Frequency correction method for improved spatial correlation of hyperpolarized <sup>13</sup>C metabolites and anatomy
Bibliographic record
Abstract
Blip-reversed echo-planar imaging (EPI) is investigated as a method for measuring and correcting the spatial shifts that occur due to bulk frequency offsets in (13)C metabolic imaging in vivo. By reversing the k-space trajectory for every other time point, the direction of the spatial shift for a given frequency is reversed. Here, mutual information is used to find the 'best' alignment between images and thereby measure the frequency offset. Time-resolved 3D images of pyruvate/lactate/urea were acquired with 5 s temporal resolution over a 1 min duration in rats (N = 6). For each rat, a second injection was performed with the demodulation frequency purposely mis-set by +35 Hz, to test the correction for erroneous shifts in the images. Overall, the shift induced by the 35 Hz frequency offset was 5.9 ± 0.6 mm (mean ± standard deviation). This agrees well with the expected 5.7 mm shift based on the 2.02 ms delay between k-space lines (giving 30.9 Hz per pixel). The 0.6 mm standard deviation in the correction corresponds to a frequency-detection accuracy of 4 Hz. A method was presented for ensuring the spatial registration between (13)C metabolic images and conventional anatomical images when long echo-planar readouts are used. The frequency correction method was shown to have an accuracy of 4 Hz. Summing the spatially corrected frames gave a signal-to-noise ratio (SNR) improvement factor of 2 or greater, compared with the highest single frame.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".