Frequency and Phase Correction of GABA-Edited Magnetic Resonance Spectroscopy using Complex-Valued Convolutional Neural Networks
Bibliographic record
Abstract
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 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 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 GABA’s signal-to-noise ratio and linewidth, Choline artifact, and by analyzing the reconstructed final difference spectrum. Statistical significance and effect size were assessed using the Wilcoxon signed rank test and Cohen’s d respectively. Results The ablation study showed that using complex values for the input represented by real and imaginary channels in our model input tensor, with real (conventional) convolutions was most effective for FPC. For the comparative study, the simulated data test set showed that our CNN model that received complex-valued inputs with real convolutions outperformed models it was compared against with a lower mean absolute error (p<0.05). For the in vivo data test set, our model performed similarly to other DL FPC models. Conclusion Our results indicate that the optimal CNN configuration for GABA-edited MRS FPC uses a complex-valued input and real-valued convolutions. This model outperformed existing DL models on simulated data and performed similarly on in vivo data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".