Multi‐site feasibility and reproducibility study on UTE <scp>3D</scp> phosphorous <scp>MRSI</scp> using novel rosette trajectory (<scp>PETALUTE</scp>)
Bibliographic record
Abstract
Abstract Purpose This study aims (1) to implement a robust acquisition, fully automated 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 reproducibility at different experimental setups. Methods A multi‐center feasibility/reproducibility study was conducted for the novel UTE 31 P 3D MRSI sequence with rosette petal trajectory (PETALUTE) at three institutions with different experimental setups (Siemens Prisma with volume head coil or surface coil, Siemens Biograph mMR with volume head coil). Five healthy subjects at each site were measured with an acquisition delay of 65 μs and a final resolution of 10 × 10 × 10 mm 3 in 9 min. The measurement was repeated three times and averaged for the spectral analysis using the LCModel package. The potential for acceleration was assessed 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 phosphocreatine (PCr), adenosine triphosphate (ATP), phosphomonoesters (PME), and a mean coefficient of variation for ATP/PCr below 20%. Conclusion We demonstrated that UTE 31 P 3D rosette MRSI acquisition, combined with compressed sensing and LCModel analysis, allows clinically feasible, robust, high‐resolution 31 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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".