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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".