Crossing muscle fibers of the human tongue resolved in vivo using constrained spherical deconvolution

Pig tongue soft robot mimicking intrinsic tongue muscle structure

Animal muscles have complex, three-dimensional structures with fibers oriented in various directions. The tongue, in particular, features a highly intricate muscular system composed of four intrinsic muscles and several types of extrinsic muscles, enabling flexible and diverse movements essential for feeding, swallowing, and speech production. Replicating these structures could lead to the development of multifunctional manipulators and advanced platforms for studying muscle-motion relationships. In this study, we developed a pig tongue soft robot that focuses on replicating the intrinsic muscles using thin McKibben artificial muscles, silicone rubber, and gel. We began by performing three-dimensional scans and sectional observations in the coronal and sagittal planes to examine the arrangement and orientation of the intrinsic muscles in the actual pig tongue. Additionally, we used the diffusible iodine-based contrast-enhanced computed tomography (Dice-CT) technique to observe the three-dimensional flow of muscle pathways. Based on these observations, we constructed a three-dimensional model and molded the pig tongue shape with silicone rubber and gel, embedding artificial muscles into the robot body. We conducted experiments to assess both the motion of the tongue robot's tip and its stiffness during muscle contractions. The results confirmed characteristic tongue motions, such as tip extension, flexion, and lateral bending, as well as stiffness changes during actuation, suggesting the potential for this soft robot to serve as a platform for academic and engineering studies.

Research Progress in Diffusion Spectrum Imaging

Studies have demonstrated that many regions in the human brain include multidirectional fiber tracts, in which the diffusion of water molecules within image voxels does not follow a Gaussian distribution. Therefore, the conventional diffusion tensor imaging (DTI) that hypothesizes a single fiber orientation within a voxel is intrinsically incapable of revealing the complex microstructures of brain tissues. Diffusion spectrum imaging (DSI) employs a pulse sequence with different b-values along multiple gradient directions to sample the diffusion information of water molecules in the entire q-space and then quantitatively estimates the diffusion profile using a probability density function with a high angular resolution. Studies have suggested that DSI can reliably observe the multidirectional fibers within each voxel and allow fiber tracking along different directions, which can improve fiber reconstruction reflecting the true but complicated brain structures that were not observed in the previous DTI studies. Moreover, with increasing angular resolution, DSI is able to reveal new neuroimaging biomarkers used for disease diagnosis and the prediction of disorder progression. However, so far, this method has not been used widely in clinical studies, due to its overly long scanning time and difficult post-processing. Within this context, the current paper aims to conduct a comprehensive review of DSI research, including the fundamental principles, methodology, and application progress of DSI tractography. By summarizing the DSI studies in recent years, we propose potential solutions towards the existing problem in the methodology and applications of DSI technology as follows: (1) using compressed sensing to undersample data and to reconstruct the diffusion signal may be an efficient and promising method for reducing scanning time; (2) the probability density function includes more information than the orientation distribution function, and it should be extended in application studies; and (3) large-sample study is encouraged to confirm the reliability and reproducibility of findings in clinical diseases. These findings may help deepen the understanding of the DSI method and promote its development in clinical applications.

Analysis of Tongue Muscle Strain During Speech From Multimodal Magnetic Resonance Imaging

PURPOSE

Muscle groups within the tongue in healthy and diseased populations show different behaviors during speech. Visualizing and quantifying strain patterns of these muscle groups during tongue motion can provide insights into tongue motor control and adaptive behaviors of a patient.

METHOD

We present a pipeline to estimate the strain along the muscle fiber directions in the deforming tongue during speech production. A deep convolutional network estimates the crossing muscle fiber directions in the tongue using diffusion-weighted magnetic resonance imaging (MRI) data acquired at rest. A phase-based registration algorithm is used to estimate motion of the tongue muscles from tagged MRI acquired during speech. After transforming both muscle fiber directions and motion fields into a common atlas space, strain tensors are computed and projected onto the muscle fiber directions, forming so-called strains in the line of actions (SLAs) throughout the tongue. SLAs are then averaged over individual muscles that have been manually labeled in the atlas space using high-resolution T2-weighted MRI. Data were acquired, and this pipeline was run on a cohort of eight healthy controls and two glossectomy patients.

