Sajan Goud Lingala1, Sampada Bhave2, Yinghua Zhu1, Krishna Nayak1, and Mathews Jacob2
1Electrical Engineering, University of Southern California, Los Angeles, CA, United States, 2Electrical and Computer Engineering, University of Iowa, Iowa city, IA, United States
A number of dynamic MRI applications have seen the adaptation of data-driven models for efficient de-noising and reconstruction from under-sampled data. In this work, we develop a novel temporal point spread function interpretation of two data-driven models: low rank, and dictionary-learning. Through this interpretation, we show (a) the low rank model to perform spatially invariant non-local view-sharing, and (b) the dictionary-learning model to perform spatially varying non-local view-sharing. Both the models can be viewed as efficient data-driven retrospective binning techniques. We provide demonstrations using the application of de-noising real-time MRI speech data.
