Effects of cricothyroid and thyroarytenoid interaction on voice control: Muscle activity, vocal fold biomechanics, flow, and acoustics.

An MRI-based three-dimensional computer model of a canine larynx was used to investigate the effect of cricothyroid (CT) and thyroarytenoid (TA) muscle activity on vocal fold pre-phonatory posturing and glottic dynamics during voice production. Static vocal fold posturing in the full activation space of CT and TA muscles was first simulated using a laryngeal muscle mechanics model; dynamic flow-structure-acoustics interaction (FSAI) simulations were then performed to predict glottal flow and voice acoustics. The results revealed that TA activation decreased the length and increased the bulging, height, and contact area of the vocal fold. CT activation increased the length and contact area and decreased the height of the vocal fold. Both CT and TA activations increased the vocal fold stress, stiffness, and closure quotient; and only slightly affected the flow rate and voice intensity. Furthermore, CT and TA showed a complex control mechanism on the fundamental frequency pattern, which highly correlated with a combination of the stress, stiffness, and stretch of the vocal fold.

Vocal fold vibration mode changes due to cricothyroid and thyroarytenoid muscle interaction in a three-dimensional model of the canine larynx.

Using a continuum model based on magnetic resonance imaging of a canine larynx, parametric simulations of the vocal fold vibration during phonation were conducted with the cricothyroid muscle (CT) and the thyroarytenoid muscle (TA) independently activated from zero to full activation. The fundamental frequency (f0) first increased and then experienced a downward jump as TA activity gradually increased under moderate to high CT activation. Proper orthogonal decomposition analysis revealed that the vocal fold vibrations were dominated by two modes representing a lateral motion and rotational motion, respectively, and the f0 drop was associated with a switch on the order of the two modes. In another parametric set where only the vocalis was active, f0 increased monotonically with both TA and CT activity and the mode switch did not occur. The results suggested that the active stress in the TA, which causes large stress differences between the body and cover, is essential for the occurrence of the rotational mode and mode switch. Relatively greater TA activity tends to promote the rotational mode, while relatively greater CT activity tends to promote the lateral mode. The results also suggested that the vibration modes affected f0 by affecting the contribution of the TA stress to the effective stiffness. The switch in the dominant mode caused the non-monotonic change of f0.

Computed tomography-based automated 3D measurement of femoral version: Validation against standard 2D measurements in symptomatic patients.

To validate 3D methods for femoral version measurement, we asked: (1) Can a fully automated segmentation of the entire femur and 3D measurement of femoral version using a neck based method and a head-shaft based method be performed? (2) How do automatic 3D-based computed tomography (CT) measurements of femoral version compare to the most commonly used 2D-based measurements utilizing four different landmarks? Retrospective study (May 2017 to June 2018) evaluating 45 symptomatic patients (57 hips, mean age 18.7 ± 5.1 years) undergoing pelvic and femoral CT. Femoral version was assessed using four previously described methods (Lee, Reikeras, Tomczak, and Murphy). Fully-automated segmentation yielded 3D femur models used to measure femoral version via femoral neck- and head-shaft approaches. Mean femoral version with 95% confidence intervals, and intraclass correlation coefficients were calculated, and Bland-Altman analysis was performed. Automatic 3D segmentation was highly accurate, with mean dice coefficients of 0.98 ± 0.03 and 0.97 ± 0.02 for femur/pelvis, respectively. Mean difference between 3D head-shaft- (27.4 ± 16.6°) and 3D neck methods (12.9 ± 13.7°) was 14.5 ± 10.7° (p < 0.001). The 3D neck method was closer to the proximal Lee (-2.4 ± 5.9°, -4.4 to 0.5°, p = 0.009) and Reikeras (2 ± 5.6°, 95% CI: 0.2 to 3.8°, p = 0.03) methods. The 3D head-shaft method was closer to the distal Tomczak (-1.3 ± 7.5°, 95% CI: -3.8 to 1.1°, p = 0.57) and Murphy (1.5 ± 5.4°, -0.3 to 3.3°, p = 0.12) methods. Automatic 3D neck-based-/head-shaft methods yielded femoral version angles comparable to the proximal/distal 2D-based methods, when applying fully-automated segmentations.

Excessive Femoral Anteversion Leading to Symptomatic Posterior Femoroacetabular Impingement, Cam Deformity of the Posterior Femoral Head-Neck Junction, and Anterior Hip Instability in a Dancer: A Case Report.

CASE: An adolescent female dancer with excessive femoral anteversion presented with posterior and anterior hip pain aggravated by poses that required extension and external rotation. Imaging revealed an atypical cam deformity of the posterior head-neck junction. During surgery, the posterior head-neck junction was observed to impinge on the posterior acetabulum with anterior subluxation of the hip. After a derotational femoral osteotomy, the patient experienced resolution of her symptoms. CONCLUSION: Excessive femoral anteversion can lead to reactive cam deformity, posterior intra-articular impingement, and anterior hip instability in patients who require repetitive hip extension and external rotation, such as ballet dancers.

Predicting 3D soft tissue dynamics from 2D imaging using physics informed neural networks.

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 for patient-specific surgical planning of type 1 thyroplasty.

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).