Literature DB >> 30218776

Relating the mechanical properties of atherosclerotic calcification to radiographic density: A nanoindentation approach.

Rachel M Cahalane1, Hilary E Barrett2, Julie M O'Brien3, Eamon G Kavanagh4, Michael A Moloney4, Michael T Walsh5.   

Abstract

Calcification morphology can determine atherosclerotic plaque stability and is associated with increased failures rates for endovascular interventions. Computational efforts have sought to elucidate the relationship between calcification and plaque rupture in addition to predicting tissue response during aggressive revascularisation techniques. However, calcified material properties are currently estimated and may not reflect real tissue conditions. The objective of this study is to correlate calcification mechanical properties with three radiographic density groups obtained from corresponding Computed Tomography (CT) images. Seventeen human plaques extracted from carotid (n = 10) and peripheral lower limb (n = 7) arteries were examined using micro-computed tomography (µCT), simultaneously locating the calcified deposits within their internal structure and quantifying their densities. Three radiographic density groups were defined based on the sample density distribution: (A) 130-299.99 Hounsfield Units (HU), (B) 300-449.99 HU and (C) >450 HU. Nanoindentation was employed to determine the Elastic Modulus (E) and Hardness (H) values within the three density groups. Results reveal a clear distinction between mechanical properties with respect to radiographic density groups (p < 0.0005). No significant differences exist in the density-specific behaviours observed between carotid and peripheral samples. Previously defined calcification classifications indicate an association with specific radiographic density patterns. Scanning Electron Microscopy (SEM) examination revealed that density group A regions consist of both calcified and non-calcified tissues. Further research is required to define the radiographic thresholds which identify varying degrees of tissue calcification. This study demonstrates that the mechanical properties of fully mineralised atherosclerotic calcification emulate that of bone tissues (17-25 GPa), affording computational models with accurate material parameters. STATEMENT OF SIGNIFICANCE: Global mechanical characterisation techniques disregard the heterogeneous nature of atherosclerotic lesions. Previous nanoindentation results for carotid calcifications have displayed a wide range. This study evaluates calcification properties with respect to radiographic density obtained from Micro-CT images. This is the first work to characterise calcifications from peripheral lower limb arteries using nanoindentation. Results demonstrate a strong positive correlation between radiographic density and calcification mechanical properties. Characterising calcifications using their density values provides clarity on the variation in published properties for calcified tissues. Furthermore, this study confirms the hypothesis that fully calcified plaque tissue behaviour similar to that of bone. Appropriate material parameters for calcified tissues can now be employed in computational simulations.
Copyright © 2018 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Atherosclerosis; Calcification; Computed tomography; Mechanical characterisation; Nanoindentation

Mesh:

Year:  2018        PMID: 30218776     DOI: 10.1016/j.actbio.2018.09.010

Source DB:  PubMed          Journal:  Acta Biomater        ISSN: 1742-7061            Impact factor:   8.947


  3 in total

1.  Microarchitectural Changes of Cardiovascular Calcification in Response to In Vivo Interventions Using Deep-Learning Segmentation and Computed Tomography Radiomics.

Authors:  Nikhil Rajesh Patel; Kulveer Setya; Stuti Pradhan; Mimi Lu; Linda L Demer; Yin Tintut
Journal:  Arterioscler Thromb Vasc Biol       Date:  2022-06-16       Impact factor: 10.514

2.  Effect of macro-calcification on the failure mechanics of intracranial aneurysmal wall tissue.

Authors:  R N Fortunato; A M Robertson; C Sang; X Duan; S Maiti
Journal:  Exp Mech       Date:  2020-09-25       Impact factor: 2.808

3.  Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery.

Authors:  Pengfei Dong; Guochang Ye; Mehmet Kaya; Linxia Gu
Journal:  Appl Sci (Basel)       Date:  2020-08-22       Impact factor: 2.838

  3 in total

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