Can we use 1H MRS shimming values to obtain 31P spectra?
Bibliographic record
Abstract
The perfect shimming of 31P magnetic resonance spectroscopy (MRS) is not easy in vivo. The purpose of this study was to examine the feasibility of using 1H MRS shimming values to obtain 31P spectra in a same sequence. Both phantom and volunteer studies were carried out in this study. Phantom was a sphere filled with physiological metabolites of brain. In vivo study was performed on 4 healthy volunteers. The studies were performed on a 3-T GE scanner. A same localizer and a same cursor were used for both 1H and 31P scans. A spin echo MRS sequence was utilized for both 1H scans with a standard head coil and 31P scans with a GE service coil. 1H scan was performed using first and automatic shimming and water linewidth (FWHM) of 3 Hz for phantom and 5 Hz for the volunteer were obtained. Shimming values of 1H scan in x, y, z directions were copied to 31P scan. A routine procedure of 31P scan without value coping was also performed. Spectra were analyzed using SAGE/IDL program. Signal to noise ratio (SNR) was defined as the ratio of the signal height / maximum noise height. Good 1H spectra and 31P spectra were obtained for both phantom and volunteer studies. The 31P spectra with 1H MRS shimming values were similar with the 31P spectra obtained with routine procedure. Lower SNRs of 31P spectra were obtained in phantom with 1H MRS shimming values, compared with routine procedure scan. Average SNR for Pi of 31P spectra was 7.45:1 in the volunteer study with routine procedure, and 7.275:1 with 1H MRS shimming values. 1H MRS shimming values can be used to obtain useful 31P spectra in a same sequence.
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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 | 0.017 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".