An Investigation of Different Deep Learning Pipelines for GABA-Edited MRS Reconstruction

Edited magnetic resonance spectroscopy (MRS) can provide localized information on gamma-aminobutyric acid (GABA) concentration in vivo. However, edited-MRS scans are long due to the fact that many acquisitions, known as transients, need to be collected and averaged to obtain a high-quality spectrum for reliable GABA quantification. In this work, we investigate Deep Learning (DL) pipelines for the reconstruction of GABA-edited MRS spectra using only a quarter of the transients typically acquired. We compared two neural network architectures: a 1D U-NET and a proposed dimension-reducing 2D U-NET (Rdc-UNET2D) that we proposed. We also compared the impact of training the DL pipelines using solely in vivo data or pre-training the models on simulated followed by fine-tuning on in vivo data. Results for this study showed the proposed Rdc-UNET2D model pre-trained on simulated data and fine-tuned on in vivo data had the best performance among the different DL pipelines compared. This model obtained a higher SNR and a lower fit error than a conventional reconstruction pipeline using the full amount of transients typically acquired. This indicates that through DL it is possible to reduce GABA-edited MRS scan times by four times while maintaining or improving data quality. In the spirit of open science, the code and data to reproduce our pipeline are publicly available.