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      Spatially selective 2D RF inner field of view (iFOV) diffusion kurtosis imaging (DKI) of the pediatric spinal cord

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          Abstract

          Magnetic resonance based diffusion imaging has been gaining more utility and clinical relevance over the past decade. Using conventional echo planar techniques, it is possible to acquire and characterize water diffusion within the central nervous system (CNS); namely in the form of Diffusion Weighted Imaging (DWI) and Diffusion Tensor Imaging (DTI). While each modality provides valuable clinical information in terms of the presence of diffusion and its directionality, both techniques are limited to assuming an ideal Gaussian distribution for water displacement with no intermolecular interactions. This assumption neglects pathological processes that are not Gaussian therefore reducing the amount of potentially clinically relevant information. Additions to the Gaussian distribution measured by the excess kurtosis, or peakedness, of the probabilistic model provide a better understanding of the underlying cellular structure. The objective of this work is to provide mathematical and experimental evidence that Diffusion Kurtosis Imaging (DKI) can offer additional information about the micromolecular environment of the pediatric spinal cord. This is accomplished by a more thorough characterization of the nature of random water displacement within the cord. A novel DKI imaging sequence based on a tilted 2D spatially selective radio frequency pulse providing reduced field of view (FOV) imaging was developed, implemented, and optimized on a 3 Tesla MRI scanner, and tested on pediatric subjects (healthy subjects: 15; patients with spinal cord injury (SCI):5). Software was developed and validated for post processing of the DKI images and estimation of the tensor parameters. The results show statistically significant differences in mean kurtosis (p < 0.01) and radial kurtosis (p < 0.01) between healthy subjects and subjects with SCI. DKI provides incremental and novel information over conventional diffusion acquisitions when coupled with higher order estimation algorithms.

          Highlights

          • Diffusion Kurtosis Imaging (DKI) was performed on pediatric subjects using a tilted 2D RF reduced field of view sequence.

          • Results show statistically significant differences in FA, MK, Krad, and Drad between healthy subjects and patients with SCI.

          • DKI provides additional structural information that when paired with DTI metrics could be used as a novel imaging biomarker.

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          Most cited references23

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          RESTORE: robust estimation of tensors by outlier rejection.

          Signal variability in diffusion weighted imaging (DWI) is influenced by both thermal noise and spatially and temporally varying artifacts such as subject motion and cardiac pulsation. In this paper, the effects of DWI artifacts on estimated tensor values, such as trace and fractional anisotropy, are analyzed using Monte Carlo simulations. A novel approach for robust diffusion tensor estimation, called RESTORE (for robust estimation of tensors by outlier rejection), is proposed. This method uses iteratively reweighted least-squares regression to identify potential outliers and subsequently exclude them. Results from both simulated and clinical diffusion data sets indicate that the RESTORE method improves tensor estimation compared to the commonly used linear and nonlinear least-squares tensor fitting methods and a recently proposed method based on the Geman-McClure M-estimator. The RESTORE method could potentially remove the need for cardiac gating in DWI acquisitions and should be applicable to other MR imaging techniques that use univariate or multivariate regression to fit MRI data to a model. Copyright 2005 Wiley-Liss, Inc.
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            Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging.

            This article presents two related advancements to the diffusional kurtosis imaging estimation framework to increase its robustness to noise, motion, and imaging artifacts. The first advancement substantially improves the estimation of diffusion and kurtosis tensors parameterizing the diffusional kurtosis imaging model. Rather than utilizing conventional unconstrained least squares methods, the tensor estimation problem is formulated as linearly constrained linear least squares, where the constraints ensure physically and/or biologically plausible tensor estimates. The exact solution to the constrained problem is found via convex quadratic programming methods or, alternatively, an approximate solution is determined through a fast heuristic algorithm. The computationally more demanding quadratic programming-based method is more flexible, allowing for an arbitrary number of diffusion weightings and different gradient sets for each diffusion weighting. The heuristic algorithm is suitable for real-time settings such as on clinical scanners, where run time is crucial. The advantage offered by the proposed constrained algorithms is demonstrated using in vivo human brain images. The proposed constrained methods allow for shorter scan times and/or higher spatial resolution for a given fidelity of the diffusional kurtosis imaging parametric maps. The second advancement increases the efficiency and accuracy of the estimation of mean and radial kurtoses by applying exact closed-form formulae. Copyright © 2010 Wiley-Liss, Inc.
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              High b-value q-space analyzed diffusion-weighted MRS and MRI in neuronal tissues - a technical review.

              This review deals with high b-value q-space diffusion-weighted MRI (DW-MRI) of neuronal tissues. It is well documented that at sufficiently high b-values (and high q-values) neuronal water signal decay in diffusion experiments is not mono-exponential. This implies the existence of more than one apparent diffusing component or evidence for restriction. The assignment of the different apparent diffusing components to real physical entities is not straightforward. However, the apparent slow diffusing component that was found to be restricted to a compartment of a few microns, if originating mainly from a specific pool and if assigned correctly, may potentially be used to obtain more specific MR images with regard to specific pathologies of the CNS. This review examines the utility of analyzing high b-value diffusion MRS and MRI data using the q-space approach introduced by Callaghan and by Cory and Garroway. This approach provides displacement probability maps that emphasize, at long diffusion times, the characteristics of the apparent slow diffusing component. Examples from excised spinal cord, where the experimental conditions for which the q-space analysis of MR diffusion data was developed can be met or approached will be presented. Then examples from human MS patients, where q-space requirement for the short gradient pulse is clearly violated, are presented. In the excised spinal cord studies, this approach was used to study spinal cord maturation and trauma, and was found to be more sensitive than other conventional methods in following spinal cord degeneration in an experimental model of vascular dementia (VaD). High b-value q-space DWI was also recently used to study healthy and MS diseased human brains. This approach was found to be very sensitive to the disease load in MS, compared with other conventional MRI methods, especially in the normal appearing white matter (NAWM) of MS brains. Finally, the potential diagnostic capacity embedded in high b-value q-space analyzed diffusion MR images is discussed. The potentials and caveats of this approach are outlined and experimental data are presented that show the effect of violating the short gradient pulse (SGP) approximation on the extracted parameters from the q-space analysis. Copyright 2002 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                12 January 2016
                2016
                12 January 2016
                : 11
                : 61-67
                Affiliations
                [a ]Electrical Engineering, Temple University, Philadelphia, PA, United States
                [b ]Radiology, Temple University, Philadelphia, PA, United States
                [c ]Bioengineering, Temple University, Philadelphia, PA, United States
                [d ]Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
                [e ]ICON Medical Imaging, Warrington, PA, United States
                [f ]Physical Therapy, Thomas Jefferson University, Philadelphia, PA, United States
                [g ]Occupational Therapy, Thomas Jefferson University, Philadelphia, PA, United States
                [h ]Radiology, Thomas Jefferson University, Philadelphia, PA, United States
                Author notes
                [* ]Corresponding author at: Thomas Jefferson University, 909 Walnut Street, Philadelphia, PA 19107, United States.Thomas Jefferson University909 Walnut StreetPhiladelphiaPA19107United States Christopher.conklin@ 123456jefferson.edu
                Article
                S2213-1582(16)30008-0
                10.1016/j.nicl.2016.01.009
                4735660
                26909329
                7512e015-11f6-40d8-bc1a-d314c4e36642
                © 2016 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 9 November 2015
                : 24 December 2015
                : 9 January 2016
                Categories
                Regular Article

                diffusion kurtosis imaging (dki),diffusion,spinal cord injury,pediatrics

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