A High-resolution Atlas and Statistical Model of the Vocal Tract from Structural MRI

Preliminary Development of an MRI Atlas for Application to Cleft Care: Findings and Future Recommendations

OBJECTIVE

To introduce a highly innovative imaging method to study the complex velopharyngeal (VP) system and introduce the potential future clinical applications of a VP atlas in cleft care.

DESIGN

Four healthy adults participated in a 20-min dynamic magnetic resonance imaging scan that included a high-resolution T2-weighted turbo-spin-echo 3D structural scan and five custom dynamic speech imaging scans. Subjects repeated a variety of phrases when in the scanner as real-time audio was captured.

SETTING

Multisite institution and clinical setting.

PARTICIPANTS

Four adult subjects with normal anatomy were recruited for this study.

MAIN OUTCOME

Establishment of 4-D atlas constructed from dynamic VP MRI data.

RESULTS

Three-dimensional dynamic magnetic resonance imaging was successfully used to obtain high quality dynamic speech scans in an adult population. Scans were able to be re-sliced in various imaging planes. Subject-specific MR data were then reconstructed and time-aligned to create a velopharyngeal atlas representing the averaged physiological movements across the four subjects.

CONCLUSIONS

The current preliminary study examined the feasibility of developing a VP atlas for potential clinical applications in cleft care. Our results indicate excellent potential for the development and use of a VP atlas for assessing VP physiology during speech.

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.

3D Dynamic Spatiotemporal Atlas of the Vocal Tract during Consonant–Vowel Production from 2D Real Time MRI

In this work, we address the problem of creating a 3D dynamic atlas of the vocal tract that captures the dynamics of the articulators in all three dimensions in order to create a global speaker model independent of speaker-specific characteristics. The core steps of the proposed method are the temporal alignment of the real-time MR images acquired in several sagittal planes and their combination with adaptive kernel regression. As a preprocessing step, a reference space was created to be used in order to remove anatomical information of the speakers and keep only the variability in speech production for the construction of the atlas. The adaptive kernel regression makes the choice of atlas time points independently of the time points of the frames that are used as an input for the construction. The evaluation of this atlas construction method was made by mapping two new speakers to the atlas and by checking how similar the resulting mapped images are. The use of the atlas helps in reducing subject variability. The results show that the use of the proposed atlas can capture the dynamic behavior of the articulators and is able to generalize the speech production process by creating a universal-speaker reference space.

Brain MR Atlas Construction Using Symmetric Deep Neural Inpainting

Modeling statistical properties of anatomical structures using magnetic resonance imaging is essential for revealing common information of a target population and unique properties of specific subjects. In brain imaging, a statistical brain atlas is often constructed using a number of healthy subjects. When tumors are present, however, it is difficult to either provide a common space for various subjects or align their imaging data due to the unpredictable distribution of lesions. Here we propose a deep learning-based image inpainting method to replace the tumor regions with normal tissue intensities using only a patient population. Our framework has three major innovations: 1) incompletely distributed datasets with random tumor locations can be used for training; 2) irregularly-shaped tumor regions are properly learned, identified, and corrected; and 3) a symmetry constraint between the two brain hemispheres is applied to regularize inpainted regions. Henceforth, regular atlas construction and image registration methods can be applied using inpainted data to obtain tissue deformation, thereby achieving group-specific statistical atlases and patient-to-atlas registration. Our framework was tested using the public database from the Multimodal Brain Tumor Segmentation challenge. Results showed increased similarity scores as well as reduced reconstruction errors compared with three existing image inpainting methods. Patient-to-atlas registration also yielded better results with improved normalized cross-correlation and mutual information and a reduced amount of deformation over the tumor regions.

VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI

Deep learning has great potential for accurate detection and classification of diseases with medical imaging data, but the performance is often limited by the number of training datasets and memory requirements. In addition, many deep learning models are considered a ``black-box," thereby often limiting their adoption in clinical applications. To address this, we present a successive subspace learning model, termed VoxelHop, for accurate classification of Amyotrophic Lateral Sclerosis (ALS) using T2-weighted structural MRI data. Compared with popular convolutional neural network (CNN) architectures, VoxelHop has modular and transparent structures with fewer parameters without any backpropagation, so is well-suited to small dataset size and 3D volume data. Our VoxelHop has four key components, including (1) sequential expansion of near-to-far neighborhood for multi-channel 3D data; (2) subspace approximation for unsupervised dimension reduction; (3) label-assisted regression for supervised dimension reduction; and (4) concatenation of features and classification between controls and patients. Our experimental results demonstrate that our framework using a total of 20 controls and 26 patients achieves an accuracy of 93.48 % and an AUC score of 0.9394 in differentiating patients from controls, even with a relatively small number of datasets, showing its robustness and effectiveness. Our thorough evaluations also show its validity and superiority to the state-of-the-art 3D CNN classification methods. Our framework can easily be generalized to other classification tasks using different modalities.

A deep joint sparse non-negative matrix factorization framework for identifying the common and subject-specific functional units of tongue motion during speech

Intelligible speech is produced by creating varying internal local muscle groupings-i.e., functional units-that are generated in a systematic and coordinated manner. There are two major challenges in characterizing and analyzing functional units. First, due to the complex and convoluted nature of tongue structure and function, it is of great importance to develop a method that can accurately decode complex muscle coordination patterns during speech. Second, it is challenging to keep identified functional units across subjects comparable due to their substantial variability. In this work, to address these challenges, we develop a new deep learning framework to identify common and subject-specific functional units of tongue motion during speech. Our framework hinges on joint deep graph-regularized sparse non-negative matrix factorization (NMF) using motion quantities derived from displacements by tagged Magnetic Resonance Imaging. More specifically, we transform NMF with sparse and graph regularizations into modular architectures akin to deep neural networks by means of unfolding the Iterative Shrinkage-Thresholding Algorithm to learn interpretable building blocks and associated weighting map. We then apply spectral clustering to common and subject-specific weighting maps from which we jointly determine the common and subject-specific functional units. Experiments carried out with simulated datasets show that the proposed method achieved on par or better clustering performance over the comparison methods.Experiments carried out with in vivo tongue motion data show that the proposed method can determine the common and subject-specific functional units with increased interpretability and decreased size variability.

Floor-of-the-Mouth Muscle Function Analysis Using Dynamic Magnetic Resonance Imaging

To advance our understanding of speech motor control, it is essential to image and assess dynamic functional patterns of internal structures caused by the complex muscle anatomy inside the human tongue. Speech pathologists are investigating into new tools that help assessment of internal tongue muscle's cooperative mechanics on top of their anatomical differences. Previous studies using dynamic magnetic resonance imaging (MRI) of the tongue revealed that tongue muscles tend to function in different groups during speech, especially the floor-of-the-mouth (FOM) muscles. In this work, we developed a method that analyzed the unique functional pattern of the FOM muscles in speech. First, four-dimensional motion fields of the whole tongue were computed using tagged MRI. Meanwhile, a statistical atlas of the tongue was constructed to form a common space for subject comparison, while a manually delineated mask of internal tongue muscles was used to separate individual muscle's motion. Then we computed four-dimensional motion correlation between each muscle and the FOM muscle group. Finally, dynamic correlation of different muscle groups was compared and evaluated. We used data from a study group of nineteen subjects including both healthy controls and oral cancer patients. Results revealed that most internal tongue muscles coordinated in a similar pattern in speech while the FOM muscles followed a unique pattern that helped supporting the tongue body and pivoting its rotation. The proposed method can help provide further interpretation of clinical observations and speech motor control from an imaging point of view.

Imaging the breastfeeding swallow: Pilot study utilizing real-time MRI

OBJECTIVE

Knowledge of the breastfeeding swallow is limited by practical challenges. Radiation exposure to both mother and infant and the radiolucent properties of breastmilk make videofluoroscopy an unsuitable imaging modality. Furthermore, ultrasound is not ideal for capturing the complex 3-dimensional functional anatomy of swallowing. In this study we explore the feasibility of using real-time MRI to capture the breastfeeding swallow.

METHODS

Prospective observational study: Review of imaging from 12 normal infants (<5 months of age) and their mothers while breastfeeding using real-time MRI.

