Vocal Fold Reconstruction from Optical Velocity and Displacement Measurements

TL;DR

This paper introduces a novel method for 3D vocal fold reconstruction using optical velocity measurements from only the superior side, employing inverse FEM simulation and tensor product basis for force field optimization.

Contribution

The approach reconstructs the full 3D vocal fold geometry from optical measurements on just the superior side, using inverse FEM and advanced optimization techniques.

Findings

Accurately reconstructs vocal fold geometry from optical data.

Uses inverse FEM with gradient descent for force estimation.

Reduces degrees of freedom with tensor product basis.

Abstract

The three-dimensional reconstruction of vocal folds in medicine usually involves endoscopy and an approach to extract depth information like structured light or stereo matching of images. The resulting mesh can accurately represent the superior area of the vocal folds, while new approaches also try to reconstruct the inferior area. We propose a novel approach to extract the time-dependent 3D geometry of the vocal fold from optical measurements on both the superior and inferior side, requiring optical measurements only from the superior side. First, a time-dependent, tri-variate surface velocity vector field is reconstructed using a high-speed camera and a laser Doppler vibrometer in an experimental environment. This vector field serves as target in an inverse finite-element simulation that optimizes the forces applied to a deformable vocal fold model such that the resulting movement after FEM simulation matches the velocity observations on the superior side. The required forces for the finite element method simulation are treated as unknowns and are assembled using multiple scalar fields. We use tensor products in B\'ezier Bernstein basis for our scalar fields to reduce the degrees of freedom for our optimization. We use gradient descent to optimize the control points of the force field polynomials. Our utilized error metric for gradient descent consists of two terms. The first term is used to match the simulated velocities to the observed measurements, while the second term measures the silhouette difference between observation and simulation.