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Abstract Tissue dynamics play critical roles in many physiological functions and provide important metrics for clinical diagnosis. Capturing real-time high-resolution 3D images of tissue dynamics, however, remains a challenge. This study presents a hybrid physics-informed neural network algorithm that infers 3D flow-induced tissue dynamics and other physical quantities from sparse 2D images. The algorithm combines a recurrent neural network model of soft tissue with a differentiable fluid solver, leveraging prior knowledge in solid mechanics to project the governing equation on a discrete eigen space. The algorithm uses a Long-short-term memory-based recurrent encoder-decoder connected with a fully connected neural network to capture the temporal dependence of flow-structure-interaction. The effectiveness and merit of the proposed algorithm is demonstrated on synthetic data from a canine vocal fold model and experimental data from excised pigeon syringes. The results showed that the algorithm accurately reconstructs 3D vocal dynamics, aerodynamics, and acoustics from sparse 2D vibration profiles. 

A computational framework is proposed for virtual optimization of implant configurations of type 1 thyroplasty based on patient-specific laryngeal structures reconstructed from MRI images. Through integration of a muscle mechanics-based laryngeal posturing model, a flow-structure-acoustics interaction voice production model, a real-coded genetic algorithm, and virtual implant insertion, the framework acquires the implant configuration that achieves the optimal acoustic objectives. The framework is showcased by successfully optimizing an implant that restores acoustic features of a diseased voice resulted from unilateral vocal fold paralysis (UVFP) in producing a sustained vowel utterance. The sound intensity is improved from 62 dB (UVFP) to 81 dB (post-correction).