Multimodal MRI Research


The goals of this research are to develop a variety of image and motion analysis techniques to better understand the relationship between anatomy and function of the tongue during speech. The tongue plays an important role in eating, speaking, and breathing. Its function is compromised by disease and also by treatment, such as surgery or radiation for tongue cancer, aphasia caused by stroke or Alzheimer's disease, or speech motor impairments due to amyotrophic lateral sclerosis. A key to understanding the relationship between the structure and function of the tongue is to analyze motion changes in localized tongue regions and map them to anatomy.

The use of three types of magnetic resonance (MR) images (i.e., high-resolution, cine, and tag) offers new and enhanced capabilities for exploring normal and diseased tongue behavior, advancing our knowledge on a wide variety of speech-related disorders. Quantitative measures derived from MR images, such as tissue compression, expansion, principle strain, and muscle mechanics play a crucial role to characterize normal and abnormal motion of the tongue. A powerful way to measure changes and compromises in tongue structure and function is via a 4D statistical atlas and associated image analysis techniques. Atlases integrate diverse imaging information for individuals and groups, by correlating images with quantitative measurements, and constructing diagnostic tools.

multimodeMRI



 

Cine MR images from a speech task

Recent publications

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

This work is funded by the funded by the National Institute of Health

Introduction

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

Correlation pattern of 10 tongue muscles from 16 healthy controls in different time periods pronouncing “a souk.”

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.

Xing, F., Stone, M., Goldsmith, T., Prince, J. L., El Fakhri, G., & Woo, J. (2019). Atlas-Based Tongue Muscle Correlation Analysis From Tagged and High-Resolution Magnetic Resonance Imaging. Journal of Speech, Language, and Hearing Research, 1-12.


Differentiating post-cancer from healthy tongue muscle coordination patterns during speech using deep learning

This work is funded by the funded by the National Institute of Health

 Illustration of the motion fields (first row), three clusters of functional units (second row), and associated weighting maps (third row) 

The ability to differentiate post-cancer from healthy tongue muscle coordination patterns is necessary for the advancement of speech motor control theories and for the development of therapeutic and rehabilitative strategies. A deep learning approach is presented to classify two groups using muscle coordination patterns from magnetic resonance imaging (MRI). The proposed method uses tagged-MRI to track the tongue's internal tissue points and atlas-driven non-negative matrix factorization to reduce the dimensionality of the deformation fields. A convolutional neural network is applied to the classification task yielding an accuracy of 96.90%, offering the potential to the development of therapeutic or rehabilitative strategies in speech-related disorders.

Woo, J., Xing, F., Prince, J. L., Stone, M., Green, J. R., Goldsmith, T., ... & El Fakhri, G. (2019). Differentiating post-cancer from healthy tongue muscle coordination patterns during speech using deep learning. The Journal of the Acoustical Society of America, 145(5), EL423-EL429.


Magnetic resonance imaging based anatomical assessment of tongue impairment due to amyotrophic lateral sclerosis

This work is funded by the funded by the National Institute of Health

Qualitative comparison between a normal control and an ALS patient. The first and second row show mid-sagittal views of hMRI, the tractography of the GG, the whole tongue in the b = 0 space, FA, and MD of a normal control and a patient, respectively

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.

Lee, E., Xing, F., Ahn, S., Reese, T. G., Wang, R., Green, J. R., ... & Woo, J. (2018). Magnetic resonance imaging based anatomical assessment of tongue impairment due to amyotrophic lateral sclerosis: A preliminary study. The Journal of the Acoustical Society of America, 143(4), EL248-EL254.


Phase Vector Incompressible Registration Algorithm for Motion Estimation From Tagged Magnetic Resonance Images

This work is funded by the funded by the National Institute of Health

Tagged magnetic resonance imaging has been used for decades to observe and quantify motion and strain of deforming tissue. It is challenging to obtain 3-D motion estimates due to a tradeoff between image slice density and acquisition time. Typically, interpolation methods are used either to combine 2-D motion extracted from sparse slice acquisitions into 3-D motion or to construct a dense volume from sparse acquisitions before image registration methods are applied. This paper proposes a new phase-based 3-D motion estimation technique that first computes harmonic phase volumes from interpolated tagged slices and then matches them using an image registration framework. The approach uses several concepts from diffeomorphic image registration with a key novelty that defines a symmetric similarity metric on harmonic phase volumes from multiple orientations. The material property of harmonic phase solves the aperture problem of optical flow and intensity-based methods and is robust to tag fading. A harmonic magnitude volume is used in enforcing incompressibility in the tissue regions. The estimated motion fields are dense, incompressible, diffeomorphic, and inverse-consistent at a 3-D voxel level. The method was evaluated using simulated phantoms, human brain data in mild head accelerations, human tongue data during speech, and an open cardiac data set. The method shows comparable accuracy to three existing methods while demonstrating low computation time and robustness to tag fading and noise.

Xing, F., Woo, J., Gomez, A. D., Pham, D. L., Bayly, P. V., Stone, M., & Prince, J. L. (2017). Phase vector incompressible registration algorithm for motion estimation from tagged magnetic resonance images. IEEE transactions on medical imaging, 36(10), 2116-2128.