High‐resolution imaging of the intracranial arterial and venous systems following a single contrast injection
Bibliographic record
Abstract
PURPOSE: To generate two separate three-dimensional (3D) high spatial resolution images of the intracranial arterial and venous systems using a single contrast injection. MATERIALS AND METHODS: A 3D contrast-enhanced (CE) magnetic resonance angiography (MRA) acquisition was modified to create two separate k-space data sets to encode the arterial and venous enhancement signals individually after contrast agent injection. Following an automated detection of contrast arrival, the central k-space views corresponding to the arterial phase were acquired for the first eight seconds. A full elliptical-centric acquisition was then acquired for the venous phase and the missing views in the periphery of the first k-space data set were copied from the venous phase. A total of 18 patients underwent this study. Image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were determined in both intracranial systems. RESULTS: Two 3D image sets were generated for the arterial and venous intracranial systems. Both sets have high quality images that are clinically diagnostic. SNR and CNR were high in both sets, so that all the major vessels were visible. CONCLUSION: This technique provides images with high spatial resolution for both arterial and venous intracranial systems using a single contrast injection.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".