Parametric Deconvolution for Cancer Cells Viscoelasticity Measurements from Quantitative Phase Images
| Authors | |
|---|---|
| Year of publication | 2021 |
| Type | Article in Proceedings |
| Conference | 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) |
| MU Faculty or unit | |
| Citation | |
| web | https://pubmed.ncbi.nlm.nih.gov/34891327/ |
| Doi | http://dx.doi.org/10.1109/EMBC46164.2021.9630524 |
| Keywords | Cancer Cells Viscoelasticity Measurements; Parametric Deconvolution; Quantitative Phase Images |
| Description | In this contribution, we focused on optimising a dynamic flow-based shear stress system to achieve a reliable platform for cell shear modulus (stiffness) and viscosity assessment using quantitative phase imaging. The estimation of cell viscoelastic properties is influenced by distortion of the shear stress waveform, which is caused by the properties of the flow system components (i.e., syringe, flow chamber and tubing). We observed that these components have a significant influence on the measured cell viscoelastic characteristics. To suppress this effect, we applied a correction method utilizing parametric deconvolution of the flow system's optimized impulse response. Achieved results were compared with the direct fitting of the Kelvin-Voigt viscoelastic model and the basic steady-state model. The results showed that our novel parametric deconvolution approach is more robust and provides a more reliable estimation of viscosity with respect to changes in the syringe's compliance compared to Kelvin-Voigt model. |
| Related projects: |