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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