Blood Velocity Calculated From Volumetric Dynamic Computed Tomography Angiography
Bibliographic record
Abstract
OBJECTIVE: Though functional intravascular parameters such as blood velocity and direction of blood flow are available via imaging modalities such as Doppler ultrasound and phase contrast magnetic resonance imaging, such quantitative information is not yet supported by computed tomography (CT). In this study, we examined a method to calculate intra-arterial blood velocity from contrast-enhanced dynamic CT angiography (4D CTA) using the unique advantages of a volumetric 320 detector row scanner. MATERIALS AND METHODS: Contrast boluses were injected into a flow phantom under volumetric 4D CTA examination. Two pipe diameters were tested, each diameter with 4 various flow velocities, creating 8 independent flow conditions. The internal carotid arteries of 5 patients (10 arteries in total) were subjected to a similar dynamic CTA examination and reconstructed with a 1-second temporal resolution through the arterial phase. Intraluminal velocities were calculated using distance between 2 regions of interest placed proximally and distally over the vessel, divided by delay in time to peak of contrast arrival in each region of interest. Results were compared with flow velocities attained by quantitative magnetic resonance angiography in vivo. RESULTS: Phantom experiments demonstrate reasonable agreement between calculated and measured intraluminal velocity (P = 0.05). Similarly, in vivo blood velocity calculations in all internal carotid arteries show agreement with results attained by quantitative magnetic resonance angiography. CONCLUSIONS: Intraluminal blood velocity may be estimated from first-pass contrast bolus profiles acquired via volumetric 4D CTA examinations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".