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Abstract #0065

Compact Maps: A Low-Dimensional Approach for High-Dimensional Time-Resolved Coil Sensitivity Map Estimation

Shreya Ramachandran1, Frank Ong2, and Michael Lustig1
1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States, 2Electrical Engineering, Stanford University, Stanford, CA, United States

Dynamic MRI reconstruction techniques often use static coil sensitivity maps, but physical sensitivities can change substantially with respiratory and other subject motion. However, time-resolved sensitivity maps occupy a very large amount of memory, and hence, directly employing these maps is often impractical, especially on memory-limited GPUs. Here, we introduce a technique that solves for a compact representation of time-resolved sensitivity maps by leveraging a temporal basis for sensitivity kernels. Our proposed Compact Maps are significantly cheaper (by ~1000x) to store in memory than conventional time-resolved maps and result in lower calibration error and reconstruction error than do time-averaged maps.

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