RESULTS

The crossing muscle fibers reconstructed by the deep network show orthogonal patterns. The strain analysis results demonstrate consistency of muscle behaviors among some healthy controls during speech production. The patients show irregular muscle patterns, and their tongue muscles tend to show more extension than the healthy controls.

CONCLUSIONS

The study showed visual evidence of correlation between two muscle groups during speech production. Patients tend to have different strain patterns compared to the controls. Analysis of variations in muscle strains can potentially help develop treatment strategies in oral diseases.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.21957011.

Validation of Muscle Fiber Architecture of the Human Tongue Revealed by Diffusion Magnetic Resonance Imaging With Histology Verification

PURPOSE

The goal of this study is to validate the muscle architecture derived from both ex vivo and in vivo diffusion-weighted magnetic resonance imaging (dMRI) of the human tongue with histology of an ex vivo tongue.

METHOD

dMRI was acquired with a 200-direction high angular resolution diffusion imaging (HARDI) diffusion scheme for both a postmortem head (imaged within 48 hr after death) and a healthy volunteer. After MRI, the postmortem head was fixed and the tongue excised for hematoxylin and eosin (H&E) staining and histology imaging. Structure tensor images were generated from the stained images to better demonstrate muscle fiber orientations. The tongue muscle fiber orientations, estimated from dMRI, were visualized using the tractogram, a novel representation of crossing fiber orientations, and compared against the histology images of the ex vivo tongue.

RESULTS

Muscle fibers identified in the tractograms showed good correspondence with those appearing in the histology images. We further demonstrated tongue muscle architecture in in vivo tractograms for the entire tongue.

CONCLUSION

The study demonstrates that dMRI can accurately reveal the complex muscle architecture of the human tongue and may potentially benefit planning and evaluation of oral surgery and research on speech and swallowing.

Personalized biomechanical tongue models based on diffusion-weighted MRI and validated using optical tracking of range of motion

For advanced tongue cancer, the choice between surgery and organ-sparing treatment is often dependent on the expected loss of tongue functionality after treatment. Biomechanical models might assist in this choice by simulating the post-treatment function loss. However, this function loss varies between patients and should, therefore, be predicted for each patient individually. In the present study, the goal was to better predict the postoperative range of motion (ROM) of the tongue by personalizing biomechanical models using diffusion-weighted MRI and constrained spherical deconvolution reconstructions of tongue muscle architecture. Diffusion-weighted MRI scans of ten healthy volunteers were obtained to reconstruct their tongue musculature, which were subsequently registered to a previously described population average or atlas. Using the displacement fields obtained from the registration, the segmented muscle fiber tracks from the atlas were morphed back to create personalized muscle fiber tracks. Finite element models were created from the fiber tracks of the atlas and those of the individual tongues. Via inverse simulation of a protruding, downward, left and right movement, the ROM of the tongue was predicted. This prediction was compared to the ROM measured with a 3D camera. It was demonstrated that biomechanical models with personalized muscles bundles are better in approaching the measured ROM than a generic model. However, to achieve this result a correction factor was needed to compensate for the small magnitude of motion of the model. Future versions of these models may have the potential to improve the estimation of function loss after treatment for advanced tongue cancer.

Prospective motion detection and re-acquisition in diffusion MRI using a phase image-based method-Application to brain and tongue imaging

PURPOSE

To develop an image-based motion-robust diffusion MRI (dMRI) acquisition framework that is able to minimize motion artifacts caused by rigid and nonrigid motion, applicable to both brain and tongue dMRI.

METHODS

We developed a novel prospective motion-correction technique in dMRI using a phase image-based real-time motion-detection method (PITA-MDD) with re-acquisition of motion-corrupted images. The prospective PITA-MDD acquisition technique was tested in the brains and tongues of volunteers. The subjects were instructed to move their heads or swallow, to induce motion. Motion-detection efficacy was validated against visual inspection as the gold standard. The effect of the PITA-MDD technique on diffusion-parameter estimates was evaluated by comparing reconstructed fiber tracts using tractography with and without re-acquisition.

