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Abstract PurposeTo demonstrate speech‐production real‐time MRI (RT‐MRI) using a contemporary 0.55T system, and to identify opportunities for improved performance compared with conventional field strengths. MethodsExperiments were performed on healthy adult volunteers using a 0.55T MRI system with high‐performance gradients and a custom 8‐channel upper airway coil. Imaging was performed using spiral‐based balancedSSFPand gradient‐recalled echo (GRE) pulse sequences using a temporal finite‐difference constrained reconstruction. Speech‐production RT‐MRI was performed with three spiral readout durations (8.90, 5.58, and 3.48 ms) to determine trade‐offs with respect to articulator contrast, blurring, banding artifacts, and overall image quality. ResultsBoth spiral GRE and bSSFP captured tongue boundary dynamics during rapid consonant‐vowel syllables. Although bSSFP provided substantially higher SNR in all vocal tract articulators than GRE, it suffered from banding artifacts at TR > 10.9 ms. Spiral bSSFP with the shortest readout duration (3.48 ms, TR = 5.30 ms) had the best image quality, with a 1.54‐times boost in SNR compared with an equivalent GRE sequence. Longer readout durations led to increased SNR efficiency and blurring in both bSSFP and GRE. ConclusionHigh‐performance 0.55T MRI systems can be used for speech‐production RT‐MRI. Spiral bSSFP can be used without suffering from banding artifacts in vocal tract articulators, provide better SNR efficiency, and have better image quality than what is typically achieved at 1.5 T or 3 T.
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Localizing Concurrent Sound Sources with Binaural Microphones: A Simulation Study
The human auditory system can localize multiple sound sources using time, intensity, and frequency cues in the sound received by the two ears. Being able to spatially segregate the sources helps perception in a challenging condition when multiple sounds coexist. This study used model simulations to explore an algorithm for localizing multiple sources in azimuth with binaural (i.e., two) microphones. The algorithm relies on the “sparseness” property of daily signals in the time-frequency domain, and sound coming from different locations carrying unique spatial features will form clusters. Based on an interaural normalization procedure, the model generated spiral patterns for sound sources in the frontal hemifield. The model itself was created using broadband noise for better accuracy, because speech typically has sporadic energy at high frequencies. The model at an arbitrary frequency can be used to predict locations of speech and music that occurred alone or concurrently, and a classification algorithm was applied to measure the localization error. Under anechoic conditions, averaged errors in azimuth increased from 4.5° to 19° with RMS errors ranging from 6.4° to 26.7° as model frequency increased from 300 to 3000 Hz. The low-frequency model performance using short speech sound was notably better than the generalized cross-correlation model. Two types of room reverberations were then introduced to simulate difficult listening conditions. Model performance under reverberation was more resilient at low frequencies than at high frequencies. Overall, our study presented a spiral model for rapidly predicting horizontal locations of concurrent sound that is suitable for real-world scenarios.
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- Award ID(s):
- 2217032
- PAR ID:
- 10463906
- Editor(s):
- Canlon Barbara
- Date Published:
- Journal Name:
- Hearing research
- ISSN:
- 0378-5955
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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