MRI-based strain mapping for cartilage repair assessment

Authors
Sajid, Sameer
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Other Contributors
Corr, David T.
Chan, D. D. (Deva D.)
Wan, Leo Q.
Issue Date
2021-05
Keywords
Biomedical engineering
Degree
MS
Terms of Use
Attribution-NonCommercial-NoDerivs 3.0 United States
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
I conclude that MRI-based strains should be considered for cartilage repair evaluation. The mean strains of both the interface and regions of altered material properties correlated with mechanical stiffness changes. As a result, MRI-based strain mapping has clear qualitative value in assessing relative mechanical property differences.
Successful cartilage repair must feature a close match of material properties. While tissue viscoelasticity match represents the ideal repair scenario, many TE studies focus their effects on recapitulating Young’s modulus first. I built a finite element (FE) of cartilage tissue featuring an inclusion of altered stiffness. I then synthesized MRI imaging-derived displacement maps using the model to simulate MRI-based strain mapping. I also analyzed strain map topography through texture-based spatial analysis techniques. I found that the mean ratio of the compressive strains of the inclusion versus the surrounding tissue strain was nearly perfectly correlated with the change in stiffness, with some correlation with spatial analyses heuristics. Additionally, MRI-based strains qualitatively visualized the regions of altered stiffness.
Finally, I also assessed interface mechanics in a patient-based FE model of knee cartilage. I created a defect in the femoral tissue and filled it with repair tissue of varying material properties and interface strength. I examined the effects of the material stiffness and interface strength on the MRI-based strains and spatial analyses of the interface, compared to those of an intact model. I found that all strains were highly correlated with material stiffness match, but did not change at all based on interface strength.
Current cartilage repair strategies fall short of therapeutic repair due to a lack of true tissue integration. As cutting-edge tissue engineering (TE) approaches advance toward recapitulation of the depth-dependent healthy tissue structure, repair assessment methods have not kept pace. Most in vitro TE studies employ destructive, singleton measurements that ignore spatially-dependent mechanical information, or they examine through-thickness mechanics by altering boundary conditions. Clinical research employs in vivo qualitative morphological assessment that ignores mechanical function entirely. There is a need for non-destructive, full-depth assessment of tissue mechanics.MRI is the current gold standard for assessing cartilage repair in vivo due to excellent tissue contrast and ability to visualize tissue morphology. MRI-based strain mapping can visualize changes associated with cartilage tissue defects and has been performed in vivo. However, the ability of MRI-based strains to evaluate cartilage repair has not been assessed. This was done through four key features of cartilage repair; tissue viscoelasticity, depth-dependent material properties, repair stiffness match, and interface mechanics.
Here, I assessed the ability of MRI-based strain mapping in evaluating these features of cartilage repair. As TE methods move toward recapitulating tissue viscoelasticity, I attempted to fit full-depth tissue deformations to a quasi-linear viscoelastic model of cartilage; however, adequate fits required infeasible SNR and temporal resolution.
Previous studies have examined depth-wise strain distribution as a correlate for depth-dependent material properties. I analyzed the strain distribution of MRI-based strain maps from depth-dependent agarose phantoms, from previously published work. While the strain distribution was more specific than mean strain in detecting differences in agarose concentration, it proved less sensitive. Thus, MRI-based mean strains were considered for the remaining work.
Description
May 2021
School of Engineering
Department
Dept. of Biomedical Engineering
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
Access
CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.