Abstract #1877

Characterizing laryngeal dynamics during voicing and breathing with real-time multi-slice variational manifold learning

Rushdi Z. Rusho¹, Matthew R. Hoffman², Christopher S. Apfelbach³, Wahidul Alam¹, Hiroyuki Oya⁴, Matthew A. Howard⁴, David Meyer ⁵, Mathews Jacob⁶, and Sajan Goud Lingala^1,7

¹Roy J. Carver Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, United States, ²Department of Otolaryngology, The University of Iowa, Iowa City, IA, United States, ³Department of Communication Sciences and Disorders, The University of Iowa, Iowa City, IA, United States, ⁴Department of Neurosurgery, The University of Iowa, Iowa City, IA, United States, ⁵Janette Ogg Voice Research Center, Shenandoah University, Winchester, VA, United States, ⁶Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, United States, ⁷Department of Radiology, The University of Iowa, Iowa City, IA, United States

Synopsis

Keywords: Image Reconstruction, Image Reconstruction

Motivation: Visualizing the movement of laryngeal muscles during voicing and breathing is important for understanding the mechanics of speech production.

Goal(s): The goal is to improve our understanding of speech production by visualizing gross-vocal fold movements due to laryngeal muscle contractions.

Approach: We proposed a multi-slice non-gated spiral sparse sampling strategy, and variational manifold based reconstruction scheme for dynamic laryngeal MR imaging at high spatio-temporal resolution.

Results: Our proposed method can capture gross vocal fold motions (e.g., adduction, abduction, elongation, shortening) during various voicing and breathing at both high spatial (1.5 mm²) and high temporal resolutions (36 ms/frame).

Impact: Otolaryngologists, voice and neuroscientists interested in larynx physiology and disorders pertaining to breathing and phonation.

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