Four Dimensional Intravenous Cone-Beam Computed Tomographic Subtraction Angiography
Bibliographic record
Abstract
OBJECTIVE: We demonstrate the feasibility of 4D intravenous computed tomographic (CT) subtraction cerebral angiography using in vitro, anthropomorphic techniques. MATERIALS AND METHODS: High-resolution 3D cone-beam CT datasets (0.45 mm isotropic voxel size, 120 kVp, 90 mA) of a cadaver-derived cerebrovascular phantom, containing a saccular aneurysm, were acquired at a rate of 1 Hz for 20 seconds. A computer-controlled pump provided physiologically realistic blood-flow waveforms using a water-glycerol blood-mimicking fluid (10 mL/s mean flow). Contrast agent injected at 0.94 mL/s for 5 seconds provided a clinically realistic intravenous vascular enhancement of approximately 300 Hounsfield units. The first 4 to 5 volumes (precontrast) provided a mask dataset for volumetric subtraction. Vascular enhancement was measured in the dynamic, time-resolved, subtracted 3D angiograms. Contrast-to-noise ratio was measured in 3D source data and maximum intensity projections (MIPs). Dose measurements were made using an ionization chamber. RESULTS: MIP images of the time-resolved volumetric data were of diagnostic quality, clearly showing the aneurysm dome and neck, and cerebral vessels. Dynamic flow information (contrast wash-in/wash-out) was observed, including differential opacification and draining of the anterior and posterior vasculature and the aneurysm. Contrast-to-noise ratio was measured to be in the range of 3 to 4.5 in averaged volumes, and 10.5 to 17 in the corresponding MIPs, at an effective patient dose of 2.8 mSv, with 4 cm of axial coverage. CONCLUSIONS: We have demonstrated the feasibility of 4D volumetric, intravenous CT subtraction angiography, in vitro, providing time-resolved, diagnostic quality 3D datasets. We were able to show time-resolved blood-flow information and high-resolution local and global anatomic renderings, from a single 20-second scan, at acceptable x-ray dose.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".