Improved tolerance to off‐resonance in spectral‐spatial EPI of hyperpolarized [1‐<sup>13</sup>C]pyruvate and metabolites
Bibliographic record
Abstract
Purpose For 13C echo‐planar imaging (EPI) with spectral‐spatial excitation, main field inhomogeneity can result in reduced flip angle and spatial artifacts. A hybrid time‐resolved pulse sequence, multi‐echo spectral‐spatial EPI, is proposed combining broader spectral‐spatial passbands for greater off‐resonance tolerance with a multi‐echo acquisition to separate signals from potentially co‐excited resonances. Methods The performance of the imaging sequence and the reconstruction pipeline were evaluated for 1H imaging using a series of increasingly dilute 1,4‐dioxane solutions and for 13C imaging using an ethylene glycol phantom. Hyperpolarized [1‐13C]pyruvate was administered to two healthy rats. Multi‐echo data of the rat kidneys were acquired to test realistic cases of off‐resonance. Results Analysis of separated images of water and 1,4‐dioxane following multi‐echo signal decomposition showed water‐to‐dioxane 1H signal ratios that were in agreement with the independent measurements by 1H spectroscopy for all four concentrations of 1,4‐dioxane. The 13C signal ratio of two co‐excited resonances of ethylene glycol was accurately recovered after correction for the spectral profile of the redesigned spectral‐spatial pulse. In vivo, successful separation of lactate and pyruvate‐hydrate signals was achieved for all except the early time points during which signal variations exceeded the temporal resolution of the multi‐echo acquisition. Conclusion Improved tolerance to off‐resonance in the new 13C data acquisition pipeline was demonstrated in vitro and in vivo. Magn Reson Med 80:925–934, 2018. © 2018 International Society for Magnetic Resonance in Medicine.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".