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Event Abstract Back to Event Fast kurtosis imaging and its application to assess time dependence of diffusion and kurtosis parameters in the rat spinal cord Sune Nørhøj Jespersen1, Brian Hansen1, Ebbe Stubbe2, Daniel Nunes3 and Noam Shemesh3* 1 University of Aarhus, CFIN/MINDLab, Denmark 2 University of Aarhus, Department of Physics and Astronomy, Denmark 3 Champalimaud Centre for the Unknown, Champalimaud Neuroscience Programme, Portugal Introduction: Non-vanishing diffusion kurtosis and time-dependent diffusion are both hallmarks of nongaussian diffusion in biological tissues – but studies investigating time dependence of kurtosis parameters[1] in neural tissue are lacking, presumably due to time consuming acquisitions. Here we describe a recently proposed fast kurtosis imaging protocol[2,3] requiring only 13 or 19 diffusion weighted images, and characterize its experimental performance compared to conventional DKI. We also describe a number of postprocessing steps for improving robustness. We then apply it to obtain combined measurements of time-dependent DTI and DKI parameters in rat spinal cord. Methods: For investigation of 1-9-9 experimental performance, three subjects were scanned using a DTI sequence with CSF suppression on a Siemens Trio 3T, using the 1-9-9 protocol as well as 24 directions on 14 shells from 0.2-3 ms/μm2. DKI “ground truth parameters” were obtained from a full nonlinear fit to the full data and compared to 1-9-9 estimates using simulations. For DTI/DKI time dependence, 3 fixated rat spinal cords, were scanned with a Stimulated-Echo EPI DTI sequence on a 16.4T Bruker Aeon Ascend magnet; 15 directions, b=1.2 ms/μm2 and diffusion times (Δ) 7 -150 ms. The 1-9-9 fast kurtosis scheme[3] was executed using two b-value shells b=1.0 and 3.2 ms/μm2, with Δ between 4 and 130 ms. White matter ROIs were selected in order of increasing axonal diameters[4,5] and 6 gray matter ROIs were also analysed. Results: Figure 1 illustrates the precision and accuracy of the fast kurtosis method as a function of SNR compared to nonlinear least squares. The time dependence of the parallel diffusivity D∥ and perpendicular diffusivity D⊥ are shown in Fig. 2. Although there is a tendency for areas with larger axons to have higher D⊥ , the order of the curves reveals no clear-cut relation to the axonal radii. Tortuosities can be estimated roughly by D⊥(t=0)/D⊥(t→∞), to yield 1.83, 1.77, 2.50, 1.98, 1.80, 1.67, and 1.92, for regions (A-G). Fractional anisotropy is generally lower in the ROIs with larger diameters, and increases strongly with time initially leveling out somewhat above ~70 ms. In Fig. 3, the output from the 1-9-9 protocol is shown. The behavior of mean diffusivity in white matter is consistent with Fig. 2. In gray matter, `W decreases smoothly with time, whereas in contrast,`W increases initially in white matter (robust across animals), perhaps indicating that water has not fully sampled the intra-axonal space in the transverse direction below 10 ms. Conclusions: We showed that mean kurtosis estimation with the 1-9-9 protocol is a robust method. We demonstrated time dependence of parallel and perpendicular diffusivity in white and gray matter of rat spinal cord and presented the first demonstrations of time dependent kurtosis in neural tissue using the 1-9-9 fast kurtosis protocol, and mean kurtosis was found to depend on time in both white and gray spinal cord matter, with a nontrivial time dependence in white matter. These may have significant implications both for analyzing and planning kurtosis experiments as well as for probing the neural tissues using DKI. Acknowledgement: Lundbeck Foundation grant R83–A7548, Simon Fougner Hartmanns Familiefond, and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 657366. References 1. Jensen, J.H., et al., Magnetic Resonance in Medicine, 2005. 53(6). 2. Hansen, B., et al., Magn Reson Med, 2013. 69(6). 3. Hansen, B., et al., Magn. Reson. Med. (in press), 2015. 4. Ong, H.H., et al., Neuroimage, 2010. 51(4). 5. Xu, J., et al., NeuroImage, 2014. 103. Fig. 1 Precision and bias for the fast estimates of 1-3-9 (red) and 1-9-9 (green) estimates of`D (A,C) and`W (B,D) quantified by standard deviation (filled area) and mean values (markers) over 500 noise realizations. For comparison, nonlinear least squares (blue) are shown also. The top row (A, B) corresponds to a WM voxel with ground truth values`D=0.74 μm2/ms,`W=1.12 FA=0.72, and in lower row (C, D) to a GM voxel with ground truth values`D=0.86 μm2/ms,`W=0.5 FA=0.04. Fig. 2 Time dependence of parallel diffusivity (left), transverse diffusivity (middle), and FA (right) in the 7 white matter ROIs. Fig. 3 Mean diffusion and kurtosis time dependence from 1-9-9 protocol. The left column shows behavior in the gray matter ROIs and the right column corresponds to white matter ROIs. The upper row shows mean diffusivity and the lower row mean kurtosis. Figure 1 Figure 2 Figure 3 Keywords: Diffusion, MRI, dMRI, multidimensional, diffusion encoding Conference: New dimensions in diffusion encoding, Fjälkinge, Sweden, 11 Jan - 14 Jan, 2016. Presentation Type: Oral presentation Topic: New Dimensions in Diffusion Encoding Citation: Nørhøj Jespersen S, Hansen B, Stubbe E, Nunes D and Shemesh N (2016). Fast kurtosis imaging and its application to assess time dependence of diffusion and kurtosis parameters in the rat spinal cord. Front. Phys. Conference Abstract: New dimensions in diffusion encoding. doi: 10.3389/conf.FPHY.2016.01.00017 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 07 Jul 2016; Published Online: 07 Jul 2016. * Correspondence: PhD. Noam Shemesh, Champalimaud Centre for the Unknown, Champalimaud Neuroscience Programme, Lisbon, 1400-038, Portugal, noam.shemesh@neuro.fchampalimaud.org Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Sune Nørhøj Jespersen Brian Hansen Ebbe Stubbe Daniel Nunes Noam Shemesh Google Sune Nørhøj Jespersen Brian Hansen Ebbe Stubbe Daniel Nunes Noam Shemesh Google Scholar Sune Nørhøj Jespersen Brian Hansen Ebbe Stubbe Daniel Nunes Noam Shemesh PubMed Sune Nørhøj Jespersen Brian Hansen Ebbe Stubbe Daniel Nunes Noam Shemesh Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.
