Space–time relationship in continuously moving table method for large FOV peripheral contrast-enhanced magnetic resonance angiography
Bibliographic record
Abstract
Data acquisition using a continuously moving table approach is a method capable of generating large field-of-view (FOV) 3D MR angiograms. However, in order to obtain venous contamination-free contrast-enhanced (CE) MR angiograms in the lower limbs, one of the major challenges is to acquire all necessary k-space data during the restricted arterial phase of the contrast agent. Preliminary investigation on the space-time relationship of continuously acquired peripheral angiography is performed in this work. Deterministic and stochastic undersampled hybrid-space (x, k(y), k(z)) acquisitions are simulated for large FOV peripheral runoff studies. Initial results show the possibility of acquiring isotropic large FOV images of the entire peripheral vascular system. An optimal trade-off between the spatial and temporal sampling properties was found that produced a high-spatial resolution peripheral CE-MR angiogram. The deterministic sampling pattern was capable of reconstructing the global structure of the peripheral arterial tree and showed slightly better global quantitative results than stochastic patterns. Optimal stochastic sampling patterns, on the other hand, enhanced small vessels and had more favourable local quantitative results. These simulations demonstrate the complex spatial-temporal relationship when sampling large FOV peripheral runoff studies. They also suggest that more investigation is required to maximize image quality as a function of hybrid-space coverage, acquisition repetition time and sampling pattern parameters.
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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 | 0.000 | 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) | 0.002 | 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".