One of the critical components of large-scale manufacturing of bioengineered tissues is the sensing of information for quality control and critical feedback of tissue growth. Modern sensors that measure mechanical qualities of tissues, however, are invasive and destructive. The goal of this project is to develop noninvasive methodologies to measure the mechanical properties of tissue engineering products. Our approach is to utilize acoustic waves to induce nanoscale level vibrations in the engineered tissues in which corresponding displacements are measured in full-field with quantitative optical techniques. A digital holographic system images the tissue’s vibration at significant modes and provides the displacement patterns of the tissue. These data are used to train a supervised learning classifier with a goal of using the comparisons between the experimental vibrational modes and the ones obtained by finite element simulation to estimate the physical properties of the tissue. This methodology has the promise of mechanical properties that would allow technicians to noninvasively determine when samples are ready to be packaged, if their growth deviates from expected time frames, or if there are defects in the tissue. It is expected that this approach will streamline several components of the quality control and production process.
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This content will become publicly available on July 1, 2024
Characterization of Bioengineered Tissues by Digital Holographic Vibrometry and 3D Shape Deep Learning
Tissue engineering is an active field and one of its aims is to produce tissues to repair the human body. The Advanced Regenerative Medicine Initiative (ARMI) currently seeks to help increase the manufacturability of tissue engineering products (TEMPs). One of the critical components of large-scale manufacturing is the sensing of information for quality control and critical feedback of tissue growth patterns. Modern sensors that provide information about physical qualities of tissues, however, are invasive or destructive. The goal of this project is to develop noninvasive methodologies to measure the mechanical properties of TEMPs. Our approach is to utilize acoustic waves to induce nano-scale level vibrations in the enginineered tissues in which corresponding displacements are measured in full-field with quantitative optical techniques. In our work, a digital holographic system images the tissue’s vibration at significant modes and provides the displacement patterns of the tissue at various points along the sinusoidal excitation curve. These data are applied to a neural network to compare the experimental vibrational modes to the ones obtained by FEA simulation to estimate the physical properties of the tissue. This methodology has the promise of yielding critical control parameters that would allow technicians to noninvasively and consistently determine when samples are ready to be packaged or if their growth deviates from expected time frames or if there are defects in the tissue. It is expected that this approach will streamline several components of the quality control and production process.
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- Award ID(s):
- 1761432
- NSF-PAR ID:
- 10440489
- Editor(s):
- Lin, MT.; Furlong, C.; Hwang, CH.; Naraghi, M.; DelRio, F.
- Date Published:
- Journal Name:
- 2022 Conference Proceedings of the Society for Experimental Mechanics Series
- Volume:
- 4
- Page Range / eLocation ID:
- 57-62
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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