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Xing F, Stone M, Goldsmith T, Prince JL, El Fakhri G, Woo J. Atlas-Based Tongue Muscle Correlation Analysis From Tagged and High-Resolution Magnetic Resonance Imaging. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2019; 62:2258-2269. [PMID: 31265364 PMCID: PMC6808360 DOI: 10.1044/2019_jslhr-s-18-0495] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 03/25/2019] [Accepted: 05/10/2019] [Indexed: 06/09/2023]
Abstract
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.
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Affiliation(s)
- Fangxu Xing
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston
| | - Maureen Stone
- Department of Neural and Pain Sciences, University of Maryland Dental School, Baltimore
| | - Tessa Goldsmith
- Department of Speech, Language and Swallowing, Massachusetts General Hospital, Boston
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston
| | - Jonghye Woo
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston
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Woo J, Prince JL, Stone M, Xing F, Gomez AD, Green JR, Hartnick CJ, Brady TJ, Reese TG, Wedeen VJ, El Fakhri G. A Sparse Non-Negative Matrix Factorization Framework for Identifying Functional Units of Tongue Behavior From MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:730-740. [PMID: 30235120 PMCID: PMC6422735 DOI: 10.1109/tmi.2018.2870939] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
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.
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Affiliation(s)
- Jonghye Woo
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering at Johns Hopkins University
| | | | - Fangxu Xing
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School
| | - Arnold D. Gomez
- Department of Electrical and Computer Engineering at Johns Hopkins University
| | | | | | - Thomas J. Brady
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School
| | - Timothy G. Reese
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School
| | - Van J. Wedeen
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School
| | - Georges El Fakhri
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School
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Gomez AD, Knutsen AK, Xing F, Lu YC, Chan D, Pham DL, Bayly P, Prince JL. 3-D Measurements of Acceleration-Induced Brain Deformation via Harmonic Phase Analysis and Finite-Element Models. IEEE Trans Biomed Eng 2018; 66:1456-1467. [PMID: 30296208 DOI: 10.1109/tbme.2018.2874591] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
OBJECTIVE To obtain dense spatiotemporal measurements of brain deformation from two distinct but complementary head motion experiments: linear and rotational accelerations. METHODS This study introduces a strategy for integrating harmonic phase analysis of tagged magnetic resonance imaging (MRI) and finite-element models to extract mechanically representative deformation measurements. The method was calibrated using simulated as well as experimental data, demonstrated in a phantom including data with image artifacts, and used to measure brain deformation in human volunteers undergoing rotational and linear acceleration. RESULTS Evaluation methods yielded a displacement error of 1.1 mm compared to human observers and strain errors between [Formula: see text] for linear acceleration and [Formula: see text] for rotational acceleration. This study also demonstrates an approach that can reduce error by 86% in the presence of corrupted data. Analysis of results shows consistency with 2-D motion estimation, agreement with external sensors, and the expected physical behavior of the brain. CONCLUSION Mechanical regularization is useful for obtaining dense spatiotemporal measurements of in vivo brain deformation under different loading regimes. SIGNIFICANCE The measurements suggest that the brain's 3-D response to mild accelerations includes distinct patterns observable using practical MRI resolutions. This type of measurement can provide validation data for computer models for the study of traumatic brain injury.
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Tolpadi AA, Stone ML, Carass A, Prince JL, Gomez AD. Inverse Biomechanical Modeling of the Tongue via Machine Learning and Synthetic Training Data. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2018; 10576. [PMID: 29997406 DOI: 10.1117/12.2296927] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
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.
