High-resolution dynamic speech imaging with joint low-rank and sparsity constraints
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...
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Published in | Magnetic resonance in medicine Vol. 73; no. 5; pp. 1820 - 1832 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Blackwell Publishing Ltd
01.05.2015
Wiley Subscription Services, Inc |
Subjects | |
Online Access | Get full text |
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Summary: | 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 mm3. 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. Magn Reson Med 73:1820–1832, 2015. © 2014 Wiley Periodicals, Inc. |
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Bibliography: | NIH - No. 1R03DC009676-01A1 a Computational Science and Engineering fellowship from the University of Illinois at Urbana-Champaign (to M. F.) ArticleID:MRM25302 NSF - No. 1121780 istex:A818B362DB9C376A66199909AD07B7347692BB80 ark:/67375/WNG-28TB9TSJ-2 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Undefined-3 |
ISSN: | 0740-3194 1522-2594 |
DOI: | 10.1002/mrm.25302 |