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Diffusion imaging is an MRI modality that measures the microscopic molecular motion of water in order to investigate white matter microstructure. The modality has been used extensively in recent years to investigate the neuroanatomical basis of congenital brain malformations. We review the basic principles of diffusion imaging and of specific techniques, including diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI). We show how DTI and HARDI, and their application to fiber tractography, has elucidated the aberrant connectivity underlying a number of congenital brain malformations. Finally, we discuss potential uses for diffusion imaging of developmental disorders in the clinical and research realms.
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Diffusion imaging is a quantitative, MR-based technique potentially useful for the study of multiple sclerosis (MS), due to its increased pathologic specificity over conventional MRI and its ability to assess in vivo the presence of tissue damage occurring outside T2-visible lesions, i.e., in the so-called normal-appearing white and gray matter. The present review aims at critically summarizing the state-of-the-art and providing a background for the planning of future diffusion studies of MS. Several pieces of evidence suggest that diffusion-weighted and diffusion tensor MRI are sensitive to MS damage and able to detect its evolution over relatively short periods of time. Although a significant relationship between diffusion-weighted MRI findings and MS clinical disability was not found in the earliest studies, with improved diffusion imaging technology correlations between diffusion abnormalities and MS clinical aspects are now emerging. However, the best acquisition and postprocessing strategies for MS studies remain a matter of debate and the contribution of newer and more sophisticated techniques to diffusion tensor MRI investigations in MS needs to be further evaluated. Although changes in diffusion MRI indices reflect a net loss of structural organization, at present we can only speculate on their possible pathologic substrates in the MS brain. Postmortem studies correlating diffusion findings with histopathology of patients with MS are, therefore, also warranted.
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This paper reviews the use of magnetic resonance diffusion imaging in studies of multiple sclerosis. Firstly, the principles of diffusion imaging are explained together with a discussion of the hardware and techniques required. The concept of diffusion tensor imaging is introduced and images obtained using this method are presented. Studies that have used diffusion imaging in patients with multiple sclerosis and the implications of the results are discussed. There is an increase in the diffusion coefficient of water molecules in the plaques of patients with multiple sclerosis, compared with healthy brain. Some workers also report increased diffusion in the normal appearing white matter of some patients with multiple sclerosis. Possible mechanisms are given for these findings, together with the experimental evidence to support them.
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In conventional diffusion tensor imaging (DTI), water diffusion distribution is described as a 2nd-order three-dimensional (3D) diffusivity tensor. It assumes that diffusion occurs in a free and unrestricted environment with a Gaussian distribution of diffusion displacement, and consequently that diffusion weighted (DW) signal decays with diffusion factor (b-value) monoexponentially. In biological tissue, complex cellular microstructures make water diffusion a highly hindered or restricted process. Non-monoexponential decays are experimentally observed in both white matter and gray matter. As a result, DTI quantitation is b-value dependent and DTI fails to fully utilize the diffusion measurements that are inherent to tissue microstructure. Diffusion kurtosis imaging (DKI) characterizes restricted diffusion and can be readily implemented on most clinical scanners. It provides a higher-order description of water diffusion process by a 2nd-order 3D diffusivity tensor as in conventional DTI together with a 4th-order 3D kurtosis tensor. Because kurtosis is a measure of the deviation of the diffusion displacement profile from a Gaussian distribution, DKI analyses quantify the degree of diffusion restriction or tissue complexity without any biophysical assumption. In this work, the theory of diffusion kurtosis and DKI including the directional kurtosis analysis is revisited. Several recent rodent DKI studies from our group are summarized, and DKI and DTI compared for their efficacy in detecting neural tissue alterations. They demonstrate that DKI offers a more comprehensive approach than DTI in describing the complex water diffusion process in vivo. By estimating both diffusivity and kurtosis, it may provide improved sensitivity and specificity in MR diffusion characterization of neural tissues.
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Although some diffusion-weighted imaging (DWI) techniques have entered the stage of clinical routine application, particularly in the detection of cerebral infarction, obtaining and interpreting diffusion imaging results is not always straightforward. This reflects both the numerous technical difficulties and also the sensitivity of diffusion imaging experiments to phenomena other than diffusion.[1,2] Further complications arise from the sheer number of diffusion parameters that can be derived from the measurement in biological tissue, such as eigenvectors, eigenvalues, anisotropy, and trace of the diffusion tensor, diffusion coefficients for a given direction, and so on. This chapter aims at providing an overview of the most important difficulties encountered in MR diffusion imaging.
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