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Affiliation(s)
- Aniket A Tolpadi
- Department of Bioengineering, Rice University, Houston, TX, US 77005
| | - Maureen L Stone
- Department of Neural and Pain Sciences, Dept of Orthodontics, University of Maryland Dental School, Baltimore, MD, US 21201
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, US 21218
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, US 21218
| | - Arnold D Gomez
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, US 21218
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Xing F, Prince JL, Stone M, Reese TG, Atassi N, Wedeen VJ, El Fakhri G, Woo J. Strain Map of the Tongue in Normal and ALS Speech Patterns from Tagged and Diffusion MRI. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2018; 10574:1057411. [PMID: 29706684 PMCID: PMC5922778 DOI: 10.1117/12.2293028] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Amyotrophic Lateral Sclerosis (ALS) is a neurological disease that causes death of neurons controlling muscle movements. Loss of speech and swallowing functions is a major impact due to degeneration of the tongue muscles. In speech studies using magnetic resonance (MR) techniques, diffusion tensor imaging (DTI) is used to capture internal tongue muscle fiber structures in three-dimensions (3D) in a non-invasive manner. Tagged magnetic resonance images (tMRI) are used to record tongue motion during speech. In this work, we aim to combine information obtained with both MR imaging techniques to compare the functionality characteristics of the tongue between normal and ALS subjects. We first extracted 3D motion of the tongue using tMRI from fourteen normal subjects in speech. The estimated motion sequences were then warped using diffeomorphic registration into the b0 spaces of the DTI data of two normal subjects and an ALS patient. We then constructed motion atlases by averaging all warped motion fields in each b0 space, and computed strain in the line of action along the muscle fiber directions provided by tractography. Strain in line with the fiber directions provides a quantitative map of the potential active region of the tongue during speech. Comparison between normal and ALS subjects explores the changing volume of compressing tongue tissues in speech facing the situation of muscle degradation. The proposed framework provides for the first time a dynamic map of contracting fibers in ALS speech patterns, and has the potential to provide more insight into the detrimental effects of ALS on speech.
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Affiliation(s)
- Fangxu Xing
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, US 02114
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, US 21218
| | - Maureen Stone
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, MD, US 21201
| | - Timothy G. Reese
- Department of Radiology, Massachusetts General Hospital, Boston, MA, US 02114
| | - Nazem Atassi
- Department of Neurology, Massachusetts General Hospital, Boston, MA, US 02114
| | - Van J. Wedeen
- Department of Radiology, Massachusetts General Hospital, Boston, MA, US 02114
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, US 02114
| | - Jonghye Woo
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, US 02114
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Woo J, Xing F, Stone M, Green J, Reese TG, Brady TJ, Wedeen VJ, Prince JL, El Fakhri G. Speech Map: A Statistical Multimodal Atlas of 4D Tongue Motion During Speech from Tagged and Cine MR Images. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2017; 7:361-373. [PMID: 31328049 DOI: 10.1080/21681163.2017.1382393] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
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.
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Affiliation(s)
- Jonghye Woo
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Fangxu Xing
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Maureen Stone
- Department of Neural and Pain Sciences, University of Maryland Dental School, Baltimore, MD 21201, USA
| | - Jordan Green
- Department of Communication Sciences and Disorders, MGH Institute of Health Professions, Boston, MA 02129, USA
| | - Timothy G Reese
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
| | - Thomas J Brady
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Van J Wedeen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
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M Harandi N, Woo J, Stone M, Abugharbieh R, Fels S. Variability in muscle activation of simple speech motions: A biomechanical modeling approach. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2017; 141:2579. [PMID: 28464688 PMCID: PMC6909993 DOI: 10.1121/1.4978420] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Revised: 01/31/2017] [Accepted: 02/27/2017] [Indexed: 06/07/2023]
Abstract
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/.
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Affiliation(s)
- Negar M Harandi
- Electrical and Computer Engineering Department, University of British Columbia, Vancouver, British Columbia, Canada
| | - Jonghye Woo
- Department of Radiology, Harvard Medical School/MGH, Boston, Massachusetts 02114, USA
| | - Maureen Stone
- University of Maryland Dental School, Baltimore, Maryland 21201, USA
| | - Rafeef Abugharbieh
- Electrical and Computer Engineering Department, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sidney Fels
- Electrical and Computer Engineering Department, University of British Columbia, Vancouver, British Columbia, Canada
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