Joint Optimization of Data Sampling and Reconstruction for Dynamic MRI

Motivation: Sampling patterns in deep learning (DL) or compressed sensing (CS) based accelerated dynamic MRI reconstructions are typically chosen heuristically. k-t sampling patterns can be optimized to capture the spatio-temporal characteristics of dynamic MRI data more efficiently. Goal(s): Our objective is to develop a method for optimizing k-t sampling patterns for dynamic MRI. Approach: We extend the recently developed AutoSamp framework to dynamic MRI setting to jointly optimize k-t sampling and reconstruction. We test our method on a cardiac cine dataset. Results: DL reconstruction with optimized k-t patterns using the proposed method produces higher quality results with reduced spatial and temporal artifacts. Impact: Dynamic MRI reconstructions with learned sampling patterns improves reconstruction quality. The learned patterns can also provide insights about designing general k-t MRI sampling patterns.