Abstract

Speech RT-MRI has recently experienced significant improvements in spatio-temporal resolution, through the use of sparse sampling and constrained reconstruction. The regularization parameters used for balancing data consistency and object model consistency were often chosen by visual assessment of image quality. Here, we perform task-based optimization of regularization in highly accelerated speech RT-MRI, focusing on the production of consonants and vowels, and analyzing the articulatory features, using both qualitative and quantitative methods. Results drawn from different methods help determine proper regularization parameters for the reconstruction of specific speaking tasks.
Purpose
Real-time MRI has emerged as a powerful tool to noninvasively assess vocal tract dynamics during speech production. It is continuing to provide new insights in several speech science and clinical applications (eg language production, language timing, speech errors, speech assessment pre and post oropharyngeal cancer treatment) 1, 2, 3). Recently, significant improvements in spatio-temporal resolution and coverage have been achieved via the use of sparse sampling and constrained reconstruction using object models (eg. low rank, and/or transform sparsity) 4-7. These require tuning of one or more regularization parameters that are used to perform a trade-off between data consistency and object model consistency. These parameters are chosen heuristically, based on an L-curve 6, or more often by visual assessment of image quality. In this work, we perform optimization for speech RT-MRI based on task-specific metrics including preservation …

Date

2017

Authors

Jieshen Chen, Sajan Goud Lingala, Yongwan Lim, Asterios Toutios, SS Narayanan, KS Nayak

Journal

Proceedings of the International Society of Magnetic Resonance in Medicine, Honolulu, HI

Pages

1409