RNN-aided metabolite quantification from incomplete FIDs in 1H-MRS of the brain

Motivation: Incomplete FIDs can be obtained due to the limited sampling windows as in spectroscopic MRF and SSFP-MRSI, or due to FID truncation for removing spectral artifact. Goal(s): Developing a means of quantifying metabolites from incomplete FIDs will allow more efficient sequence design and better experimental outcome. Approach: We developed a recurrent-neural-network (RNN) for metabolite quantification from incomplete FIDs at 3.0T. The RNN was trained on simulated data and tested on in vivo data. Results: Although the performance of the RNN requires further improvement for low concentration metabolites (e.g., GABA), it may allow quantification of the major metabolites under highly limited sampling windows. Impact: Incomplete FIDs can be obtained due to the limited sampling windows as in spectroscopic MRF and SSFP-MRSI. We developed a recurrent-neural-network, which can quantify the major metabolites from the initial 64 FID data points, thereby allowing more efficient sequence design.