k-Space-based coil combination via geometric deep learning for reconstruction of non-Cartesian MRSI data

Purpose: State-of-the-art whole-brain MRSI with spatial-spectral encoding and multichannel acquisition generates huge amounts of data, which must be efficiently processed to stay within reasonable reconstruction times. Although coil combination significantly reduces the amount of data, currently it is performed in image space at the end of the reconstruction. This prolongs reconstruction times and increases RAM requirements. We propose an alternative k-space-based coil combination that uses geometric deep learning to combine MRSI data already in native non-Cartesian k-space. Methods: Twelve volunteers were scanned at a 3T MR scanner with a 20-channel head coil at 10 different positions with water-unsuppressed MRSI. At the eleventh position, water-suppressed MRSI data were acquired. Data of 7 volunteers were used to estimate sensitivity maps and form a base for simulating training data. A neural network was designed and trained to remove the effect of sensitivity profiles of the coil elements from the MRSI data. The water-suppressed MRSI data of the remaining volunteers were used to evaluate the performance of the new k-space-based coil combination relative to that of a conventional image-based alternative. Results: For both approaches, the resulting metabolic ratio maps were similar. The SNR of the k-space-based approach was comparable to the conventional approach in low SNR regions, but underperformed for high SNR. The Cramer-Rao lower bounds show the same trend. The analysis of the FWHM showed no difference between the two methods. Conclusion: k-Space-based coil combination of MRSI data is feasible and reduces the amount of raw data immediately after their sampling.