Results of the 2023 ISBI Challenge to Reduce GABA-Edited MRS Acquisition Time
This conference challenge compared machine-learning reconstruction models for gamma-aminobutyric acid (GABA)-edited MR spectroscopy using only one-quarter of the transients typically required for a complete scan, across three tracks: simulated data, in vivo data with matched acquisition parameters, and in vivo data with different parameters. Performance was scored on mean squared error, signal-to-noise ratio, linewidth, and a proposed shape-score metric, using an openly released baseline model and simulated data. Of three compared submissions, a covariance-matrix CNN performed best on simulated data, while a vision transformer on a spectrogram representation performed best on both in vivo tracks. Reconstructions from just 80 transients matched or beat conventional 320-transient results on SNR, linewidth, and fit error, though some models optimized those metrics without truly improving spectral quality, pointing to a need for more robust evaluation metrics.
Abstract
Purpose: Use a conference challenge format to compare machine learning-based gamma-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) reconstruction models using one-quarter of the transients typically acquired during a complete scan.
Methods: There were three tracks: Track 1: simulated data, Track 2: identical acquisition parameters with in vivo data, and Track 3: different acquisition parameters with in vivo data. The mean squared error, signal-to-noise ratio, linewidth, and a proposed shape score metric were used to quantify model performance. Challenge organizers provided open access to a baseline model, simulated noise-free data, guides for adding synthetic noise, and in vivo data.
Results: Three submissions were compared. A covariance matrix convolutional neural network model was most successful for Track 1. A vision transformer model operating on a spectrogram data representation was most successful for Tracks 2 and 3. Deep learning (DL) reconstructions with 80 transients achieved equivalent or better SNR, linewidth and fit error compared to conventional 320 transient reconstructions. However, some DL models optimized linewidth and SNR without actually improving overall spectral quality, indicating a need for more robust metrics.
Conclusion: DL-based reconstruction pipelines have the promise to reduce the number of transients required for GABA-edited MRS.