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 13C 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 cm3 coverage at 5 × 5 × 5 mm3 nominal resolution. Multiple sources of EPI distortions were encoded using a multi‐echo 1H EPI reference scan. Phase maps computed from the reference scans were combined with a bulk 13C frequency offset encoded in the dual‐echo [1‐13C]pyruvate images to correct geometric distortion and improve spatial registration. The proposed scheme was validated in a phantom study, and in vivo [1‐13C]pyruvate and [1‐13C]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 13C 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".