RESULTS

Static images were successfully captured in 11 infants and dynamic images in nine infants. This imaging modality confirms the dorsal surface of the infant's tongue elevates the maternal nipple to the hard palate, closing the space around the nipple with no air visible in the oral cavity during sucking and swallowing. We obtained dynamic imaging of mandibular movement with sucking, palatal elevation and pharyngeal constriction with swallowing, diaphragm movement with breathing and milk entering the stomach. Breastmilk was easily visualized, being high intensity on T2 sequences. Technical challenges were encountered secondary to infant movement and difficulties acquiring and maintaining midsagittal orientation. The similarity in tissue densities of the lips, tongue, nipple and hard palate limited definition between these structures.

CONCLUSION

Real-time MRI imaging was successful in capturing dynamic images of the breastfeeding swallow. However, technical and practical challenges make real-time MRI unlikely at present to be suitable for swallow assessment in clinical practice. Advances in technology and expertise in dynamic image capture may improve the feasibility of using MRI to understand and assess the breastfeeding swallow in the near future.

LEVEL OF EVIDENCE

4.

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.

Intermittently tagged real-time MRI reveals internal tongue motion during speech production

PURPOSE

To demonstrate a tagging method compatible with RT-MRI for the study of speech production.

METHODS

Tagging is applied as a brief interruption to a continuous real-time spiral acquisition. Tagging can be initiated manually by the operator, cued to the speech stimulus, or be automatically applied with a fixed frequency. We use a standard 2D 1-3-3-1 binomial SPAtial Modulation of Magnetization (SPAMM) sequence with 1 cm spacing in both in-plane directions. Tag persistence in tongue muscle is simulated and validated in vivo. The ability to capture internal tongue deformations is tested during speech production of American English diphthongs in native speakers.

RESULTS

We achieved an imaging window of 650-800 ms at 1.5T, with imaging signal to noise ratio ≥ 17 and tag contrast to noise ratio ≥ 5 in human tongue, providing 36 frames/s temporal resolution and 2 mm in-plane spatial resolution with real-time interactive acquisition and view-sharing reconstruction. The proposed method was able to capture tongue motion patterns and their relative timing with adequate spatiotemporal resolution during the production of American English diphthongs and consonants.

CONCLUSION

Intermittent tagging during real-time MRI of speech production is able to reveal the internal deformations of the tongue. This capability will allow new investigations of valuable spatiotemporal information on the biomechanics of the lingual subsystems during speech without reliance on binning speech utterance repetition.

Atlas-Based Tongue Muscle Correlation Analysis From Tagged and High-Resolution Magnetic Resonance Imaging

Purpose Intrinsic and extrinsic tongue muscles in healthy and diseased populations vary both in their intra- and intersubject behaviors during speech. Identifying coordination patterns among various tongue muscles can provide insights into speech motor control and help in developing new therapeutic and rehabilitative strategies. Method We present a method to analyze multisubject tongue muscle correlation using motion patterns in speech sound production. Motion of muscles is captured using tagged magnetic resonance imaging and computed using a phase-based deformation extraction algorithm. After being assembled in a common atlas space, motions from multiple subjects are extracted at each individual muscle location based on a manually labeled mask using high-resolution magnetic resonance imaging and a vocal tract atlas. Motion correlation between each muscle pair is computed within each labeled region. The analysis is performed on a population of 16 control subjects and 3 post-partial glossectomy patients. Results The floor-of-mouth (FOM) muscles show reduced correlation comparing to the internal tongue muscles. Patients present a higher amount of overall correlation between all muscles and exercise en bloc movements. Conclusions Correlation matrices in the atlas space show the coordination of tongue muscles in speech sound production. The FOM muscles are weakly correlated with the internal tongue muscles. Patients tend to use FOM muscles more than controls to compensate for their postsurgery function loss.

