Abstract #1409

Task-based Optimization of Regularization in Highly Accelerated Speech RT-MRI

Jieshen Chen¹, Sajan Goud Lingala¹, Yongwan Lim¹, Asterios Toutios¹, Shrikanth Narayanan¹, and Krishna Nayak¹

¹Electrical Engineering, University of Southern California, Los Angeles, CA, United States

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.

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