Literature DB >> 24912452

High-resolution dynamic speech imaging with joint low-rank and sparsity constraints.

Maojing Fu1, Bo Zhao, Christopher Carignan, Ryan K Shosted, Jamie L Perry, David P Kuehn, Zhi-Pei Liang, Bradley P Sutton.   

Abstract

PURPOSE: To enable dynamic speech imaging with high spatiotemporal resolution and full-vocal-tract spatial coverage, leveraging recent advances in sparse sampling.
METHODS: An imaging method is developed to enable high-speed dynamic speech imaging exploiting low-rank and sparsity of the dynamic images of articulatory motion during speech. The proposed method includes: (a) a novel data acquisition strategy that collects spiral navigators with high temporal frame rate and (b) an image reconstruction method that derives temporal subspaces from navigators and reconstructs high-resolution images from sparsely sampled data with joint low-rank and sparsity constraints.
RESULTS: The proposed method has been systematically evaluated and validated through several dynamic speech experiments. A nominal imaging speed of 102 frames per second (fps) was achieved for a single-slice imaging protocol with a spatial resolution of 2.2 × 2.2 × 6.5 mm(3) . An eight-slice imaging protocol covering the entire vocal tract achieved a nominal imaging speed of 12.8 fps with the identical spatial resolution. The effectiveness of the proposed method and its practical utility was also demonstrated in a phonetic investigation.
CONCLUSION: High spatiotemporal resolution with full-vocal-tract spatial coverage can be achieved for dynamic speech imaging experiments with low-rank and sparsity constraints.
© 2014 Wiley Periodicals, Inc.

Entities:  

Keywords:  dynamic speech imaging; low-rank approximation; partial separability modeling; sparsity; spiral navigation

Mesh:

Year:  2014        PMID: 24912452      PMCID: PMC4261062          DOI: 10.1002/mrm.25302

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   4.668


  42 in total

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