Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (3/3 displayed)

  • 2023QCT-based computational bone strength assessment updated with MRI-derived ‘hidden’ microporosity2citations
  • 2022Nonlinear micro finite element models based on digital volume correlation measurements predict early microdamage in newly formed bone16citations
  • 2021Heat impact during laser ablation extraction of mineralised tissue micropillars9citations

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Chart of shared publication
Taylor, Sarah E.
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Wolfram, Uwe
3 / 24 shared
Peña Fernández, Marta
2 / 9 shared
Kershaw, Lucy E.
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Daniel, Carola R.
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Cillán-García, Eugenio
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Black, Cameron
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Tozzi, Gianluca
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Sasso, Sebastian J.
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Groetsch, Alexander
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Shephard, Jonathan D.
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2023
2022
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Co-Authors (by relevance)

  • Taylor, Sarah E.
  • Wolfram, Uwe
  • Peña Fernández, Marta
  • Kershaw, Lucy E.
  • Daniel, Carola R.
  • Cillán-García, Eugenio
  • Black, Cameron
  • Tozzi, Gianluca
  • Sasso, Sebastian J.
  • Kanczler, Janos
  • Groetsch, Alexander
  • Shephard, Jonathan D.
OrganizationsLocationPeople

article

QCT-based computational bone strength assessment updated with MRI-derived ‘hidden’ microporosity

  • Taylor, Sarah E.
  • Wolfram, Uwe
  • Peña Fernández, Marta
  • Kershaw, Lucy E.
  • Daniel, Carola R.
  • Mcphee, Samuel
  • Cillán-García, Eugenio
Abstract

Microdamage accumulated through sustained periods of cyclic loading or single overloading events contributes to bone fragility through a reduction in stiffness and strength. Monitoring microdamage in vivo remains unattainable by clinical imaging modalities. As such, there are no established computational methods for clinical fracture risk assessment that account for microdamage that exists in vivo at any specific timepoint. We propose a method that combines multiple clinical imaging modalities to identify an indicative surrogate, which we term ‘hidden porosity,’ that incorporates pre-existing bone microdamage in vivo. To do so, we use the third metacarpal bone of the equine athlete as an exemplary model for fatigue induced microdamage, which coalesces in the subchondral bone. N = 10 metacarpals were scanned by clinical quantitative computed tomography (QCT) and magnetic resonance imaging (MRI). We used a patch-based similarity method to quantify the signal intensity of a fluid sensitive MRI sequence in bone regions where microdamage coalesces. The method generated MRI-derived pseudoCT images which were then used to determine a pre-existing damage (Dpex) variable to quantify the proposed surrogate and which we incorporate into a nonlinear constitutive model for bone tissue. The minimum, median, and maximum detected <i>D</i><sup>pex</sup> of 0.059, 0.209, and 0.353 reduced material stiffness by 5.9%, 20.9%, and 35.3% as well as yield stress by 5.9%, 20.3%, and 35.3%. Limb-specific voxel-based finite element meshes were equipped with the updated material model. Lateral and medial condyles of each metacarpal were loaded to simulate physiological joint loading during gallop. The degree of detected <i>D</i><sup>pex</sup> correlated with a relative reduction in both condylar stiffness (p = 0.001, R<sup>2</sup> &gt; 0.74) and strength (p &lt; 0.001, R<sup>2</sup> &gt; 0.80). Our results illustrate the complementary value of looking beyond clinical CT, which neglects the inclusion of microdamage due to partial volume effects. As we use clinically available imaging techniques, our results may aid research beyond the equine model on fracture risk assessment in human diseases such as osteoarthritis, bone cancer, or osteoporosis.

Topics
  • inclusion
  • tomography
  • strength
  • fatigue
  • porosity