ArticleMagnetic resonance in medicine2019
Intermittently tagged real-time MRI reveals internal tongue motion during speech production.
Article in Magnetic resonance in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Feasibility of tagged MRI at 0.55 T.Magnetic resonance in medicine · 2026Article
- Real-Time Magnetic Resonance Imaging.Journal of magnetic resonance imaging : JMRI · 2022Review
- Aliasing artifact reduction in spiral real-time MRI.Magnetic resonance in medicine · 2021Article
- Improved real-time tagged MRI using REALTAG.Magnetic resonance in medicine · 2020Article
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Authors and funding
4 authors.
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Abstract
purposeTo demonstrate a tagging method compatible with RT-MRI for the study of speech production.
methodsTagging 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.
resultsWe 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.
conclusionIntermittent 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.
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