A Pilot Study on Three-Dimensional Visualization of Perforator Flaps by Using Angiography in Cadavers
Bibliographic record
Abstract
BACKGROUND: Perforator flaps have become popular worldwide, in part because of their ability to reliably support a large skin territory on a single perforator. Although the lead oxide injection technique provides excellent images for anatomical study, it is not possible to show the location, course, and direction of the source artery. Materialise's Interactive Medical Image Control System allows microvascular anatomy to be evaluated in three-dimensions to design perforator flaps. METHODS: Two fresh cadavers were injected using the lead oxide-gelatin injection technique. The cadavers were imaged using a spiral computed tomography scanner. The computed tomographic data were transferred to Digital Imaging and Communications in Medicine format and imported to a personal computer. Three-dimensional reconstructions of various parts of the body were then performed using Materialise's Interactive Medical Image Control System software. RESULTS: : Three-dimensional visualization of various parts of the body was obtained. This technique clearly shows the bone, soft tissue, skin, and vascular structures in a layer-by-layer transparent process. The detailed views of the microvasculature provide extensive information regarding the course of vessels in all layers of tissue. CONCLUSIONS: The intricate vascular details captured by this technique clearly demonstrate the three-dimensional anatomy of the integument, bone, and soft tissue in a layer-by-layer transparent process. It is a powerful, quick, easy method with which to demonstrate cadaver vascular anatomy that may be useful in the design of surgical flaps.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".