Effects of eddy currents on selective spectral editing experiments at 3T
Bibliographic record
Abstract
Purpose To investigate frequency‐offset effects in edited magnetic resonance spectroscopy (MRS) experiments arising from B0 eddy currents. Materials and Methods Macromolecule‐suppressed (MM‐suppressed) γ‐aminobutyric acid (GABA)‐edited experiments were performed at 3T. Saturation‐offset series of MEGA‐PRESS experiments were performed in phantoms, in order to investigate different aspects of the relationship between the effective editing frequencies and eddy currents associated with gradient pulses in the sequence. Difference integrals were quantified for each series, and the offset dependence of the integrals was analyzed to quantify the difference in frequency (Δf) between the actual vs. nominal expected saturation frequency. Results Saturation‐offset N‐acetyl‐aspartate‐phantom experiments show that Δf varied with voxel orientation, ranging from 10.4 Hz (unrotated) to 6.4 Hz (45° rotation about the caudal–cranial axis) and 0.4 Hz (45° rotation about left–right axis), indicating that gradient‐related B0 eddy currents vary with crusher‐gradient orientation. Fixing the crusher‐gradient coordinate‐frame substantially reduced the orientation dependence of Δf (to ∼2 Hz). Water‐suppression crusher gradients also introduced a frequency offset, with Δf = 0.6 Hz (“excitation” water suppression), compared to 10.2 Hz (no water suppression). In vivo spectra showed a negative edited “GABA” signal, suggesting Δf on the order of 10 Hz; with fixed crusher‐gradient coordinate‐frame, the expected positive edited “GABA” signal was observed. Conclusion Eddy currents associated with pulsed field gradients may have a considerable impact on highly frequency‐selective spectral‐editing experiments, such as MM‐suppressed GABA editing at 3T. Careful selection of crusher gradient orientation may ameliorate these effects. Level of Evidence: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:673–681.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".