Abstract #4233

Temporal point spread function interpretation of low rank, dictionary learning models in dynamic MRI

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.

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