Characterization and compensation of inhomogeneity artifact in spiral hyperpolarized<sup>13</sup>C imaging of the human heart
Bibliographic record
Abstract
Purpose This study aimed to investigate the role of regional inhomogeneity in spiral hyperpolarized13C image quality and to develop measures to alleviate these effects. Methods Field map correction of hyperpolarized13C cardiac imaging using spiral readouts was evaluated in healthy subjects. Spiral readouts with differing duration (26 and 45 ms) but similar resolution were compared with respect to off‐resonance performance and image quality. An map‐based image correction based on the multifrequency interpolation (MFI) method was implemented and compared to correction using a global frequency shift alone. Estimation of an unknown frequency shift was performed by maximizing a sharpness objective based on the Sobel variance. The apparent full width half at maximum (FWHM) of the myocardial wall on [13C]bicarbonate was used to estimate blur. Results Mean myocardial wall FWHM measurements were unchanged with the short readout pre‐correction (14.1 ± 2.9 mm) and post‐MFI correction (14.1 ± 3.4 mm), but significantly decreased in the long waveform (20.6 ± 6.6 mm uncorrected, 17.7 ± 7.0 corrected,P= .007). Bicarbonate signal‐to‐noise ratio (SNR) of the images acquired with the long waveform were increased by 1.4 ± 0.3 compared to those acquired with the short waveform (predicted 1.32). Improvement of image quality was observed for all metabolites with correction. Conclusions ‐map correction reduced blur and recovered signal from dropouts, particularly along the posterior myocardial wall. The low imageSNRof [13C]bicarbonate can be compensated with longer duration readouts but at the expense of increased artifacts, which can be partially corrected for with the proposed methods.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".