RESULTS

The prospective PITA-MDD technique was able to effectively and accurately detect motion-corrupted data as compared with visual inspection. Tractography results demonstrated that PITA-MDD motion detection followed by re-acquisition helps in recovering lost and misshaped fiber tracts in the brain and tongue that would otherwise be corrupted by motion and yield erroneous estimates of the diffusion tensor.

CONCLUSION

A prospective PITA-MDD technique was developed for dMRI acquisition, providing improved dMRI image quality and motion-robust diffusion estimation of the brain and tongue.

Diffusion tensor imaging of oral carcinoma: Clinical evaluation and comparison with histopathological findings

PURPOSE

This study aims to assess the usefulness of diffusion tensor imaging (DTI) as a noninvasive method for the evaluation of histological grade and lymph node metastasis in patients with oral carcinoma (OC).

MATERIALS AND METHODS

Thirty-six consecutive patients with histologically confirmed OC underwent examination by 3-T MRI. DTI was performed using a single-shot echo-planar imaging sequence with b values of 0 and 1000 s/mm² and motion-probing gradients in 12 noncollinear directions. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) maps were compared with histopathological findings. The DTI parameters were correlated with the histological grade of the OCs based on the World Health Organization grading criteria and the presence or absence of lymph node metastasis.

RESULTS

The FA values (0.275 ± 0.058) of OC were significantly lower than those of normal tongue, muscle, and parotid glands (P < 0.001 for all), and the MD, AD, and RD values (1.220 ± 0.149, 1.434 ± 0.172, and 1.019 ± 0.165 × 10^-3 mm²/s, respectively) were significantly higher than their respective normal values (P < 0.001 for all). Significant inverse correlations with histological grades were shown for FA, MD, AD, and RD values in OC patients (r = -0.862, r = -0.797, r = -0.747, and r = -0.844, respectively; P < 0.001 for all). In addition, there was a significant difference in the FA values of metastatic and nonmetastatic lymph nodes (0.186 vs. 0.276), MD (0.923 vs. 1.242 × 10^-3 mm²/s), AD (1.246 vs. 1.621 × 10^-3 mm²/s), and RD (0.792 vs. 1.100 × 10^-3 mm²/s; P < 0.001 for all).

CONCLUSIONS

DTI may be clinically useful for the noninvasive evaluation of histological grade and lymph node metastasis in OC patients.

A 12-channel flexible receiver coil for accelerated tongue imaging

Objective

MRI of the tongue requires acceleration to minimise motion artefacts and to facilitate real-time imaging of swallowing. To accelerate tongue MRI, we designed a dedicated flexible receiver coil.

Materials and methods

We designed a flexible 12-channel receiver coil for tongue MRI at 3T and compared it to a conventional head-and-neck coil regarding SNR and g-factor. Furthermore, two accelerated imaging techniques were evaluated using both coils: multiband (MB) diffusion-tensor imaging (DTI) and real-time MRI of swallowing.

Results

The flexible coil had significantly higher SNR in the anterior (2.1 times higher, P = 0.002) and posterior (2.0 times higher, P < 0.001) parts of the tongue, while the g-factor was lower at higher acceleration. Unlike for the flexible coil, the apparent diffusion coefficient (P = 0.001) and fractional anisotropy (P = 0.008) deteriorated significantly while using the conventional coil after accelerating DTI with MB. The image quality of real-time MRI of swallowing was significantly better for hyoid elevation (P = 0.029) using the flexible coil.

Conclusion

Facilitated by higher SNR and lower g-factor values, our flexible tongue coil allows faster imaging, which was successfully demonstrated in MB DTI and real-time MRI of swallowing.

Electronic supplementary material

The online version of this article (10.1007/s10334-019-00824-5) contains supplementary material, which is available to authorized users.