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Intermittently tagged real‐time MRI reveals internal tongue motion during speech production
Author(s) -
Chen Weiyi,
Byrd Dani,
Narayanan Shrikanth,
Nayak Krishna S.
Publication year - 2019
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27745
Subject(s) - computer science , speech production , diphthong , temporal resolution , speech recognition , cued speech , speech error , tongue , artificial intelligence , computer vision , vowel , physics , psychology , linguistics , philosophy , quantum mechanics , cognitive psychology
Purpose To demonstrate a tagging method compatible with RT‐MRI for the study of speech production. Methods Tagging 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. Results We 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. Conclusion Intermittent 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.