Keywords: AI/ML Image Reconstruction, Data Processing, Dynamic Imaging
Motivation: Supervised deep learning (DL) reconstruction requires large training sets and computationally demanding training. Real-time MRI offers large temporal redundancy which yields high reconstruction performance from training on a subset of frames.
Goal(s): To develop a method for curating small DL training datasets that capture the variance of the entire training set and provide performance non-inferior to the entire training set, with reduced training time.
Approach: We use clustering for each training speech task followed by selecting a fraction of each cluster to train U-Nets for reconstruction.
Results: We achieve improved image quality metrics with comparable image quality metrics with 10x improved training time.
Impact: By using curated training data based on identification and clustering of vocal tract postures, we demonstrate supervised DL-reconstruction of speech RT-MRI with 10-fold training time reduction and comparable NRMSE, PSNR, and SSIM. This may be generalized to other dynamic reconstructions.
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