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Related Experiment Video

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High-frame-rate full-vocal-tract 3D dynamic speech imaging.

Maojing Fu1,2, Marissa S Barlaz3, Joseph L Holtrop2,4

  • 1Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.

Magnetic Resonance in Medicine
|April 22, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new 3D dynamic speech MRI method using sparse sampling and low-rank modeling. It achieves high frame rates and resolution for full vocal tract imaging, aiding phonetic research.

Keywords:
cone navigationdynamic speech imaginglow-rank approximationpartial separabilitysparsity

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Speech Science

Background:

  • Accurate 3D dynamic speech MRI requires high temporal and spatial resolution.
  • Current methods face limitations in achieving full vocal tract coverage at high frame rates.

Purpose of the Study:

  • To develop a 3D dynamic speech MRI technique with high temporal frame rate, spatial resolution, and full vocal tract coverage.
  • To utilize low-rank modeling and sparse sampling for accelerated data acquisition and reconstruction.

Main Methods:

  • Implemented a novel data acquisition strategy with self-navigated sparse sampling for accelerated data collection.
  • Employed an image reconstruction method enforcing joint low-rank and spatiotemporal total variation constraints on sparse (k,t)-space data.
  • Integrated these methods with the partial separability model for 3D dynamic speech MRI.

Main Results:

  • Achieved a nominal temporal frame rate of 166 frames per second with a repetition time of 5.99 ms.
  • Obtained a spatial resolution of 2.2 × 2.2 × 5.0 mm³ for an imaging volume covering the entire vocal tract.
  • Demonstrated practical utility through validation experiments and a phonetics investigation.

Conclusions:

  • 3D dynamic speech imaging with full vocal tract coverage, high spatial resolution, and high nominal frame rate is feasible.
  • The developed method provides valuable dynamic speech data for phonetic studies.
  • This technique advances the capabilities of MRI for speech research.