Browsing by Author "Pommot Berto, Rodrigo"

Now showing 1 - 1 of 1
  • Thumbnail Image
    ItemOpen Access
    An Investigation of Deep Learning Methods to Shorten GABA-edited Magnetic Resonance Spectroscopy Scan Times
    (2023-09-18) Pommot Berto, Rodrigo; Medeiros de Souza, Roberto; Harris, Ashley; Lebel, Marc; Zhang, Yunyan; Frayne, Richard; McCreary, Cheryl
    Edited magnetic resonance spectroscopy (MRS) can provide localized information on gamma-aminobutyric acid (GABA) concentration in vivo. However, GABA-edited MRS data has a low spectral quality, and many measurements, known as transients, need to be collected and averaged to obtain a high-quality spectrum, resulting in long scan times. This work investigated using deep learning (DL) with only a quarter of the number of conventionally acquired transients to shorten scan times by four while maintaining or improving spectral quality. A proof of concept was demonstrated by reconstructing GABA-edited spectra with only 80 transients and different configurations of DL-based pipelines. The best-performing pipeline used a proposed dimension-reducing 2D U-NET variation and it obtained better spectral quality metrics than conventionally reconstructed spectra with 320 transients. Simulated data was also shown to be useful in pre-training DL model weights. An open data challenge for reconstructing GABA-edited spectra from reduced transients was organized, and various DL models from different participating teams were compared. The challenge results reinforced the proof of concept conclusions that higher spectral quality can be achieved with DL reconstructions. However, the challenge metric evaluation also showed that DL models are able to undesirably exploit the limitations of conventional MRS metrics when using those as the training loss for the models, leading to good metric values but poor reconstructed spectra quality. DL reconstructions of GABA-edited MRS with 80 transients were also quantified and had significant differences from results from conventional reconstructions with 320 transients. However, given the lack of ground truths in the quantified data, it is not possible to conclude which results are closer to the actual concentrations. This work showed that DL methods can reduce GABA-edited MRS scan times while increasing spectral quality. Due to the lack of ground truths for the in vivo data, further studies are necessary to validate the concentrations obtained from the DL-based GABA-edited MRS reconstructions in comparison to conventional methods. This work was developed in the spirit of open science, and the data and code to reproduce the results were made publicly available.