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Accelerated three-dimensional upper airway MRI using compressed sensing.

Yoon-Chul Kim1, Shrikanth S Narayanan, Krishna S Nayak

  • 1Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, California 90089-2564, USA. yoonckim@usc.edu

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|April 9, 2009
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Summary
This summary is machine-generated.

Compressed sensing (CS) with phase constraints improves high-resolution 3D MRI of the vocal tract during speech. This technique enables detailed imaging of the upper airway for speech production research.

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Area of Science:

  • Medical Imaging
  • Speech Science
  • Biomedical Engineering

Background:

  • Three-dimensional (3D) MRI is crucial for understanding vocal tract shaping in speech production.
  • Improving the resolution of MRI data is essential for detailed articulatory modeling.
  • Current MRI techniques face limitations in capturing dynamic speech movements with high fidelity.

Purpose of the Study:

  • To apply compressed sensing (CS) for high-resolution 3D upper airway MRI.
  • To investigate the benefits of incorporating image phase constraints into CS reconstruction.
  • To enable dynamic 3D MRI during sustained sound production.

Main Methods:

  • Utilized spatial finite difference as the sparsifying transform for CS.
  • Incorporated image phase estimates to enhance solution sparsity.
  • Conducted retrospective subsampling experiments to determine optimal acceleration factors.
  • Performed prospective 5x undersampled acquisition with phase-constrained CS reconstruction.

Main Results:

  • Phase constraints improved image quality, allowing higher acceleration factors (5x vs. 4x).
  • Achieved 1.5 x 1.5 x 2.0 mm³ spatial resolution during sustained speech.
  • Successfully acquired 3D vocal tract MRI data within a 7-second scan time for consonants /s/, /ʃ/, /l/, and /r/.

Conclusions:

  • Compressed sensing with phase constraints significantly enhances high-resolution 3D vocal tract MRI.
  • This method allows for detailed imaging of articulatory dynamics during speech production.
  • The technique provides valuable data for speech production modeling and research.