Frequency and phase correction for multiplexed edited MRS of GABA and glutathione
Bibliographic record
Abstract
PURPOSE: Detection of endogenous metabolites using multiplexed editing substantially improves the efficiency of edited magnetic resonance spectroscopy. Multiplexed editing (i.e., performing more than one edited experiment in a single acquisition) requires a tailored, robust approach for correction of frequency and phase offsets. Here, a novel method for frequency and phase correction (FPC) based on spectral registration is presented and compared against previously presented approaches. METHODS: One simulated dataset and 40 γ-aminobutyric acid-/glutathione-edited HERMES datasets acquired in vivo at three imaging centers were used to test four FPC approaches: no correction; spectral registration; spectral registration with post hoc choline-creatine alignment; and multistep FPC. The performance of each routine for the simulated dataset was assessed by comparing the estimated frequency/phase offsets against the known values, whereas the performance for the in vivo data was assessed quantitatively by calculation of an alignment quality metric based on choline subtraction artifacts. RESULTS: The multistep FPC approach returned corrections that were closest to the true values for the simulated dataset. Alignment quality scores were on average worst for no correction, and best for multistep FPC in both the γ-aminobutyric acid- and glutathione-edited spectra in the in vivo data. CONCLUSIONS: Multistep FPC results in improved correction of frequency/phase errors in multiplexed γ-aminobutyric acid-/glutathione-edited magnetic resonance spectroscopy experiments. The optimal FPC strategy is experiment-specific, and may even be dataset-specific. Magn Reson Med 80:21-28, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".