Materials Map

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

  • About
  • Privacy Policy
  • Legal Notice
  • Contact

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.

×

Materials Map under construction

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.

To Graph

1.080 Topics available

To Map

977 Locations available

693.932 PEOPLE
693.932 People People

693.932 People

Show results for 693.932 people that are selected by your search filters.

←

Page 1 of 27758

→
←

Page 1 of 0

→
PeopleLocationsStatistics
Naji, M.
  • 2
  • 13
  • 3
  • 2025
Motta, Antonella
  • 8
  • 52
  • 159
  • 2025
Aletan, Dirar
  • 1
  • 1
  • 0
  • 2025
Mohamed, Tarek
  • 1
  • 7
  • 2
  • 2025
Ertürk, Emre
  • 2
  • 3
  • 0
  • 2025
Taccardi, Nicola
  • 9
  • 81
  • 75
  • 2025
Kononenko, Denys
  • 1
  • 8
  • 2
  • 2025
Petrov, R. H.Madrid
  • 46
  • 125
  • 1k
  • 2025
Alshaaer, MazenBrussels
  • 17
  • 31
  • 172
  • 2025
Bih, L.
  • 15
  • 44
  • 145
  • 2025
Casati, R.
  • 31
  • 86
  • 661
  • 2025
Muller, Hermance
  • 1
  • 11
  • 0
  • 2025
Kočí, JanPrague
  • 28
  • 34
  • 209
  • 2025
Šuljagić, Marija
  • 10
  • 33
  • 43
  • 2025
Kalteremidou, Kalliopi-ArtemiBrussels
  • 14
  • 22
  • 158
  • 2025
Azam, Siraj
  • 1
  • 3
  • 2
  • 2025
Ospanova, Alyiya
  • 1
  • 6
  • 0
  • 2025
Blanpain, Bart
  • 568
  • 653
  • 13k
  • 2025
Ali, M. A.
  • 7
  • 75
  • 187
  • 2025
Popa, V.
  • 5
  • 12
  • 45
  • 2025
Rančić, M.
  • 2
  • 13
  • 0
  • 2025
Ollier, Nadège
  • 28
  • 75
  • 239
  • 2025
Azevedo, Nuno Monteiro
  • 4
  • 8
  • 25
  • 2025
Landes, Michael
  • 1
  • 9
  • 2
  • 2025
Rignanese, Gian-Marco
  • 15
  • 98
  • 805
  • 2025

Dantuono, J. T.

  • Google
  • 1
  • 5
  • 44

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2016Quantitative Compression Optical Coherence Elastography as an Inverse Elasticity Problem44citations

Places of action

Chart of shared publication
Wijesinghe, Philip
1 / 4 shared
Munro, Peter
1 / 3 shared
Oberai, A. A.
1 / 1 shared
Sampson, David
1 / 4 shared
Dong, L.
1 / 4 shared
Chart of publication period
2016

Co-Authors (by relevance)

  • Wijesinghe, Philip
  • Munro, Peter
  • Oberai, A. A.
  • Sampson, David
  • Dong, L.
OrganizationsLocationPeople

article

Quantitative Compression Optical Coherence Elastography as an Inverse Elasticity Problem

  • Wijesinghe, Philip
  • Munro, Peter
  • Oberai, A. A.
  • Dantuono, J. T.
  • Sampson, David
  • Dong, L.
Abstract

© 2015 IEEE. Quantitative elasticity imaging seeks to retrieve spatial maps of elastic moduli of tissue. Unlike strain, which is commonly imaged in compression elastography, elastic moduli are intrinsic properties of tissue, and therefore, this approach reconstructs images that are largely operator and system independent, enabling objective, longitudinal, and multisite diagnoses. Recently, novel quantitative elasticity imaging approaches to compression elastography have been developed. These methods use a calibration layer with known mechanical properties to sense the stress at the tissue surface, which combined with strain, is used to estimate the tissue's elastic moduli by assuming homogeneity in the stress field. However, this assumption is violated in mechanically heterogeneous samples. We present a more general approach to quantitative elasticity imaging that overcomes this limitation through an efficient iterative solution of the inverse elasticity problem using adjoint elasticity equations. We present solutions for linear elastic, isotropic, and incompressible solids; however, this method can be employed for more complex mechanical models. We retrieve the spatial distribution of shear modulus for a tissue-simulating phantom and a tissue sample. This is the first time, to our knowledge, that the iterative solution of the inverse elasticity problem has been implemented on experimentally acquired compression optical coherence elastography data.

Topics
  • impedance spectroscopy
  • surface
  • elasticity
  • isotropic