Impact of frequency drift on gamma-aminobutyric acid-edited MR spectroscopy
Bibliographic record
Abstract
PURPOSE: To investigate the quantitative impact of frequency drift on Gamma-Aminobutyric acid (GABA+)-edited MRS of the human brain at 3 Tesla (T). METHODS: Three sequential GABA+-edited MEGA-PRESS acquisitions were acquired in fifteen sessions; in ten of these, MRS was preceded by functional MRI (fMRI) to induce frequency drift, which was estimated from the creatine resonance at 3.0 ppm. Simulations were performed to examine the effects of frequency drift on the editing efficiency of GABA and co-edited macromolecules (MM) and of subtraction artifacts on GABA+ quantification. The efficacy of postprocessing frequency correction was also investigated. RESULTS: Gradient-induced frequency drifts affect GABA+ quantification for at least 30 min after imaging. Average frequency drift was low in control sessions and as high as -2 Hz/min after fMRI. Uncorrected frequency drift has an approximately linear effect on GABA+ measurements with a -10 Hz drift resulting in a 16% decrease in GABA+, primarily due to subtraction artifacts. CONCLUSION: Imaging acquisitions with high gradient duty cycles can impact subsequent GABA+ measurements. Postprocessing can address subtraction artifacts, but not changes in editing efficiency or GABA:MM signal ratios; therefore, protocol design should avoid intensive gradient sequences before edited MRS Magn Reson Med 72:941-948, 2014. © 2013 Wiley Periodicals, Inc.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".