MO‐F‐211‐09: Scalar Coupling Effects on Lipid Composition Determination Using Localized 1H Magnetic Resonance Spectroscopy at 9.4 T
Bibliographic record
Abstract
Purpose: To improve lipid composition determination by localized proton magnetic resonance spectroscopy (MRS) at 9.4 T by minimizing J‐coupling modulations of the 2.1 ppm, 2.3 ppm, and 2.8 ppm lipid peaks. Methods: Experiments were conducted on corn oil and sesame oil phantoms at 9.4 T. A regular PRESS (Point RESolved Spectroscopy) sequence was used to obtain spectra from the oils with echo times (TEs) of 20, 30, 40, 50, 80, 120, 180, and 240 ms. A modified PRESS sequence designed to minimize signal losses due to J‐coupling was also utilized to acquire spectra of the 2.1 ppm, 2.3 ppm, and 2.8 ppm resonances of the oils with TEs of 40, 50, 80, 120, 180 and 240 ms. The areas of the three lipid resonances obtained by both sequences were plotted as a function of echo time and the resulting curves were fitted to the function Moexp(−TE/T2), where Mo is proportional to the proton concentration of the target resonance. Oil compositions (% linoleic acid, % oleic acid, and % saturated fatty acids) were determined from extrapolated Mo values for the three resonances and from equations established in the literature. Results: The responses of the three proton groups to a regular PRESS sequence were modulated due to J‐coupling interactions. However, the responses to the modified PRESS sequence fit well to monoexponentially decaying functions. Oil compositions determined with the PRESS sequence designed to minimize J‐coupling effects agreed with results reported in the literature unlike those calculated from spectra acquired with the regular PRESS sequence. Conclusions: Lipid compositions determined by localized proton MRS can contain significant errors (as high as 49 %), even if they are determined from short‐ TE PRESS spectra corrected for T2 relaxation, if the influence of J‐coupling is not considered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".