Comparison of matched‐filtered two‐dimensional projection and elliptical centric‐ordered three‐dimensional contrast‐enhanced magnetic resonance angiography
Bibliographic record
Abstract
PURPOSE: To compare the image quality of matched-filtered two-dimensional projection magnetic resonance angiography (MRA) and elliptical centric-ordered (EC) three-dimensional MRA. MATERIALS AND METHODS: Signal-to-noise ratios (SNRs) of matched-filtered two-dimensional projection and EC three-dimensional MRA are developed theoretically and compared by clinical studies, in which 10-20 mL of gadolinium (Gd) was injected at 1.5 mL/second. The artery-vein contrast in two-dimensional projection MRA was managed by manually selecting specific templates for the matched filters. RESULTS: The SNR of matched-filtered two-dimensional projection MRA is superior to that of EC three-dimensional MRA for vessels wider than one pixel due to the integral effect. The artery-vein contrast can be managed flexibly in two-dimensional projection MRA by choosing different templates for the matched filter, while the artery-vein contrast in EC three-dimensional MRA is solely determined by the timing to start the acquisition. CONCLUSION: Matched-filtered two-dimensional projection MRA provides comparable image quality and is a flexible alternative to EC three-dimensional MRA in applications where contrast timing is difficult and temporal information is of interest.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".