scholarly journals Integrating Material Properties from Magnetic Resonance Elastography into Subject-Specific Computational Models for the Human Brain

2021 ◽  
pp. 100038
Author(s):  
Ahmed Alshareef ◽  
Andrew K. Knutsen ◽  
Curtis L. Johnson ◽  
Aaron Carass ◽  
Kshitiz Upadhyay ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Material Properties ◽  
Computational Models ◽  
Magnetic Resonance Elastography ◽  
Subject Specific

Simulation of harmonic shear waves in the human brain and comparison with measurements from magnetic resonance elastography

2021 ◽  
Vol 118 ◽  
pp. 104449
Author(s):  
Yang Li ◽  
Ruth Okamoto ◽  
Andrew Badachhape ◽  
Chengwei Wu ◽  
Philip Bayly ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Shear Waves ◽  
Magnetic Resonance Elastography ◽  
Harmonic Shear

scholarly journals Magnetic resonance elastography of the human brain using a multiphase DENSE acquisition

2019 ◽  
Vol 81 (6) ◽  
pp. 3578-3587 ◽  
Author(s):  
Johannes Strasser ◽  
Michaela Tanja Haindl ◽  
Rudolf Stollberger ◽  
Franz Fazekas ◽  
Stefan Ropele
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Magnetic Resonance Elastography

High-resolution mechanical imaging of the human brain by three-dimensional multifrequency magnetic resonance elastography at 7T

NeuroImage ◽  
2014 ◽  
Vol 90 ◽  
pp. 308-314 ◽  
Author(s):  
Jürgen Braun ◽  
Jing Guo ◽  
Ralf Lützkendorf ◽  
Jörg Stadler ◽  
Sebastian Papazoglou ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
High Resolution ◽  
Human Brain ◽  
Three Dimensional ◽  
Magnetic Resonance Elastography ◽  
Mechanical Imaging

scholarly journals Calibration of a Heterogeneous Brain Model Using a Subject-Specific Inverse Finite Element Approach

2021 ◽  
Vol 9 ◽  
Author(s):  
J. Sebastian Giudice ◽  
Ahmed Alshareef ◽  
Taotao Wu ◽  
Andrew K. Knutsen ◽  
Lucy V. Hiscox ◽  
...  
Keyword(s):  
Experimental Data ◽  
Finite Element ◽  
Magnetic Resonance ◽  
Human Brain ◽  
Template Model ◽  
Material Parameters ◽  
Material Heterogeneity ◽  
Subject Specific ◽  
Brain Models

Central to the investigation of the biomechanics of traumatic brain injury (TBI) and the assessment of injury risk from head impact are finite element (FE) models of the human brain. However, many existing FE human brain models have been developed with simplified representations of the parenchyma, which may limit their applicability as an injury prediction tool. Recent advances in neuroimaging techniques and brain biomechanics provide new and necessary experimental data that can improve the biofidelity of FE brain models. In this study, the CAB-20MSym template model was developed, calibrated, and extensively verified. To implement material heterogeneity, a magnetic resonance elastography (MRE) template image was leveraged to define the relative stiffness gradient of the brain model. A multi-stage inverse FE (iFE) approach was used to calibrate the material parameters that defined the underlying non-linear deviatoric response by minimizing the error between model-predicted brain displacements and experimental displacement data. This process involved calibrating the infinitesimal shear modulus of the material using low-severity, low-deformation impact cases and the material non-linearity using high-severity, high-deformation cases from a dataset of in situ brain displacements obtained from cadaveric specimens. To minimize the geometric discrepancy between the FE models used in the iFE calibration and the cadaveric specimens from which the experimental data were obtained, subject-specific models of these cadaveric brain specimens were developed and used in the calibration process. Finally, the calibrated material parameters were extensively verified using independent brain displacement data from 33 rotational head impacts, spanning multiple loading directions (sagittal, coronal, axial), magnitudes (20–40 rad/s), durations (30–60 ms), and severity. Overall, the heterogeneous CAB-20MSym template model demonstrated good biofidelity with a mean overall CORA score of 0.63 ± 0.06 when compared to in situ brain displacement data. Strains predicted by the calibrated model under non-injurious rotational impacts in human volunteers (N = 6) also demonstrated similar biofidelity compared to in vivo measurements obtained from tagged magnetic resonance imaging studies. In addition to serving as an anatomically accurate model for further investigations of TBI biomechanics, the MRE-based framework for implementing material heterogeneity could serve as a foundation for incorporating subject-specific material properties in future models.

