A 4D visualization tool for treatment planning of non-invasive radioablation in patients with ventricular tachycardia
Bibliographic record
Abstract
Non-invasive cardiac radioablation is an emerging therapy for the treatment of ventricular tachycardia (VT). Electrophysiologic, anatomic and molecular imaging studies are used to localize the breakout region of the VT, but current therapy planning is tedious and prone to error due to a lack of data integration. In this work we present the design and development of a software platform and workflow to facilitate precision-targeted therapy planning, including affine non-rigid multimodality image registration and 2D-3D-4D visualization across modalities. Registration accuracy was measured using Dice Similarity and Hausdorff Distance of total left ventricle tissue volumes, which were 0.914 ± 0.013 and 2.65mm ± 0.34mm, respectively (average ± standard deviation). Electrocardiographic maps of VT parameters were registered temporally to surface electrode data to recreate familiar ECG tracings. 2D polar maps, 3D slice-views, and 4D cine-renderings were used for hybrid fusion displays of molecular and electroanatomic images. Segmentations of the cardiac-gated contrast CT blood-pool and molecular images of perfusion and glucose metabolism were used to identify regions of fibrotic scar tissue and hibernating myocardium in the 3D scene. Ablation targets were painted onto the 2D polar map, 3D slice or 4D-cine views, and exported as DICOM for import to radiotherapy planning software. We anticipate that the combination of accurate multimodality image registration and visualizations will enable more reliable therapy planning, expedite treatment and may improve understanding of the underlying pathophysiology of these lethal arrhythmias.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".