Sagittal Measurement of Tongue Movement During Respiration: Comparison Between Ultrasonography and Magnetic Resonance Imaging

The tongue makes up the anterior pharyngeal wall and is critical for airway patency. Magnetic resonance imaging (MRI) is commonly used to study pharyngeal muscle function in pharyngeal disorders such as obstructive sleep apnoea. Tagged MRI and ultrasound studies have separately revealed ∼1 mm of anterior tongue movement during inspiration in healthy patients, but these modalities have not been directly compared. In the study described here, agreement between ultrasound and MRI in measuring regional tongue displacement in 21 healthy patients and 21 patients with obstructive sleep apnoea was evaluated. We found good consistency and agreement between the two techniques, with an intra-class correlation coefficient of 0.79 (95% confidence interval: 0.75-0.82) for anteroposterior tongue motion during inspiration. Ultrasound measurements of posterior tongue displacement were 0.24 ± 0.64 mm greater than MRI measurements (95% limits of agreement: 1.03 to -1.49). This may reflect the higher spatial and temporal resolution of the ultrasound technique. This study confirms that ultrasound is a suitable method for quantifying inspiratory tongue movement.

A Sparse Non-Negative Matrix Factorization Framework for Identifying Functional Units of Tongue Behavior From MRI

Muscle coordination patterns of lingual behaviors are synergies generated by deforming local muscle groups in a variety of ways. Functional units are functional muscle groups of local structural elements within the tongue that compress, expand, and move in a cohesive and consistent manner. Identifying the functional units using tagged-magnetic resonance imaging (MRI) sheds light on the mechanisms of normal and pathological muscle coordination patterns, yielding improvement in surgical planning, treatment, or rehabilitation procedures. In this paper, to mine this information, we propose a matrix factorization and probabilistic graphical model framework to produce building blocks and their associated weighting map using motion quantities extracted from tagged-MRI. Our tagged-MRI imaging and accurate voxel-level tracking provide previously unavailable internal tongue motion patterns, thus revealing the inner workings of the tongue during speech or other lingual behaviors. We then employ spectral clustering on the weighting map to identify the cohesive regions defined by the tongue motion that may involve multiple or undocumented regions. To evaluate our method, we perform a series of experiments. We first use two-dimensional images and synthetic data to demonstrate the accuracy of our method. We then use three-dimensional synthetic and in vivo tongue motion data using protrusion and simple speech tasks to identify subject-specific and data-driven functional units of the tongue in localized regions.

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.

Magnetic resonance imaging based anatomical assessment of tongue impairment due to amyotrophic lateral sclerosis: A preliminary study

Amyotrophic Lateral Sclerosis (ALS) is a neurological disorder, which impairs tongue function for speech and swallowing. A widely used Diffusion Tensor Imaging (DTI) analysis pipeline is employed for quantifying differences in tongue fiber myoarchitecture between controls and ALS patients. This pipeline uses both high-resolution magnetic resonance imaging (hMRI) and DTI. hMRI is used to delineate tongue muscles, while DTI provides indices to reveal fiber connectivity within and between muscles. The preliminary results using five controls and two patients show quantitative differences between the groups. This work has the potential to provide insights into the detrimental effects of ALS on speech and swallowing.

Speech Map: A Statistical Multimodal Atlas of 4D Tongue Motion During Speech from Tagged and Cine MR Images

Quantitative measurement of functional and anatomical traits of 4D tongue motion in the course of speech or other lingual behaviors remains a major challenge in scientific research and clinical applications. Here, we introduce a statistical multimodal atlas of 4D tongue motion using healthy subjects, which enables a combined quantitative characterization of tongue motion in a reference anatomical configuration. This atlas framework, termed Speech Map, combines cine- and tagged-MRI in order to provide both the anatomic reference and motion information during speech. Our approach involves a series of steps including (1) construction of a common reference anatomical configuration from cine-MRI, (2) motion estimation from tagged-MRI, (3) transformation of the motion estimations to the reference anatomical configuration, and (4) computation of motion quantities such as Lagrangian strain. Using this framework, the anatomic configuration of the tongue appears motionless, while the motion fields and associated strain measurements change over the time course of speech. In addition, to form a succinct representation of the high-dimensional and complex motion fields, principal component analysis is carried out to characterize the central tendencies and variations of motion fields of our speech tasks. Our proposed method provides a platform to quantitatively and objectively explain the differences and variability of tongue motion by illuminating internal motion and strain that have so far been intractable. The findings are used to understand how tongue function for speech is limited by abnormal internal motion and strain in glossectomy patients.

