A spatio-temporal atlas and statistical model of the tongue during speech from cine-MRI

Woo, Jonghye; Xing, Fangxu; Lee, Junghoon; Stone, Maureen; Prince, Jerry L.

doi:10.1080/21681163.2016.1169220

Cited by 15 publications

(20 citation statements)

References 32 publications

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“…The large number of publications in various bio-medical fields proves that statistical shape modeling is a versatile and widely accepted technique to capture anatomic variation in the population. For example, it is extensively used for organ segmentation, [36][37][38] to extract morphological bio-markers of (diseased) organs, [39][40][41] or for numerous orthopedic applications. [42][43][44][45][46][47] Although few, several studies-mainly in the field of orthopedics-attempted to relate SSM parameterized anatomical shape variation with Finite Element Analysis (FEA).…”

Section: Discussionmentioning

confidence: 99%

Combining statistical shape modeling, CFD, and meta‐modeling to approximate the patient‐specific pressure‐drop across the aortic valve in real‐time

Hoeijmakers

Waechter-Stehle

Weese

et al. 2020

Numer Methods Biomed Eng

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Background: Advances in medical imaging, segmentation techniques, and high performance computing have stimulated the use of complex, patient-specific, three-dimensional Computational Fluid Dynamics (CFD) simulations. Patient-specific, CFD-compatible geometries of the aortic valve are readily obtained. CFD can then be used to obtain the patient-specific pressure-flow relationship of the aortic valve. However, such CFD simulations are computationally expensive, and real-time alternatives are desired. Aim: The aim of this work is to evaluate the performance of a meta-model with respect to high-fidelity, three-dimensional CFD simulations of the aortic valve. Methods: Principal component analysis was used to build a statistical shape model (SSM) from a population of 74 iso-topological meshes of the aortic valve. Synthetic meshes were created with the SSM, and steady-state CFD simulations at flow-rates between 50 and 650 mL/s were performed to build a metamodel. The meta-model related the statistical shape variance, and flow-rate to the pressure-drop. Results: Even though the first three shape modes account for only 46% of shape variance, the features relevant for the pressure-drop seem to be captured. The three-mode shape-model approximates the pressure-drop with an average error of 8.8% to 10.6% for aortic valves with a geometric orifice area below 150 mm 2. The proposed methodology was least accurate for aortic valve areas above 150 mm 2. Further reduction to a meta-model introduces an additional 3% error.

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Section: Discussionmentioning

confidence: 99%

Combining statistical shape modeling, CFD, and meta‐modeling to approximate the patient‐specific pressure‐drop across the aortic valve in real‐time

Hoeijmakers

Waechter-Stehle

Weese

et al. 2020

Numer Methods Biomed Eng

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“…The results from all fourteen subjects are used to construct a 4D intensity atlas [5] . In this process, the deformations Ψ s,t from each individual subject s at time frame t to the atlas space at time frame t are obtained.…”

Section: Methodsmentioning

confidence: 99%

“…As a result, statistical properties of the entire motion field was not evaluated. On the other hand, there have been methods proposed to construct a 4D intensity atlas (scalar atlas) of the tongue from cine MR images [5] , from which a collected anatomical change can be statistically evaluated. However, an atlas of 4D motion fields (vector atlas) has never been proposed due to the complexity of motion estimation combined with high-dimensional atlas construction.…”

Section: Introductionmentioning

confidence: 99%

A four-dimensional motion field atlas of the tongue from tagged and cine magnetic resonance imaging

et al. 2017

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Representation of human tongue motion using three-dimensional vector fields over time can be used to better understand tongue function during speech, swallowing, and other lingual behaviors. To characterize the inter-subject variability of the tongue’s shape and motion of a population carrying out one of these functions it is desirable to build a statistical model of the four-dimensional (4D) tongue. In this paper, we propose a method to construct a spatio-temporal atlas of tongue motion using magnetic resonance (MR) images acquired from fourteen healthy human subjects. First, cine MR images revealing the anatomical features of the tongue are used to construct a 4D intensity image atlas. Second, tagged MR images acquired to capture internal motion are used to compute a dense motion field at each time frame using a phase-based motion tracking method. Third, motion fields from each subject are pulled back to the cine atlas space using the deformation fields computed during the cine atlas construction. Finally, a spatio-temporal motion field atlas is created to show a sequence of mean motion fields and their inter-subject variation. The quality of the atlas was evaluated by deforming cine images in the atlas space. Comparison between deformed and original cine images showed high correspondence. The proposed method provides a quantitative representation to observe the commonality and variability of the tongue motion field for the first time, and shows potential in evaluation of common properties such as strains and other tensors based on motion fields.

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“…Next, we address the spatial alignment issue between subjects. In previous work, we have reported a method to construct an intensity tongue atlas using cine MRI data (Woo, Xing, Lee, Stone, & Prince, 2018). We regard this cine atlas from the same subject group as pre-existing data and use it as the basis of the common space that we relocate the 4D motions in.…”

Section: D Motion Atlas Constructionmentioning

confidence: 99%

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

Xing

Stone

Goldsmith

et al. 2019

J Speech Lang Hear Res

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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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A spatio-temporal atlas and statistical model of the tongue during speech from cine-MRI

Cited by 15 publications

References 32 publications

Combining statistical shape modeling, CFD, and meta‐modeling to approximate the patient‐specific pressure‐drop across the aortic valve in real‐time

Combining statistical shape modeling, CFD, and meta‐modeling to approximate the patient‐specific pressure‐drop across the aortic valve in real‐time

A four-dimensional motion field atlas of the tongue from tagged and cine magnetic resonance imaging

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

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