Mechanical stiffness and anisotropy measured by MRE during brain development in the minipig

Wang, S.; Guertler, Charlotte A.; Okamoto, Ruth J.; Johnson, Curtis L.; McGarry, Matthew; Bayly, Philip V.

doi:10.1016/j.neuroimage.2023.120234

Cited by 12 publications

(11 citation statements)

References 73 publications

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“…Among various inversion methods of MRE [107][108][109] , the finite element-based, transversely-isotropic nonlinear inversion (TI-NLI) 109 algorithm has shown great promise in recovering accurate heterogenous parameter fields and repeatable property estimates for in vivo human brain 110 . Very recently, using this method, longitudinal anisotropic mechanical properties of the minipig brain were estimated from displacement fields and DTI's fiber directions 111 . The study revealed that the white matter is stiffer than gray matter in shear in any plane containing the fiber direction and in tension in any direction with a component of fiber direction.…”

Section: Discussionmentioning

confidence: 99%

“…A current challenge in predicting the anisotropic material properties of brain tissue, through the synergy of MRE and DTI, is that the used material model captures the behavior of a material with a single dominant fiber direction. This results in parameter estimates that can be inaccurate or challenging to interpret in regions with crossing fibers 111 . This so-called "crossing-fiber" problem cannot be adequately described using the diffusion tensor model 112 .…”

Section: Discussionmentioning

confidence: 99%

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A Deep Learning Framework for Predicting the Heterogeneous Stiffness Map of Brain White Matter Tissue

Chavoshnejad,

Li,

Liu

et al. 2024

Preprint

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Finding the stiffness map of biological tissues is of great importance in evaluating their healthy or pathological conditions. However, due to the heterogeneity and anisotropy of biological fibrous tissues, this task presents challenges and significant uncertainty when characterized only by single-mode loading experiments. In this study, we propose a new theoretical framework to map the stiffness landscape of fibrous tissues, specifically focusing on brain white matter tissue. Initially, a finite element model of the fibrous tissue was subjected to six loading cases, and their corresponding stress-strain curves were characterized. By employing multiobjective optimization, the material constants of an equivalent anisotropic material model were inversely extracted to best fit all six loading modes simultaneously. Subsequently, large-scale finite element simulations were conducted, incorporating various fiber volume fractions and orientations, to train a convolutional neural network capable of predicting the equivalent anisotropic material properties solely based on the fibrous architecture of any given tissue. The method was applied to local imaging data of brain white matter tissue, demonstrating its effectiveness in precisely mapping the anisotropic behavior of fibrous tissue. In the long-term, the proposed method may find applications in traumatic brain injury, brain folding studies, and neurodegenerative diseases, where accurately capturing the material behavior of the tissue is crucial for simulations and experiments.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Deep Learning Framework for Predicting the Heterogeneous Stiffness Map of Brain White Matter Tissue

Chavoshnejad,

Li,

Liu

et al. 2024

Preprint

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“…Wang et al (2023) also considered age-related effects on anisotropic MRE parameters, but instead examined minipig brains across a period of development. That study also used TI-NLI and an actuator modified from Guertler et al (2018) to achieve multiple wave fields as in human multi-excitation MRE.…”

Section: Discussionmentioning

confidence: 99%

Mechanical Properties of White Matter Tracts in Aging Assessed via Anisotropic MR Elastography

Caban-Rivera,

Williams,

McGarry

et al. 2024

Preprint

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Magnetic resonance elastography (MRE) is a promising neuroimaging technique to probe tissue microstructure, which has revealed widespread softening with loss of structural integrity in the aging brain. Traditional MRE approaches assume mechanical isotropy. However, white matter is known to be anisotropic from aligned, myelinated axonal bundles, which can lead to uncertainty in mechanical property estimates in these areas when using isotropic MRE. Recent advances in anisotropic MRE now allow for estimation of shear and tensile anisotropy, along with substrate shear modulus, in white matter tracts. The objective of this study was to investigate age-related differences in anisotropic mechanical properties in human brain white matter tracts for the first time. Anisotropic mechanical properties in all tracts were found to be significantly lower in older adults compared to young adults, with average property differences ranging between 0.028-0.107 for shear anisotropy and between 0.139-0.347 for tensile anisotropy. Stiffness perpendicular to the axonal fiber direction was also significantly lower in older age, but only in certain tracts. When compared with fractional anisotropy measures from diffusion tensor imaging, we found that anisotropic MRE measures provided additional, complementary information in describing differences between the white matter integrity of young and older populations. Anisotropic MRE provides a new tool for studying white matter structural integrity in aging and neurodegeneration.

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“…For MRE, shear waves were induced using a custom multi-directional jaw actuator with three configurations at a frequency of 100 Hz [ 32 , 48 ]. The harmonic motion was transmitted from a pneumatic driver (Resoundant ™ , Rochester, MN) by plastic tubing to the passive jaw actuator, which consists of two flexible plastic bottles.…”

Section: Methodsmentioning

confidence: 99%

“…Transversely isotropic NLI (TI-NLI) and other anisotropic MRE methods also need sufficient and varied displacement data to ensure the accurate estimation of mechanical parameters [ 47 ]. Anderson et al [ 35 ] and Wang et al [ 48 ] proposed multi-excitation MRE with more than one actuator or actuator location to provide sufficiently diverse displacement data for anisotropic inversion, and TI-NLI incorporates these multiple displacement fields to improve anisotropic property estimation. TI-NLI has been shown to recover accurate (within 1.3 % of true values) heterogenous parameter fields using synthetic data in the presence of measurement noise, and repeatable (coefficient of variation < 7.7 % over 10 scans) property estimates for in vivo human brain [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Post-mortem changes of anisotropic mechanical properties in the porcine brain assessed by MR elastography

Wang,

Eckstein,

Guertler

et al. 2024

Brain Multiphysics

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Mechanical stiffness and anisotropy measured by MRE during brain development in the minipig

Cited by 12 publications

References 73 publications

A Deep Learning Framework for Predicting the Heterogeneous Stiffness Map of Brain White Matter Tissue

A Deep Learning Framework for Predicting the Heterogeneous Stiffness Map of Brain White Matter Tissue

Mechanical Properties of White Matter Tracts in Aging Assessed via Anisotropic MR Elastography

Post-mortem changes of anisotropic mechanical properties in the porcine brain assessed by MR elastography

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