Quantitative evaluation of nonrepetitive phase‐encoding orders for first‐pass, 3D contrast‐enhanced MR angiography
Bibliographic record
Abstract
In this work, a detailed quantitative comparison was made of many alternative phase-encoding strategies for first-pass 3D MR angiography where each phase encode is only sampled once during the transient passage of contrast agent. A series of standard sequential and centric phase-encoding orders including elliptical centric were tested, as well as a new order called elliptical sequential. The characteristics of the different phase-encoding orders were tested using a computer simulation followed by experimental verification using a variable flip angle scheme. The characteristics to be considered included: arterial intensity, arterial-to-venous contrast, degree of artifact, and the blurring of the point-spread function. By making use of a wide range of start times and a rapidly varying contrast curve, the quantitative results clearly indicate the widely varying merits of each phase-encoding order. In general, when an optimal start time is used techniques that sample the low k-space views most compactly will produce the best results; however, the same methods are more problematic when the bolus arrival time deviates substantially from that expected.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".