Multi-site Ultrashort Echo Time 3D Phosphorous MRSI repeatability using novel Rosette Trajectory (PETALUTE)
Bibliographic record
Abstract
Abstract Purpose This study aims 1) to implement an operator-independent acquisition, reconstruction, and processing pipeline using a novel rosette k-space pattern for UTE 31 P 3D MRSI and 2) to evaluate the clinical applicability and replicability at different experimental setups. Methods A multicenter repeatability study was conducted for the novel UTE 31 P 3D Rosette MRSI at three institutions with different experimental setups. Non-localized 31 P MRSI data of 5 healthy subjects at each site were acquired with an acquisition delay of 65 μs and a final resolution of 10 × 10 × 10 mm 3 in 9 min. Spectra were quantified using the LCModel package. The potential acceleration was achieved using compressed sensing on retrospectively undersampled data. Reproducibility at each site was evaluated using the inter-subject coefficient of variance. Results This novel acquisition and advanced processing techniques yielded high-quality spectra and enabled the detection of the critical brain metabolites at three different sites with different hardware specifications. In vivo, feasibility with an acceleration factor of 4 in 6.75 min resulted in a mean Cramér-Rao lower bounds below 20% for PCr, ATPs, PME, and the mean CoV of ATP/PCr resulted in below %20. Conclusion We demonstrated that UTE 31 P 3D Rosette MRSI acquisition, combined with compressed sensing and LCModel analysis, allows patient-friendly, operator-independent, high-resolution 3 1 P MRSI to be acquired at clinical setups.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".