Maojing Fu1,2, Marissa S Barlaz3, Joseph L Holtrop2,4, Jamie L Perry5, David P Kuehn6, Ryan K Shosted2,3, Zhi-Pei Liang1,2, Bradley P Sutton2,4. 1. Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA. 2. Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA. 3. Linguistics, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA. 4. Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA. 5. Communication Sciences and Disorders, East Carolina University, Greenville, North Carolina, USA. 6. Speech and Hearing Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
Abstract
PURPOSE: To achieve high temporal frame rate, high spatial resolution and full-vocal-tract coverage for three-dimensional dynamic speech MRI by using low-rank modeling and sparse sampling. METHODS: Three-dimensional dynamic speech MRI is enabled by integrating a novel data acquisition strategy and an image reconstruction method with the partial separability model: (a) a self-navigated sparse sampling strategy that accelerates data acquisition by collecting high-nominal-frame-rate cone navigator sand imaging data within a single repetition time, and (b) are construction method that recovers high-quality speech dynamics from sparse (k,t)-space data by enforcing joint low-rank and spatiotemporal total variation constraints. RESULTS: The proposed method has been evaluated through in vivo experiments. A nominal temporal frame rate of 166 frames per second (defined based on a repetition time of 5.99 ms) was achieved for an imaging volume covering the entire vocal tract with a spatial resolution of 2.2 × 2.2 × 5.0 mm3 . Practical utility of the proposed method was demonstrated via both validation experiments and a phonetics investigation. CONCLUSION: Three-dimensional dynamic speech imaging is possible with full-vocal-tract coverage, high spatial resolution and high nominal frame rate to provide dynamic speech data useful for phonetic studies. Magn Reson Med 77:1619-1629, 2017.
PURPOSE: To achieve high temporal frame rate, high spatial resolution and full-vocal-tract coverage for three-dimensional dynamic speech MRI by using low-rank modeling and sparse sampling. METHODS: Three-dimensional dynamic speech MRI is enabled by integrating a novel data acquisition strategy and an image reconstruction method with the partial separability model: (a) a self-navigated sparse sampling strategy that accelerates data acquisition by collecting high-nominal-frame-rate cone navigator sand imaging data within a single repetition time, and (b) are construction method that recovers high-quality speech dynamics from sparse (k,t)-space data by enforcing joint low-rank and spatiotemporal total variation constraints. RESULTS: The proposed method has been evaluated through in vivo experiments. A nominal temporal frame rate of 166 frames per second (defined based on a repetition time of 5.99 ms) was achieved for an imaging volume covering the entire vocal tract with a spatial resolution of 2.2 × 2.2 × 5.0 mm3 . Practical utility of the proposed method was demonstrated via both validation experiments and a phonetics investigation. CONCLUSION: Three-dimensional dynamic speech imaging is possible with full-vocal-tract coverage, high spatial resolution and high nominal frame rate to provide dynamic speech data useful for phonetic studies. Magn Reson Med 77:1619-1629, 2017.
Authors: Frank Ong; Xucheng Zhu; Joseph Y Cheng; Kevin M Johnson; Peder E Z Larson; Shreyas S Vasanawala; Michael Lustig Journal: Magn Reson Med Date: 2020-04-09 Impact factor: 4.668
Authors: Fangxu Xing; Riwei Jin; Imani R Gilbert; Jamie L Perry; Bradley P Sutton; Xiaofeng Liu; Georges El Fakhri; Ryan K Shosted; Jonghye Woo Journal: J Acoust Soc Am Date: 2021-11 Impact factor: 1.840
Authors: Eshan Pua Schleif; Catherine M Pelland; Charles Ellis; Xiangming Fang; Stephen J Leierer; Bradley P Sutton; David P Kuehn; Silvia S Blemker; Jamie L Perry Journal: J Speech Lang Hear Res Date: 2020-06-15 Impact factor: 2.297