Imaging considerations for in vivo <sup>13</sup>C metabolic mapping using hyperpolarized <sup>13</sup>C‐pyruvate
Bibliographic record
Abstract
One of the challenges of optimizing signal-to-noise ratio (SNR) and image quality in (13)C metabolic imaging using hyperpolarized (13)C-pyruvate is associated with the different MR signal time-courses for pyruvate and its metabolic products, lactate and alanine. The impact of the acquisition time window, variation of flip angles, and order of phase encoding on SNR and image quality were evaluated in mathematical simulations and rat experiments, based on multishot fast chemical shift imaging (CSI) and three-dimensional echo-planar spectroscopic imaging (3DEPSI) sequences. The image timing was set to coincide with the peak production of lactate. The strategy of combining variable flip angles and centric phase encoding (cPE) improved image quality while retaining good SNR. In addition, two aspects of EPSI sampling strategies were explored: waveform design (flyback vs. symmetric EPSI) and spectral bandwidth (BW = 500 Hz vs. 267 Hz). Both symmetric EPSI and reduced BW trended toward increased SNR. The imaging strategies reported here can serve as guidance to other multishot spectroscopic imaging protocols for (13)C metabolic imaging applications.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".