Aspects of spinal bone marrow fat to water quantification with magnetic resonance spectroscopy at 3 T
Bibliographic record
Abstract
Aspects of spinal bone marrow fat to water ratio (FWR) quantification with magnetic resonance spectroscopy (MRS) at 3 T were examined in this work. A Point RESolved Spectroscopy (PRESS) sequence with TE = 40 ms and TE = 70 ms was employed to obtain spectra from L3 and T7 vertebrae of twenty healthy volunteers within the age range of 21–50 years (8 female, 12 male); measurements from the C4 vertebra were obtained from 19 of the volunteers. The spectra were used to determine FWR and fat and water T 2 values. Spectra were fitted to yield areas for the fat peak (≈1.3 ppm), the water peak (≈4.7 ppm) and the olefinic resonance (≈5.3 ppm). Ignoring the olefinic contribution to the water signal results in about 10% lower FWRs using short-TE PRESS and overestimates water T 2 by about 8% in the L3 vertebrae. Neglecting to correct for T 2 relaxation resulted in an average overestimation of FWR by a factor 3.11 ± 1.30 (when comparing T 2 corrected FWR to that obtained with PRESS TE = 40 ms) in L3 vertebrae. Paired t-tests were employed to investigate statistical significance of differences between different pairs of vertebrae in each volunteer. On average, it was found that FWRs in the T7 and C4 vertebrae were approximately 75% and 57%, respectively, of the corresponding value for the L3 vertebra in each volunteer ( p -values < 0.01). Fat T 2 values were on average, in each volunteer, lower in C4 vertebrae compared to those of L3 and T7 (17% and 23% lower, respectively) and C4 water T 2 values were ≈6% lower than the T7 values. To our knowledge, this is the first MRS study to examine the variation of FWR and T 2 values across different vertebral sections.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".