Left ventricle segment-specific motion assessment for cardiac-gated radiosurgery
Bibliographic record
Abstract
Abstract Purpose. Cardiac radiosurgery is a non-invasive treatment modality for ventricular tachycardia, where a linear accelerator is used to irradiate the arrhythmogenic region within the heart. In this work, cardiac magnetic resonance (CMR) cine images were used to quantify left ventricle (LV) segment-specific motion during the cardiac cycle and to assess potential advantages of cardiac-gated radiosurgery. Methods. CMR breath-hold cine images and LV contour points were analyzed for 50 controls and 50 heart failure patients with reduced ejection fraction (HFrEF, EF < 40%). Contour points were divided into anatomic segments according to the 17-segment model, and each segment was treated as a hypothetical treatment target. The optimum treatment window (one fifth of the cardiac cycle) was determined where segment centroid motion was minimal, then the maximum centroid displacement and treatment area were determined for the full cardiac cycle and for the treatment window. Mean centroid displacement and treatment area reductions with cardiac gating were determined for each of the 17 segments. Results. Full motion segment centroid displacements ranged between 6–14 mm (controls) and 4–11 mm (HFrEF). Full motion treatment areas ranged between 129–715 mm2 (controls) and 149–766 mm2 (HFrEF). With gating, centroid displacements were reduced to 1 mm (controls and HFrEF), while treatment areas were reduced to 62–349 mm2 (controls) and 83–393 mm2 (HFrEF). Relative treatment area reduction ranged between 38%–53% (controls) and 26%–48% (HFrEF). Conclusion. This data demonstrates that cardiac cycle motion is an important component of overall target motion and varies depending on the anatomic cardiac segment. Accounting for cardiac cycle motion, through cardiac gating, has the potential to significantly reduce treatment volumes for cardiac radiosurgery.
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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) | 0.001 | 0.000 |
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".