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Abstract #2849

Principal Component Characterization of Deformation Variations Using Dynamic Imaging Atlases

Fangxu Xing1, Riwei Jin2, Imani Gilbert3, Georges El Fakhri1, Jamie Perry3, Bradley Sutton2, and Jonghye Woo1
1Radiology, Massachusetts General Hospital, Boston, MA, United States, 2University of Illinois at Urbana-Champaign, Champaign, IL, United States, 3East Carolina University, Greenville, NC, United States


High-speed dynamic magnetic resonance imaging is a highly efficient tool in capturing vocal tract deformation during speech. However, automated quantification of variations in motion patterns during production of different utterances has been a challenging task due to spatial and temporal misalignments between different image datasets. We present a principal component analysis-based deformation characterization technique built on top of established dynamic speech imaging atlases. Two layers of principal components are extracted to represent common motion and utterance-specific motion, respectively. Comparison between two speech tasks with and without nasalization reveals subtle differences on velopharyngeal deformation reflected in the utterance-specific principal components.

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