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, 141 (5), 3323

Test-retest Repeatability of Human Speech Biomarkers From Static and Real-Time Dynamic Magnetic Resonance Imaging

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Test-retest Repeatability of Human Speech Biomarkers From Static and Real-Time Dynamic Magnetic Resonance Imaging

Johannes Töger et al. J Acoust Soc Am.

Abstract

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 (σe). Inter-study agreement is strong to very strong for static measures (ICC: min/median/max 0.71/0.89/0.98, σe: 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, σe: 1.6/2.5/3.6 mm), and poor to very strong for velocities (ICC: 0.21/0.56/0.93, σe: 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.

Figures

FIG. 1.
FIG. 1.
Potential MRI operator variability. Padding (gray) is used to ensure that the subject's head is stationary. The padding is completely removed between each scan. Panel (a) shows how the patient may be positioned differently, leading to different angles between the neck and head, potentially influencing speech anatomy and dynamic measures. Panel (b) shows how the imaging slice, ideally located in the midsagittal plane of the subject's head, may vary in the coronal view. Panel (c) shows the positioning of the upper airway coils and how the imaging slice may vary in the transversal view.
FIG. 2.
FIG. 2.
Definition of static anatomical landmarks. Panel (a) shows a high-resolution T2-weighted image in the midsagittal plane. The landmarks A–I are placed manually to obtain midsagittal measures of the vocal tract according to a previous study (Ref. 18) (see text for details). Furthermore, the Gn is annotated manually as the most inferior–anterior point of the mandible. Panels (b) and (c) show how oblique slices through each side of mandible are prescribed and reconstructed. Two landmarks are placed on each side as previously described (Ref. 27): the gonion (GoLt and GoRt for the left and right gonion, respectively), and the superior aspect of the condylar process (CdSuLt and CdSuRt, respectively). See text for details.
FIG. 3.
FIG. 3.
(Color online) Quantitative dynamic RT-MRI measures using the grid-based method. Panel (a) shows the manual input to the method. First, an approximate vocal tract centerline is drawn (yellow line). Three landmarks are positioned (dots) at (1) the lowest point of the upper lip, (2) the top of the palate, and (3) at the pharyngeal wall at the top of the larynx. Panel (b) shows the constructed analysis gridlines. Panel (c) shows the automatic airway boundary segmentation. Panel (d) shows gridlines chosen for further analysis of distance and velocity located (1) between the lips, (2) between the tongue tip and alveolar ridge, (3) between the tongue and top of the palate, and (4) between the tongue and pharyngeal wall. Panel (e) shows quantitative measures, in this example for the utterance aa-ii-aa at location 4 (pharyngeal wall—tongue). Velocity (V) is measured by manually selecting a time interval and fitting a straight line to the data using linear regression. Range (R) is quantified by manually selecting the time interval of interest and then taking the difference between the 10th and 90th percentile of distance values in that interval.
FIG. 4.
FIG. 4.
(Color online) Quantitative dynamic RT-MRI measures using the region-based segmentation method. Panel (a) shows an RT-MRI frame. Panel (b) shows a template, manually specified for each subject. Panel (c) shows the resulting automatic segmentation. Panel (d) shows where search regions for constriction search are manually located. The lip constriction degree is computed as the minimum distance between the upper and lower lip contours (1). Analogous measurements are made for the tongue-alveolar ridge (2), tongue-palate (3), tongue-velum (4), tongue-pharynx (5), and velum-pharynx constrictions (6). Panel (e) shows a visualization of constriction measurements (white lines). Finally, panel (f) shows how quantitative measures were computed (aa-ii-aa, tongue-pharynx). Velocity (V) is measured by manually selecting the time interval of interest and fitting a straight line to the data using linear regression. Range (R) is quantified by manually selecting the time interval of interest and taking the difference between the 10th and 90th percentile of distance values in that interval.
FIG. 5.
FIG. 5.
Test–retest repeatability of static anatomical measures for a subset of biomarkers. Full results are shown in Table V and supplemental material (Ref. 77). See Fig. 2 for definition of measures.
FIG. 6.
FIG. 6.
Test–retest repeatability of dynamic measures using the grid-based segmentation method for a subset of biomarkers (Ref. 40). Full results are given in Table VI and supplemental material (Ref. 77).
FIG. 7.
FIG. 7.
Test–retest repeatability of dynamic measures using the region-based segmentation method for a subset of biomarkers (Ref. 42). Full results are given in Table VI and supplemental material (Ref. 77).

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