Initial In-Vivo Results Considering Rayleigh Damping in Magnetic Resonance Elastography

2009 ◽  
Author(s):  
D. Viviers ◽  
E. E. W. Van Houten ◽  
M. D. J. McGarry ◽  
J. B. Weaver ◽  
K. D. Paulsen
Keyword(s):  
Magnetic Resonance ◽  
Soft Tissue ◽  
Shear Modulus ◽  
Material Properties ◽  
Magnetic Resonance Elastography ◽  
Imaginary Component ◽  
Non Invasive ◽  
Rayleigh Damping ◽  
Time Harmonic ◽  
Complex Valued

Dispersive material properties provide valuable metrics for characterizing the nature of soft tissue lesions. Magnetic Resonance Elastography (MRE) targets non-invasive breast cancer diagnosis and is capable of imaging the damping properties of soft tissue. 3D time-harmonic displacement data obtained via MRI is used to drive a reconstruction algorithm capable of deducing the distribution of mechanical properties in the tissue. To make the most of this diagnostic capability, characterization of the damping behavior of tissue is made more sophisticated by the use of a Rayleigh damping model. To date, time-harmonic motion attenuation in tissue as found in dynamic MRE has been characterized by a single parameter model that takes the form of an imaginary component of a complex valued shear modulus. A more generalized damping formulation for the time-harmonic case, known commonly as Rayleigh or proportional damping, includes an additional parameter that takes the form of an imaginary component of a complex valued density. The effects of these two different damping mechanisms can be shown to be independent across homogeneous distributions and mischaracterization of the damping structure can be shown to lead to artifacts in the reconstructed attenuation profile. We have implemented a Rayleigh damping reconstruction method for MRE and measured the dispersive properties of actual patient data sets with impressive results. Reconstructions show a close match with varying tissue structure. The reconstructed values for real shear modulus and overall damping levels are in reasonable agreement with values established in the literature or measured by mechanical testing, and in the case of malignant lesions, show good correspondence with contrast enhanced MRI. There is significant medical potential for an algorithm that can accurately reconstruct soft tissue material properties through non invasive MRI scans. Imaging methods that help identify invasive regions through reconstruction of dispersive soft tissue properties could be applied to pathologies in the brain, lung, liver and kidney as well.

scholarly journals Systematic Review of Computational Models for Human Brain Parcellation

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Patrick Owate
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Computational Models ◽  
Parietal Lobe ◽  
Occipital Lobe ◽  
Appearance Energy ◽  
Magnetic Resonance Images ◽  
Tissue Segmentation ◽  
Age Related ◽  
Ageing Brain

Introduction: The human brain consists of four main lobar sections: Frontal lobe, Parietal lobe, Temporal lobe and Occipital lobe. Most of the existing models used for the parcellation of brain into these lobes have limited accuracy when applied to ageing brain. Aim: To systematically review the existing models of parcellating brain Magnetic Resonance Images, their strengths and weaknesses, and the possibility of using them for ageing brain. Materials and Methods: PubMed was searched combining search terms for Parcellation, Brain and Magnetic Resonance Imaging (MRI). Articles were considered if they met the following criteria: Parcellation method was indicated, imaging technique was MRI, high resolution anatomical T1-Weighted was used, lobar regions were parcellated, number of lobar regions was indicated. Results: The search resulted into 569 articles. 174 articles (7 from the list of references) were potentially relevant and their abstracts were read. Out of these, 108 were not relevant because they either focused on animal studies, sub-cortical segmentation or tissue segmentation. The full papers of the remaining 66 were reviewed. 39 articles met the inclusion criteria. Various parcellation models were reviewed and summarized into six groups: supervised learning, unsupervised learning, region growing, shape and appearance, energy-based and atlas-based models. Conclusion: All the existing models identified were developed for parcellation of young adult brains and none of them used age-related information. Atlas-based model was found to perform the best among all the models. Future work should consider extending atlas-based model by including ageing information which could make them perform well on ageing brain.

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