Evaluating contrast kinetics by acquiring 2D images during 3D contrast‐enhanced MR angiography
Bibliographic record
Abstract
PURPOSE: To monitor contrast kinetics by acquiring multiple 2D images during 3D contrast-enhanced magnetic resonance angiography (CE MRA). MATERIALS AND METHODS: A 2D real-time autotriggering tool was integrated into a 3D sequence, enabling it to run multiple times during 3D acquisition. Several dummy scans were applied after each transition to maintain the steady state condition of both sequences. The number of the acquired 2D images and their distribution can be adjusted. Each 2D image was saved along with its associated timing. Contrast signal variations over time were plotted, reflecting selective signal measurement over an artery and vein from the saved 2D images. RESULTS: Different contrast kinetics timings were calculated from the resulting plot. Contrast arrival time to the internal cerebral artery was 13.2 +/- 1.2 seconds and the peak arterial to peak venous (at the confluence of sinuses) enhancement was 6.7 +/- 0.6 seconds. The observed timing could be used for 3D sequence optimization; the saved 2D images are useful in detecting and characterizing vascular abnormalities. CONCLUSION: Integrating 2D and 3D sequences into one sequence to monitor contrast kinetics through the neurovasculature is feasible without the need for extra injections or reduced spatial resolution. The technique can also be used in different parts of the body to extract useful clinical information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 |
| 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".