Keywords: Data Processing, Data Analysis, Speech, motion, atlas, dynamic MRI, cleft palate
Motivation: Characterizing velopharyngeal motion patterns in children with cleft palate is an important research topic.
Goal(s): Utilizing recently improved dynamic MRI techniques, we aim to uncover distinctive deformation patterns in cleft palate speech from a statistical perspective.
Approach: We propose a post-processing pipeline based on spatiotemporal atlases, manually segmented velopharyngeal labels, deformable registration, and principal component analysis. The speech dataset consisting of 17 normal controls and 4 patients was analyzed.
Results: The proposed method effectively captures and separates patient-specific deformation patterns within principal component’s feature spaces. Furthermore, it reveals the impact from different anatomical regions in cleft palate speech.
Impact: In practice, cleft palate patterns in speech MRI are too subtle for visual examination or conventional post-processing methods to reveal. Providing a solution to uncover such patterns is essential to help understand the anatomical and functional changes in this disorder.
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