With the proposed methods, both compliance and Pclose can be calculated and used to quantify airway collapsibility in OSA with an awake scan of 30 min total scan room time. J. Magn. Reson. Imaging 2016;44:158-167.
Real-time magnetic resonance imaging (RT-MRI) of human speech production is enabling significant advances in speech science, linguistics, bio-inspired speech technology development, and clinical applications. Easy access to RT-MRI is however limited, and comprehensive datasets with broad access are needed to catalyze research across numerous domains. The imaging of the rapidly moving articulators and dynamic airway shaping during speech demands high spatio-temporal resolution and robust reconstruction methods. Further, while reconstructed images have been published, to-date there is no open dataset providing raw multi-coil RT-MRI data from an optimized speech production experimental setup. Such datasets could enable new and improved methods for dynamic image reconstruction, artifact correction, feature extraction, and direct extraction of linguistically-relevant biomarkers. The present dataset offers a unique corpus of 2D sagittal-view RT-MRI videos along with synchronized audio for 75 participants performing linguistically motivated speech tasks, alongside the corresponding public domain raw RT-MRI data. The dataset also includes 3D volumetric vocal tract MRI during sustained speech sounds and high-resolution static anatomical T2-weighted upper airway MRI for each participant.
ObjectiveTo develop and evaluate a framework for simulating low-field proton-density weighted MRI acquisitions based on high-field acquisitions, which could be used to predict the minimum B0 field strength requirements for MRI techniques. This framework would be particularly useful in the evaluation of de-noising and constrained reconstruction techniques.Materials and MethodsGiven MRI raw data, lower field MRI acquisitions can be simulated based on the signal and noise scaling with field strength. Certain assumptions are imposed for the simulation and their validity is discussed. A validation experiment was performed using a standard resolution phantom imaged at 0.35 T, 1.5 T, 3 T, and 7 T. This framework was then applied to two sample proton-density weighted MRI applications that demonstrated estimation of minimum field strength requirements: real-time upper airway imaging and liver proton-density fat fraction measurement.ResultsThe phantom experiment showed good agreement between simulated and measured images. The SNR difference between simulated and measured was ≤ 8% for the 1.5T, 3T, and 7T cases which utilized scanners with the same geometry and from the same vendor. The measured SNR at 0.35T was 1.8- to 2.5-fold less than predicted likely due to unaccounted differences in the RF receive chain. The predicted minimum field strength requirements for the two sample applications were 0.2 T and 0.3 T, respectively.ConclusionsUnder certain assumptions, low-field MRI acquisitions can be simulated from high-field MRI data. This enables prediction of the minimum field strength requirements for a broad range of MRI techniques.
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.
Purpose To determine if real-time MRI (RT-MRI) method during continuous positive airway pressure (CPAP) can be used to measure neuromuscular reflex and/or passive collapsibility of the upper airway in individual obstructive sleep apnea (OSA) subjects. Materials and Methods We conducted experiments on 4 adolescents with OSA and 3 healthy controls, during natural sleep and during wakefulness. Data were acquired on a clinical 3T scanner using simultaneous multi-slice (SMS) RT-MRI during CPAP. CPAP pressure level was alternated between therapeutic and sub-therapeutic levels. Segmented airway area changes in response to rapid CPAP pressure drop and restoration were used to estimate 1) upper airway loop gain (UALG) and 2) anatomical risk factors, including fluctuation of airway area (FAA). Results FAA significantly differed between OSA patients (2–4x larger) and healthy controls (Student’s t-test, P<0.05). UALG and FAA measurements indicate that neuromuscular reflex and passive collapsibility varied among the OSA patients, suggesting the presence of different OSA phenotypes. Measurements had high intra-subject reproducibility (intra-class correlation coefficient r > 0.7). Conclusion SMS RT-MRI during CPAP can reproducibly identify physiological traits and anatomical risk factors that are valuable in the assessment of OSA. This technique can potentially locate the most collapsible airway sites. Both UALG and FAA possess large variation among OSA patients.
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