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. 2024 Sep:111:186-195.
doi: 10.1016/j.mri.2024.05.008. Epub 2024 May 12.

Frequency and phase correction of GABA-edited magnetic resonance spectroscopy using complex-valued convolutional neural networks

Hanna Bugler  1 , Rodrigo Berto  2 , Roberto Souza  3 , Ashley D Harris  4
Affiliations
Free article

Frequency and phase correction of GABA-edited magnetic resonance spectroscopy using complex-valued convolutional neural networks

Hanna Bugler et al. Magn Reson Imaging. 2024 Sep.
Free article

Abstract

Purpose: To determine the significance of complex-valued inputs and complex-valued convolutions compared to real-valued inputs and real-valued convolutions in convolutional neural networks (CNNs) for frequency and phase correction (FPC) of GABA-edited magnetic resonance spectroscopy (MRS) data.

Methods: An ablation study using simulated data was performed to determine the most effective input (real or complex) and convolution type (real or complex) to predict frequency and phase shifts in GABA-edited MEGA-PRESS data using CNNs. The best CNN model was subsequently compared using both simulated and in vivo data to two recently proposed deep learning (DL) methods for FPC of GABA-edited MRS. All methods were trained using the same experimental setup and evaluated using the signal-to-noise ratio (SNR) and linewidth of the GABA peak, choline artifact, and by visually assessing the reconstructed final difference spectrum. Statistical significance was assessed using the Wilcoxon signed rank test.

Results: The ablation study showed that using complex values for the input represented by real and imaginary channels in our model input tensor, with complex convolutions was most effective for FPC. Overall, in the comparative study using simulated data, our CC-CNN model (that received complex-valued inputs with complex convolutions) outperformed the other models as evaluated by the mean absolute error.

Conclusion: Our results indicate that the optimal CNN configuration for GABA-edited MRS FPC uses a complex-valued input and complex convolutions. Overall, this model outperformed existing DL models.

Keywords: 1H spectroscopy; Convolutional neural networks (CNN); Frequency and phase correction; GABA-edited MRS; Machine learning; Pre-processing.

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Conflict of interest statement

Declaration of competing interest All co-authors have nothing to declare.

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