Test-retest repeatability of human speech biomarkers from static and real-time dynamic magnetic resonance imaging

Static anatomical and real-time dynamic magnetic resonance imaging (RT-MRI) of the upper airway is a valuable method for studying speech production in research and clinical settings. The test-retest repeatability of quantitative imaging biomarkers is an important parameter, since it limits the effect sizes and intragroup differences that can be studied. Therefore, this study aims to present a framework for determining the test-retest repeatability of quantitative speech biomarkers from static MRI and RT-MRI, and apply the framework to healthy volunteers. Subjects (n = 8, 4 females, 4 males) are imaged in two scans on the same day, including static images and dynamic RT-MRI of speech tasks. The inter-study agreement is quantified using intraclass correlation coefficient (ICC) and mean within-subject standard deviation (σ_e). Inter-study agreement is strong to very strong for static measures (ICC: min/median/max 0.71/0.89/0.98, σ_e: 0.90/2.20/6.72 mm), poor to strong for dynamic RT-MRI measures of articulator motion range (ICC: 0.26/0.75/0.90, σ_e: 1.6/2.5/3.6 mm), and poor to very strong for velocities (ICC: 0.21/0.56/0.93, σ_e: 2.2/4.4/16.7 cm/s). In conclusion, this study characterizes repeatability of static and dynamic MRI-derived speech biomarkers using state-of-the-art imaging. The introduced framework can be used to guide future development of speech biomarkers. Test-retest MRI data are provided free for research use.

Variability in muscle activation of simple speech motions: A biomechanical modeling approach

Biomechanical models of the oropharynx facilitate the study of speech function by providing information that cannot be directly derived from imaging data, such as internal muscle forces and muscle activation patterns. Such models, when constructed and simulated based on anatomy and motion captured from individual speakers, enable the exploration of inter-subject variability of speech biomechanics. These models also allow one to answer questions, such as whether speakers produce similar sounds using essentially the same motor patterns with subtle differences, or vastly different motor equivalent patterns. Following this direction, this study uses speaker-specific modeling tools to investigate the muscle activation variability in two simple speech tasks that move the tongue forward (/ə-ɡis/) vs backward (/ə-suk/). Three dimensional tagged magnetic resonance imaging data were used to inversely drive the biomechanical models in four English speakers. Results show that the genioglossus is the workhorse muscle of the tongue, with activity levels of 10% in different subdivisions at different times. Jaw and hyoid positioners (inferior pterygoid and digastric) also show high activation during specific phonemes. Other muscles may be more involved in fine tuning the shapes. For example, slightly more activation of the anterior portion of the transverse is found during apical than laminal /s/, which would protrude the tongue tip to a greater extent for the apical /s/.

A Spatio-Temporal Atlas and Statistical Model of the Tongue During Speech from Cine-MRI

Statistical modeling of tongue motion during speech using cine magnetic resonance imaging (MRI) provides key information about the relationship between structure and motion of the tongue. In order to study the variability of tongue shape and motion in populations, a consistent integration and characterization of inter-subject variability is needed. In this paper, a method to construct a spatio-temporal atlas comprising a mean motion model and statistical modes of variation during speech is presented. The model is based on the cine-MRI from twenty two normal speakers and consists of several steps involving both spatial and temporal alignment problems independently. First, all images are registered into a common reference space, which is taken to be a neutral resting position of the tongue. Second, the tongue shapes of each individual relative to this reference space are produced. Third, a time warping approach (several are evaluated) is used to align the time frames of each subject to a common time series of initial mean images. Finally, the spatio-temporal atlas is created by time-warping each subject, generating new mean images at each time, and producing shape statistics around these mean images using principal component analysis at each reference time frame. Experimental results consist of comparison of various parameters and methods in creation of the atlas and a demonstration of the final modes of variations at various key time frames in a sample phrase.

