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The IVIM signal in the healthy cerebral gray matter: a play of spherical and non-spherical components


Finkenstaedt, Tim; Klarhoefer, Markus; Eberhardt, Christian; Becker, Anton S; Andreisek, Gustav; Boss, Andreas; Rossi, Cristina (2017). The IVIM signal in the healthy cerebral gray matter: a play of spherical and non-spherical components. NeuroImage, 152:340-347.

Abstract

The intra-voxel incoherent motion (IVIM) model assumes that blood flowing in isotropically distributed capillary segments induces a phase dispersion of the MR signal, which increases the signal attenuation in diffusion-weighted images. However, in most tissue types the capillary network has an anisotropic micro-architecture. In this study, we investigated the possibility to indirectly infer the anisotropy of the capillary network in the healthy cerebral gray matter by evaluating the dependence of the IVIM signal from the direction of the diffusion-encoding. Perfusion-related indices and self-diffusion were modelled as symmetric rank 2 tensors. The geometry of the tensors was quantified pixel-wise by decomposing the tensor in sphere-like, plane-like, and line-like components. Additionally, trace and fractional anisotropy of the tensors were computed. While the self-diffusion tensor is dominated by a spherical geometry with a residual contribution of the non-spherical components, both, fraction of perfusion and pseudo-diffusion, present a substantial (in the order of 30%) contribution of planar and linear components to the tensor metrics. This study shows that the IVIM perfusion estimates in the cerebral gray matter present a detectable deviation from the spherical model. These non-spherical components may reflect the direction-dependent morphology of the microcirculation. Therefore, the tensor generalization of the IVIM model may provide a tool for the non-invasive monitoring of cerebral capillary micro-architecture during development, aging or in pathologies.

Abstract

The intra-voxel incoherent motion (IVIM) model assumes that blood flowing in isotropically distributed capillary segments induces a phase dispersion of the MR signal, which increases the signal attenuation in diffusion-weighted images. However, in most tissue types the capillary network has an anisotropic micro-architecture. In this study, we investigated the possibility to indirectly infer the anisotropy of the capillary network in the healthy cerebral gray matter by evaluating the dependence of the IVIM signal from the direction of the diffusion-encoding. Perfusion-related indices and self-diffusion were modelled as symmetric rank 2 tensors. The geometry of the tensors was quantified pixel-wise by decomposing the tensor in sphere-like, plane-like, and line-like components. Additionally, trace and fractional anisotropy of the tensors were computed. While the self-diffusion tensor is dominated by a spherical geometry with a residual contribution of the non-spherical components, both, fraction of perfusion and pseudo-diffusion, present a substantial (in the order of 30%) contribution of planar and linear components to the tensor metrics. This study shows that the IVIM perfusion estimates in the cerebral gray matter present a detectable deviation from the spherical model. These non-spherical components may reflect the direction-dependent morphology of the microcirculation. Therefore, the tensor generalization of the IVIM model may provide a tool for the non-invasive monitoring of cerebral capillary micro-architecture during development, aging or in pathologies.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Diagnostic and Interventional Radiology
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Life Sciences > Neurology
Life Sciences > Cognitive Neuroscience
Language:English
Date:2 March 2017
Deposited On:16 Mar 2017 10:52
Last Modified:19 May 2024 03:30
Publisher:Elsevier
ISSN:1053-8119
OA Status:Hybrid
Publisher DOI:https://doi.org/10.1016/j.neuroimage.2017.03.004
PubMed ID:28263927
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)