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Magnetic Resonance Imaging01:24

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Related Experiment Video

Updated: May 22, 2026

fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals
11:15

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Published on: May 23, 2017

Synthesizing vocal tract magnetic resonance imaging sequences with phoneme-aware diffusion models.

Paula Andrea Perez-Toro1,2, Tomas Arias-Vergara1,2, Lukas Buess1

  • 1Friedrich-Alexander-Universität Erlangen-Nürnberg, Pattern Recognition Lab, Erlangen, Germany.

Journal of Medical Imaging (Bellingham, Wash.)
|May 21, 2026
PubMed
Summary

This study introduces a diffusion-based framework for generating vocal tract MRI from speech, improving phoneme-level accuracy. The novel Speech-to-Image Phonemic Fidelity Score (SIPFS) enhances articulatory precision for speech science and clinical applications.

Keywords:
diffusion modelsgenerative AImedical video synthesisreal time MRIvocal tract

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Area of Science:

  • Medical Imaging
  • Speech Science
  • Artificial Intelligence

Background:

  • Real-time speech Magnetic Resonance Imaging (MRI) provides crucial data for speech diagnostics, therapy, and scientific research.
  • Directly mapping speech to MRI remains a significant technical challenge, limiting current applications.

Purpose of the Study:

  • To develop a diffusion-based framework for synthesizing dynamic vocal tract MRI sequences directly from spoken language.
  • To enhance phoneme-level fidelity in speech-to-MRI mapping.
  • To introduce a new metric, the Speech-to-Image Phonemic Fidelity Score (SIPFS), for assessing articulatory precision.

Main Methods:

  • Trained audio-conditioned diffusion models with phonemic-aware attention and contrastive learning on a large-scale speech MRI database.
  • Utilized pre-trained audio encoders (Wav2Vec, WavLM, Whisper) for speech embedding.
  • Evaluated performance using standard image/video metrics (FID, FVD, SSIM, PSNR) and the novel SIPFS on unseen speakers and speech segments.

Main Results:

  • Phonemic-aware attention significantly improved image quality and phoneme-level precision.
  • The proposed framework demonstrated robust generalization to new speakers and speech segments.
  • SIPFS indicated up to 15% higher phonemic accuracy compared to unconditioned models, validating the approach.

Conclusions:

  • The developed framework successfully synthesizes accurate, phoneme-sensitive vocal tract MRI from speech using diffusion models.
  • This advancement supports clinical applications in speech diagnostics, therapy, and articulatory research.
  • Future work aims to expand datasets, improve computational efficiency, and refine evaluation metrics for broader clinical integration.