Partial Fourier reconstruction for improved resolution in 3D hyperpolarized <sup>13</sup>C EPI
Bibliographic record
Abstract
Purpose Asymmetric in‐plane k‐space sampling of EPI can reduce the minimum achievable TE in hyperpolarized with spectral‐spatial radio frequency pulses, thereby reducing weighting and signal‐losses. Partial Fourier image reconstruction exploits the approximate Hermitian symmetry of k‐space data and can be applied to asymmetric data sets to synthesize unmeasured data. Here we tested whether the application of partial Fourier image reconstruction would improve spatial resolution from hyperpolarized [1‐ ]pyruvate scans in the human brain. Methods Fifteen healthy control subjects were imaged using a volumetric dual‐echo echo‐planar imaging sequence with spectral‐spatial radio frequency excitation. Images were reconstructed by zero‐filling as well as with the partial Fourier reconstruction algorithm projection‐on‐convex‐sets. Resulting images were quantitatively evaluated with a no‐reference image quality assessment. Results The no‐reference image sharpness metric agreed with perceived improvements in image resolution and contrast. The [1‐ ]lactate images benefitted most, followed by the [1‐ ]pyruvate images. The ‐bicarbonate images were improved by the smallest degree, likely owing to relatively lower SNR. Conclusions Partial Fourier imaging and reconstruction were shown to improve the sharpness and contrast of human HP brain data and is a viable method for enhancing resolution.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".