Minimum echo time <scp>PRESS</scp>‐based proton observed carbon edited (<scp>POCE</scp>) <scp>MRS</scp> in rat brain using simultaneous editing and localization pulses
Bibliographic record
Abstract
Purpose Indirect 13C MRS by proton‐observed carbon editing (POCE) is a powerful method to study brain metabolism. The sensitivity of POCE‐MRS can be enhanced through the use of short TEs, which primarily minimizes homonuclear J‐evolution related losses; previous POCE‐MRS implementations use longer than optimal echo times due to sequence limitations, or short TE image selected in vivo spectroscopy‐based multi‐shot acquisitions for 3D localization. To that end, this paper presents a novel single‐shot point resolved spectroscopy (PRESS)‐localized POCE‐MRS sequence that involves the application of simultaneous editing and localization pulses (SEAL)‐PRESS, allowing the TE to be reduced to a theoretically optimal value of ∼ 1/JHC. Methods The optimized SEAL‐PRESS sequence was first evaluated in simulation and in phantom; next, the sequence was validated with dynamic in vivo POCE‐MRS performed in a rat preparation during a 1,6‐13C2‐Glc infusion, and on a microwave fixed rat brain following a 2‐hour [1,6‐13C2]‐Glc infusion. POCE spectra from the SEAL‐PRESS sequence were compared against a previously described 12.6‐ms PRESS‐POCE sequence utilizing a classical carbon editing scheme. Results The SEAL‐PRESS sequence provides > 95% editing efficiency, optimal sensitivity, and localization for POCE MRS with an overall sequence TE of 8.1 ms. Signal amplitude of 13C‐labeled metabolites Glu‐H4, Gln‐H4, Glx‐H3, Glc‐H6 +Glx‐H2, and Asp‐H2 were shown to be improved by >17% relative to a 12.6‐ms PRESS‐POCE sequence in vivo. Conclusion We report for the first time, a single‐shot PRESS‐localized and edited 8.1‐ms TE POCE‐MRS sequence with optimal sensitivity, editing efficiency, and localization.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".