Structure and variability in human tongue muscle anatomy

The human tongue has a complex architecture, consistent with its complex roles in eating, speaking and breathing. Tongue muscle architecture has been depicted in drawings and photographs, but not quantified volumetrically. This paper aims to fill that gap by measuring the muscle architecture of the tongue for 14 people captured in high-resolution 3D MRI volumes. The results show the structure, relationships and variability among the muscles, as well as the effects of age, gender and weight on muscle volume. Since the tongue consists of partially interdigitated muscles, we consider the muscle volumes in two ways. The functional muscle volume encompasses the region of the tongue served by the muscle. The structural volume halves the volume of the muscle in regions where it interdigitates with other muscles. Results show similarity of scaling across subjects, and speculate on functional effects of the anatomical structure.

Construction of An Unbiased Spatio-Temporal Atlas of the Tongue During Speech

Quantitative characterization and comparison of tongue motion during speech and swallowing present fundamental challenges because of striking variations in tongue structure and motion across subjects. A reliable and objective description of the dynamics tongue motion requires the consistent integration of inter-subject variability to detect the subtle changes in populations. To this end, in this work, we present an approach to constructing an unbiased spatio-temporal atlas of the tongue during speech for the first time, based on cine-MRI from twenty two normal subjects. First, we create a common spatial space using images from the reference time frame, a neutral position, in which the unbiased spatio-temporal atlas can be created. Second, we transport images from all time frames of all subjects into this common space via the single transformation. Third, we construct atlases for each time frame via groupwise diffeomorphic registration, which serves as the initial spatio-temporal atlas. Fourth, we update the spatio-temporal atlas by realigning each time sequence based on the Lipschitz norm on diffeomorphisms between each subject and the initial atlas. We evaluate and compare different configurations such as similarity measures to build the atlas. Our proposed method permits to accurately and objectively explain the main pattern of tongue surface motion.

Relating Speech Production to Tongue Muscle Compressions Using Tagged and High-resolution Magnetic Resonance Imaging

The human tongue is composed of multiple internal muscles that work collaboratively during the production of speech. Assessment of muscle mechanics can help understand the creation of tongue motion, interpret clinical observations, and predict surgical outcomes. Although various methods have been proposed for computing the tongue's motion, associating motion with muscle activity in an interdigitated fiber framework has not been studied. In this work, we aim to develop a method that reveals different tongue muscles' activities in different time phases during speech. We use four-dimensional tagged magnetic resonance (MR) images and static high-resolution MR images to obtain tongue motion and muscle anatomy, respectively. Then we compute strain tensors and local tissue compression along the muscle fiber directions in order to reveal their shortening pattern. This process relies on the support from multiple image analysis methods, including super-resolution volume reconstruction from MR image slices, segmentation of internal muscles, tracking the incompressible motion of tissue points using tagged images, propagation of muscle fiber directions over time, and calculation of strain in the line of action, etc. We evaluated the method on a control subject and two post-glossectomy patients in a controlled speech task. The normal subject's tongue muscle activity shows high correspondence with the production of speech in different time instants, while both patients' muscle activities show different patterns from the control due to their resected tongues. This method shows potential for relating overall tongue motion to particular muscle activity, which may provide novel information for future clinical and scientific studies.

Multimodal registration via mutual information incorporating geometric and spatial context

Multimodal image registration is a class of algorithms to find correspondence from different modalities. Since different modalities do not exhibit the same characteristics, finding accurate correspondence still remains a challenge. To deal with this, mutual information (MI)-based registration has been a preferred choice as MI is based on the statistical relationship between both volumes to be registered. However, MI has some limitations. First, MI-based registration often fails when there are local intensity variations in the volumes. Second, MI only considers the statistical intensity relationships between both volumes and ignores the spatial and geometric information about the voxel. In this work, we propose to address these limitations by incorporating spatial and geometric information via a 3D Harris operator. In particular, we focus on the registration between a high-resolution image and a low-resolution image. The MI cost function is computed in the regions where there are large spatial variations such as corner or edge. In addition, the MI cost function is augmented with geometric information derived from the 3D Harris operator applied to the high-resolution image. The robustness and accuracy of the proposed method were demonstrated using experiments on synthetic and clinical data including the brain and the tongue. The proposed method provided accurate registration and yielded better performance over standard registration methods.