Whole-brain MRSI measured with a concentric ring trajectories based FID-MRSI sequence generates large amounts of data, which makes post-processing very time-consuming (up to several hours). To speed-up the reconstruction, deep learning approaches could be applied. AUTOMAP provides an attractive solution to reconstruct data directly from non-Cartesian kSpace data. However, it requires single-channel data. Therefore, the coil combination needs to be performed in the kSpace domain. We showed that this strategy is in principle feasible, but requires future work on stability against noise.
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