Dual‐Echo EPI sequence for integrated distortion correction in 3D time‐resolved hyperpolarized <sup>13</sup>C MRI
Bibliographic record
Abstract
Purpose To provide built‐in off‐resonance correction in time‐resolved, volumetric hyperpolarized 13 C metabolic imaging by implementing a novel dual‐echo 3D echo‐planar imaging (EPI) sequence and reconstruction. Methods A spectral‐spatial pulse for single‐resonance excitation followed by a dual‐echo 3D EPI readout was implemented to provide 64 × 8 × 6 cm 3 coverage at 5 × 5 × 5 mm 3 nominal resolution. Multiple sources of EPI distortions were encoded using a multi‐echo 1 H EPI reference scan. Phase maps computed from the reference scans were combined with a bulk 13 C frequency offset encoded in the dual‐echo [1‐ 13 C]pyruvate images to correct geometric distortion and improve spatial registration. The proposed scheme was validated in a phantom study, and in vivo [1‐ 13 C]pyruvate and [1‐ 13 C]lactate rat images were acquired with intentional transmit frequency deviations to assess the dual‐echo 3D EPI sequence. Results The phantom study demonstrated improved spatial registration in off‐resonance corrected images. Close agreement was observed between metabolic kidney signal and the underlying anatomy in rat imaging experiments. Relative to a single‐echo acquisition, the coherent addition of the two corrected echoes provided the expected increase in signal‐to‐noise ratio by approximately . Conclusion A novel dual‐echo 3D EPI acquisition sequence for integrated off‐resonance correction in hyperpolarized 13 C imaging was developed and demonstrated. The proposed sequence offers clear advantages over flyback EPI for time‐resolved metabolic mapping. Magn Reson Med 79:643–653, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".