Four-dimensional endocardial surface imaging with dynamic virtual reality rendering: a technical note
Bibliographic record
Abstract
Open heart surgery requires a proper understanding of the endocardial surface of the heart and vascular structures. While modern four-dimensional (4D) imaging enables excellent dynamic visualization of the blood pool, endocardial surface anatomy has not routinely been assessed. 4D image data were post-processed using commercially available virtual reality (VR) software. Using thresholding, the blood pool was segmented dynamically across the imaging volume. The segmented blood pool was further edited for correction of errors due to artifacts or inhomogeneous signal intensity. Then, a surface shell of an even thickness was added to the edited blood pool. When the cardiac valve leaflets and chordae were visualized, they were segmented separately using a different range of signal intensity for thresholding. Using an interactive cutting plane, the endocardial surface anatomy was reviewed from multiple perspectives by interactively applying a cutting plane, rotating and moving the model. In conclusions, dynamic three-dimensional (3D) endocardial surface imaging is feasible and provides realistic simulated views of the intraoperative scenes at open heart surgery. As VR is based on the use of all fingers of both hands, the efficiency and speed of postprocessing are markedly enhanced. Although it is limited, visualization of the cardiac valve leaflets and chordae is also possible.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".