Accelerated 3D echo‐planar imaging with compressed sensing for time‐resolved hyperpolarized <sup>13</sup>C studies
Bibliographic record
Abstract
Purpose To enable large field‐of‐view, time‐resolved volumetric coverage in hyperpolarized 13C metabolic imaging by implementing a novel data acquisition and image reconstruction method based on the compressed sensing framework. Methods A spectral‐spatial pulse for single‐resonance excitation followed by a symmetric echo‐planar imaging (EPI) readout was implemented for encoding a 72 × 18 cm2 field of view at 5 × 5 mm2 resolution. Random undersampling was achieved with blipped z‐gradients during the ramp portion of the echo‐planar imaging readout. The sequence and reconstruction were tested with phantom studies and consecutive in vivo hyperpolarized 13C scans in rats. Retrospectively and prospectively undersampled data were compared on the basis of structural similarity in the reconstructed images and the quantification of the lactate‐to‐pyruvate ratio in rat kidneys. Results No artifacts or loss of resolution are evident in the compressed sensing reconstructed images acquired with the proposed sequence. Structural similarity analysis indicate that compressed sensing reconstructions can accurately recover spatial features in the metabolic images evaluated. Conclusion A novel z‐blip acquisition sequence for compressed sensing accelerated hyperpolarized 13C 3D echo‐planar imaging was developed and demonstrated. The close agreement in lactate‐to‐pyruvate ratios from both retrospectively and prospectively undersampled data from rats shows that metabolic information is preserved with acceleration factors up to 3‐fold with the developed method. Magn Reson Med 77:538–546, 2017. © 2016 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".