Purpose To develop and evaluate a MRI-based system for study of dynamic vocal tract shaping during speech production, which provides high spatial and temporal resolution. Methods The proposed system utilizes (a) custom eight-channel upper airway coils that have high sensitivity to upper airway regions of interest, (b) 2D golden angle spiral gradient echo acquisition, (c) on-the-fly view-sharing reconstruction, and (d) off-line temporal finite difference constrained reconstruction. The system also provides simultaneous noise-cancelled and temporally aligned audio. The system is evaluated in three healthy volunteers, and one tongue cancer patient, with a broad range of speech tasks. Results We report spatio-temporal resolutions of 2.4×2.4 mm2 every 12ms for single-slice imaging, and 2.4×2.4 mm2 every 36 ms for three-slice imaging, which reflects roughly 7-fold acceleration over Nyquist sampling. This system demonstrates improved temporal fidelity in capturing rapid vocal tract shaping for tasks such as producing consonant clusters in speech, and beat-boxing sounds. Novel acoustic-articulatory analysis was also demonstrated. Conclusions A synergistic combination of custom coils, spiral acquisitions, and constrained reconstruction enables visualization of rapid speech with high spatio-temporal resolution in multiple-planes.
USC-TIMIT is an extensive database of multimodal speech production data, developed to complement existing resources available to the speech research community and with the intention of being continuously refined and augmented. The database currently includes real-time magnetic resonance imaging data from five male and five female speakers of American English. Electromagnetic articulography data have also been presently collected from four of these speakers. The two modalities were recorded in two independent sessions while the subjects produced the same 460 sentence corpus used previously in the MOCHA-TIMIT database. In both cases the audio signal was recorded and synchronized with the articulatory data. The database and companion software are freely available to the research community.
Purpose To develop and evaluate a T1-weighted dynamic contrast enhanced (DCE) MRI methodology where tracer-kinetic (TK) parameter maps are directly estimated from undersampled (k,t)-space data. Methods The proposed reconstruction involves solving a non-linear least squares optimization problem that includes explicit use of a full forward model to convert parameter maps to (k,t)-space, utilizing the Patlak TK model. The proposed scheme is compared against an indirect method that creates intermediate images via parallel imaging and compressed sensing prior to TK modeling. Thirteen fully-sampled brain tumor DCE-MRI scans with 5 sec temporal resolution are retrospectively undersampled at rates R=20, 40, 60, 80, and 100 for each dynamic frame. TK maps are quantitatively compared based on root mean-squared-error (rMSE), and Bland-Altman analysis. The approach is also applied to four prospectively R=30 undersampled whole-brain DCE-MRI data sets. Results In the retrospective study, the proposed method performed statistically better than indirect method at R≥80 for all thirteen cases. This approach provided restoration of TK parameter values with less errors in tumor regions-of-interest, an improvement compared to a state-of-the-art indirect method. Applied prospectively, the proposed method provided whole-brain high-resolution TK maps with good image quality. Conclusion Model-based direct estimation of TK maps from k,t-space DCE-MRI data is feasible and is compatible up to 100-fold undersampling.
Purpose To develop and evaluate a technique for 3D dynamic MRI of the full vocal tract at high temporal resolution during natural speech. Methods We demonstrate 2.4 × 2.4 × 5.8 mm3 spatial resolution, 61‐ms temporal resolution, and a 200 × 200 × 70 mm3 FOV. The proposed method uses 3D gradient‐echo imaging with a custom upper‐airway coil, a minimum‐phase slab excitation, stack‐of‐spirals readout, pseudo golden‐angle view order in kx‐ky, linear Cartesian order along kz, and spatiotemporal finite difference constrained reconstruction, with 13‐fold acceleration. This technique is evaluated using in vivo vocal tract airway data from 2 healthy subjects acquired at 1.5T scanner, 1 with synchronized audio, with 2 tasks during production of natural speech, and via comparison with interleaved multislice 2D dynamic MRI. Results This technique captured known dynamics of vocal tract articulators during natural speech tasks including tongue gestures during the production of consonants “s” and “l” and of consonant–vowel syllables, and was additionally consistent with 2D dynamic MRI. Coordination of lingual (tongue) movements for consonants is demonstrated via volume‐of‐interest analysis. Vocal tract area function dynamics revealed critical lingual constriction events along the length of the vocal tract for consonants and vowels. Conclusion We demonstrate feasibility of 3D dynamic MRI of the full vocal tract, with spatiotemporal resolution adequate to visualize lingual movements for consonants and vocal tact shaping during natural productions of consonant–vowel syllables, without requiring multiple repetitions.
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