Analysis of fiber strain in the human tongue during speech.

This study investigates mechanical cooperation among tongue muscles. Five volunteers were imaged using tagged magnetic resonance imaging to quantify spatiotemporal kinematics while speaking. Waveforms of strain in the line of action of fibers (SLAF) were estimated by projecting strain tensors onto a model of fiber directionality. SLAF waveforms were temporally aligned to determine consistency across subjects and correlation across muscles. The cohort exhibited consistent patterns of SLAF, and muscular extension-contraction was correlated. Volume-preserving tongue movement in speech generation can be achieved through multiple paths, but the study reveals similarities in motion patterns and muscular action-despite anatomical (and other) dissimilarities.

A Novel Filtering Approach for 3D Harmonic Phase Analysis of Tagged MRI.

Harmonic phase analysis has been used to perform noninvasive organ motion and strain estimation using tagged magnetic resonance imaging (MRI). The filtering process, which is used to produce harmonic phase images used for tissue tracking, influences the estimation accuracy. In this work, we evaluated different filtering approaches, and propose a novel high-pass filter for volumes tagged in individual directions. Testing was done using an open benchmarking dataset and synthetic images obtained using a mechanical model. We compared estimation results from our filtering approach with results from the traditional filtering approach. Our results indicate that 1) the proposed high-pass filter outperforms the traditional filtering approach reducing error by as much as 50% and 2) the accuracy improvements are especially marked in complex deformations.

Inverse Biomechanical Modeling of the Tongue via Machine Learning and Synthetic Training Data.

The tongue's deformation during speech can be measured using tagged magnetic resonance imaging, but there is no current method to directly measure the pattern of muscles that activate to produce a given motion. In this paper, the activation pattern of the tongue's muscles is estimated by solving an inverse problem using a random forest. Examples describing different activation patterns and the resulting deformations are generated using a finite-element model of the tongue. These examples form training data for a random forest comprising 30 decision trees to estimate contractions in 262 contractile elements. The method was evaluated on data from tagged magnetic resonance data from actual speech and on simulated data mimicking flaps that might have resulted from glossectomy surgery. The estimation accuracy was modest (5.6% error), but it surpassed a semi-manual approach (8.1% error). The results suggest that a machine learning approach to contraction pattern estimation in the tongue is feasible, even in the presence of flaps.

Laplace-based modeling of fiber orientation in the tongue.

Mechanical modeling of tongue deformation plays a significant role in the study of breathing, swallowing, and speech production. In the absence of internal joints, fiber orientations determine the direction of sarcomeric contraction and have great influence over real and simulated tissue motion. However, subject-specific experimental observations of fiber distribution are difficult to obtain; thus, models of fiber distribution are generally used in mechanical simulations. This paper describes modeling of fiber distribution using solutions of Laplace equations and compares the effectiveness of this approach against tractography from diffusion tensor magnetic resonance imaging. The experiments included qualitative comparison of streamlines from the fiber model against experimental tractography, as well as quantitative differences between biomechanical simulations focusing in the region near the genioglossus. The model showed good overall agreement in terms of fiber directionality and muscle positioning when compared to subject-specific imaging results and the literature. The angle between the fiber distribution model against tractography in the genioglossus and geniohyoid muscles averaged [Formula: see text] likely due to experimental noise. However, kinematic responses were similar between simulations with modeled fibers versus experimentally obtained fibers; average discrepancy in surface displacement ranged from 1 to 7 mm, and average strain residual magnitude ranged from [Formula: see text] to 0.2. The results suggest that, for simulation purposes, the modeled fibers can act as a reasonable approximation for the tongue's fiber distribution. Also, given its agreement with the global tongue anatomy, the approach may be used in model-based reconstruction of displacement tracking and diffusion results.

Quantifying Tensor Field Similarity With Global Distributions and Optimal Transport.

Strain tensor fields quantify tissue deformation and are important for functional analysis of moving organs such as the heart and the tongue. Strain data can be readily obtained using medical imaging. However, quantification of similarity between different data sets is difficult. Strain patterns vary in space and time, and are inherently multidimensional. Also, the same type of mechanical deformation can be applied to different shapes; hence, automatic quantification of similarity should be unaffected by the geometry of the objects being deformed. This work introduces the application of global distributions used to classify shapes and vector fields in the pattern recognition literature, in the context of tensorial strain data. In particular, the distribution of mechanical properties of a field are approximated using a 3D histogram, and the Wasserstein distance from optimal transport theory is used to measure the similarity between histograms. To measure the method's consistency in matching deformations across different objects, the proposed approach was evaluated by sorting strain fields according to their similarity. Performance was compared to sorting via maximum shear distribution (a 1D histogram) and tensor residual magnitude (in perfectly registered objects). The technique was also applied to correlate muscle activation to muscular contraction observed via tagged MRI. The results show that the proposed approach accurately matches deformation regardless of the shape of the object being deformed. Sorting accuracy surpassed 1D shear distribution and was on par with residual magnitude, but without the need for registration between objects.