurate diameter estimation, which we refer to as the resolution limit. We analyse only the contribution from the intra-axonal space and assume that axons can be represented by impermeable cylinders. To address the growing interest in using techniques for diffusion encoding that go beyond the conventional single diffusion encoding (SDE) sequence, we present a generalised analysis capable of predicting the resolution limit regardless of the gradient waveform. Using this framework, waveforms were optimised to minimise the resolution limit. The results show that, for parallel cylinders, the SDE experiment is optimal in terms of yielding the lowest possible resolution limit. In the presence of orientation dispersion, diffusion encoding sequences with square-wave oscillating gradients were optimal. The resolution limit for standard clinical MRI scanners (maximum gradient strength 60-80 mT/m) was found to be between 4 and 8 μm, depending on the noise levels and the level of orientation dispersion. For scanners with a maximum gradient strength of 300 mT/m, the limit was reduced to between 2 and 5 μm.
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Affiliation(s)
- Markus Nilsson
- Clinical Sciences Lund, Department of RadiologyLund UniversityLundSweden
| | | | | | - Daniel Topgaard
- Division of Physical Chemistry, Department of ChemistryLund UniversityLundSweden
| | - Carl‐Fredrik Westin
- Department of Biomedical EngineeringLinköping UniversityLinköpingSweden
- Brigham and Women's HospitalHarvard Medical SchoolBostonMAUSA
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120
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Delgado AF, Fahlström M, Nilsson M, Berntsson SG, Zetterling M, Libard S, Alafuzoff I, van Westen D, Lätt J, Smits A, Larsson EM. Diffusion Kurtosis Imaging of Gliomas Grades II and III - A Study of Perilesional Tumor Infiltration, Tumor Grades and Subtypes at Clinical Presentation. Radiol Oncol 2017; 51:121-129. [PMID: 28740446 PMCID: PMC5514651 DOI: 10.1515/raon-2017-0010] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Accepted: 01/08/2017] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Diffusion kurtosis imaging (DKI) allows for assessment of diffusion influenced by microcellular structures. We analyzed DKI in suspected low-grade gliomas prior to histopathological diagnosis. The aim was to investigate if diffusion parameters in the perilesional normal-appearing white matter (NAWM) differed from contralesional white matter, and to investigate differences between glioma malignancy grades II and III and glioma subtypes (astrocytomas and oligodendrogliomas). PATIENTS AND METHODS Forty-eight patients with suspected low-grade glioma were prospectively recruited to this institutional review board-approved study and investigated with preoperative DKI at 3T after written informed consent. Patients with histologically proven glioma grades II or III were further analyzed (n=35). Regions of interest (ROIs) were delineated on T2FLAIR images and co-registered to diffusion MRI parameter maps. Mean DKI data were compared between perilesional and contralesional NAWM (student's t-test for dependent samples, Wilcoxon matched pairs test). Histogram DKI data were compared between glioma types and glioma grades (multiple comparisons of mean ranks for all groups). The discriminating potential for DKI in assessing glioma type and grade was assessed with receiver operating characteristics (ROC) curves. RESULTS There were significant differences in all mean DKI variables between perilesional and contralesional NAWM (p=<0.000), except for axial kurtosis (p=0.099). Forty-four histogram variables differed significantly between glioma grades II (n=23) and III (n=12) (p=0.003-0.048) and 10 variables differed significantly between ACs (n=18) and ODs (n=17) (p=0.011-0.050). ROC curves of the best discriminating variables had an area under the curve (AUC) of 0.657-0.815. CONCLUSIONS Mean DKI variables in perilesional NAWM differ significantly from contralesional NAWM, suggesting altered microstructure by tumor infiltration not depicted on morphological MRI. Histogram analysis of DKI data identifies differences between glioma grades and subtypes.
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Affiliation(s)
- Anna F Delgado
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden.,Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Markus Fahlström
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
| | | | - Shala G Berntsson
- Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden
| | - Maria Zetterling
- Department of Neuroscience, Neurosurgery, Uppsala University, Uppsala, Sweden
| | - Sylwia Libard
- Department of Immunology, Genetics and Pathology, Section of pathology, Uppsala University Hospital and Uppsala University, Uppsala, Sweden
| | - Irina Alafuzoff
- Department of Immunology, Genetics and Pathology, Section of pathology, Uppsala University Hospital and Uppsala University, Uppsala, Sweden
| | | | - Jimmy Lätt
- Department of Imaging and Function, Skåne University Healthcare, Lund, Sweden
| | - Anja Smits
- Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden
| | - Elna-Marie Larsson
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
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121
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Lampinen B, Szczepankiewicz F, Mårtensson J, van Westen D, Sundgren PC, Nilsson M. Neurite density imaging versus imaging of microscopic anisotropy in diffusion MRI: A model comparison using spherical tensor encoding. Neuroimage 2017; 147:517-531. [DOI: 10.1016/j.neuroimage.2016.11.053] [Citation(s) in RCA: 155] [Impact Index Per Article: 22.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2016] [Revised: 11/01/2016] [Accepted: 11/21/2016] [Indexed: 11/30/2022] Open
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122
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Topgaard D. Multidimensional diffusion MRI. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2017; 275:98-113. [PMID: 28040623 DOI: 10.1016/j.jmr.2016.12.007] [Citation(s) in RCA: 129] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2016] [Revised: 12/13/2016] [Accepted: 12/15/2016] [Indexed: 05/12/2023]
Abstract
Principles from multidimensional NMR spectroscopy, and in particular solid-state NMR, have recently been transferred to the field of diffusion MRI, offering non-invasive characterization of heterogeneous anisotropic materials, such as the human brain, at an unprecedented level of detail. Here we revisit the basic physics of solid-state NMR and diffusion MRI to pinpoint the origin of the somewhat unexpected analogy between the two fields, and provide an overview of current diffusion MRI acquisition protocols and data analysis methods to quantify the composition of heterogeneous materials in terms of diffusion tensor distributions with size, shape, and orientation dimensions. While the most advanced methods allow estimation of the complete multidimensional distributions, simpler methods focus on various projections onto lower-dimensional spaces as well as determination of means and variances rather than actual distributions. Even the less advanced methods provide simple and intuitive scalar parameters that are directly related to microstructural features that can be observed in optical microscopy images, e.g. average cell eccentricity, variance of cell density, and orientational order - properties that are inextricably entangled in conventional diffusion MRI. Key to disentangling all these microstructural features is MRI signal acquisition combining isotropic and directional dimensions, just as in the field of multidimensional solid-state NMR from which most of the ideas for the new methods are derived.
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Affiliation(s)
- Daniel Topgaard
- Physical Chemistry, Lund University, P.O.B. 124, SE-22100 Lund, Sweden.
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