Optimizing SNR for multi‐metabolite hyperpolarized carbon‐13 MRI using a hybrid flip‐angle scheme
Bibliographic record
Abstract
PURPOSE: C]pyruvate using a multispectral variable flip angle (msVFA) scheme in which the spectral profile and flip angle vary dynamically with time. METHODS: Each image acquisition in a time-resolved imaging experiment used a unique spectrally varying RF pulse shape for msVFA. Therefore, the flip angle for every acquisition was optimized for pyruvate and each of its metabolites to yield the highest SNR across the acquisition. Multispectral VFA was compared with a spectrally varying constant flip-angle excitation model through simulations and in vivo. A modified broadband chemical shift-encoded gradient-echo sequence was used for in vivo experiments on six pregnant guinea pigs. Regions of interest placed in the placentae, maternal liver, and maternal kidneys were used as areas for SNR measurement. RESULTS: In vivo experiments showed significant increases in SNR for msVFA relative to constant flip angle of up to 250% for multiple metabolites. CONCLUSION: Hyperpolarized carbon-13 imaging with msVFA excitation produces improved SNR for all metabolites in organs of interest.
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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 | 0.001 |
| 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".