Objective/Hypothesis The objective of the present study was to quantify the mechanical strain and stress in excised porcine larynges during self-oscillation using digital image correlation (DIC) method. The use of DIC in the excised larynx setup may yield accurate measurements of the vocal fold displacement field. Study Design Ex vivo animal larynx. Methods Measurements were performed using excised porcine larynges on a humidified flow bench, equipped with two high-speed cameras and a commercially available DIC software. Surface deformations were calculated from digital images recorded at 3000 frames per second during continuous self-oscillation for four excised porcine larynges. Larynx preparation consisted of removing the supraglottal wall and the false folds. DIC yielded the deformation field on the superior visible surface of the vocal folds. Measurement data for adducted and freely suspended vocal folds were also used to estimate the distribution of the initial prephonatory strain field. An isotropic constitutive law, the polymer eight-chain model, was used to estimate the surface distributions of planar stresses from the strain data. Results The Lagrangian normal strain values were between ∼16% and ∼29% along the anterior-posterior direction. The motion of material points on the vocal fold surface described an elliptical trajectory during oscillation. A phase difference was observed between the anterior-posterior and the medial-lateral component of the displacement. The strain data and eight-chain model yielded a maximum stress of ∼4 kPa along the medial-lateral direction on the superior surface. Conclusion DIC allowed the strain field over the superior surface of an excised porcine larynx to be quantified during self-oscillation. The approach allowed the determination of the trajectory of specific points on the vocal fold surface. The results for the excised larynx were found to be significantly different than previous results obtained using synthetic replicas. The present study provides suggestions for future studies in human subjects.
All Science Journal Classification (ASJC) codes
- Speech and Hearing
- LPN and LVN