Sci‐Fri AM: MRI and Diagnostic Imaging ‐ 01: Estimating the Transverse Relaxation Time of Taurine Protons in Rat Brain at 9.4 T with Optimized PRESS Sequence Timings
Bibliographic record
Abstract
Purpose: To investigate the response of taurine (Tau) protons as a function of PRESS echo times, TE1 and TE2, to determine two TE combinations that can be employed to estimate the T2 (transverse relaxation) value of Tau at 9.4 T. The Tau protons are involved in J‐coupling interactions; therefore, the two timing combinations should result in similar signal losses due to J‐coupling. Methods: Experiments were performed with a 9.4 T animal MRI scanner. Numerical calculations of the response of Tau as a function of PRESS TE1 and TE2 were calculated and two TE combinations that yield a similar area for the 3.42 ppm Tau resonance were selected as optimal. The timings were verified on a 50 mM Tau/10 mM Cr (creatine) phantom. In‐vivo experiments were performed on four rats. Spectra were acquired with the timings from a voxel placed in the rat brain and Tau peak areas were fit to monoexponentially decaying functions to obtain T2 values. Results: The PRESS TE combinations selected for Tau T2 determination are {TE1, TE2} = {17 ms, 10 ms} and {80 ms, 70 ms}; the signal yield for the two timings differs by 5 % theoretically. The average Tau T2 for the four rats was found to be 106 ms with a standard deviation of 12 ms. Conclusions: We have demonstrated that acquiring PRESS spectra with {TE1, TE2} = {17 ms, 10 ms} and {TE1, TE2} = {80 ms, 70 ms} enables T2 corrected measures of Tau to be obtained at 9.4 T.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| 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".