<title>Perspective maximum-intensity projection of CRA data</title>
Bibliographic record
Abstract
Three-dimensional (3D) computed rotational angiography (CRA) using an X-ray image intensifier based C-arm imaging system is a relatively new modality useful in cerebral angiography and neuro-interventional surgical procedures. In this paper, we present and study several algorithms for estimating the perspective maximum intensity projection (MIP) -- PMIP -- of CRA (neuro-angiographic) data. The nature of the data, intended applications and the associated approximations differ from those in spiral CT and MR angiography (in which parallel MIP is used extensively). Of relevance is perspective geometry, the intrinsic parameters associated with which are those of the C-arm imaging system. Its relatively small X-ray cone-angle makes the voxel driven method viable for certain applications. Consequently, the voxel-driven method is considered in greater detail. Sparseness of neuro-angiographic data allows thresholding, and hence acceleration. This feature is useful for a rapid preview and as an aid to 2D/3D registration. Post-processing for improving the quality of voxel-driven PMIP is discussed, and an adaptive algorithm is proposed in the context. Results of PMIP of data acquired by scanning patients with a prototype C-arm system are presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.053 | 0.022 |
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".