Comparison of maximum intensity projection and digitally reconstructed radiographic projection for carotid artery stenosis measurement
Bibliographic record
Abstract
Digital subtraction angiography is being supplanted by three-dimensional imaging techniques in many clinical applications, leading to extensive use of maximum intensity projection (MIP) images to depict volumetric vascular data. The MIP algorithm produces intensity profiles that are different than conventional angiograms, and can also increase the vessel-to-tissue contrast-to-noise ratio. We evaluated the effect of the MIP algorithm in a clinical application where quantitative vessel measurement is important: internal carotid artery stenosis grading. Three-dimensional computed rotational angiography (CRA) was performed on 26 consecutive symptomatic patients to verify an internal carotid artery stenosis originally found using duplex ultrasound. These volumes of data were visualized using two different postprocessing projection techniques: MIP and digitally reconstructed radiographic (DRR) projection. A DRR is a radiographic image simulating a conventional digitally subtracted angiogram, but it is derived computationally from the same CRA dataset as the MIP. By visualizing a single volume with two different projection techniques, the postprocessing effect of the MIP algorithm is isolated. Vessel measurements were made, according to the NASCET guidelines, and percentage stenosis grades were calculated. The paired t-test was used to determine if the measurement difference between the two techniques was statistically significant. The CRA technique provided an isotropic voxel spacing of 0.38 mm. The MIPs and DRRs had a mean signal-difference-to-noise-ratio of 30:1 and 26:1, respectively. Vessel measurements from MIPs were, on average, 0.17 mm larger than those from DRRs (P < 0.0001). The NASCET-type stenosis grades tended to be underestimated on average by 2.4% with the MIP algorithm, although this was not statistically significant (P=0.09). The mean interobserver variability (standard deviation) of both the MIP and DRR images was 0.35 mm. It was concluded that the MIP algorithm slightly increased the apparent dimensions of the arteries, when applied to these intra-arterial CRA images. This subpixel increase was smaller than both the voxel size and interobserver variability, and was therefore not clinically relevant.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".