Margins to account for cardiac and respiratory motion in cardiac radioablation
Bibliographic record
Abstract
Abstract Background Cardiac radioablation (CR) is an emerging treatment for ventricular tachycardia, a rapid abnormal heart rhythm. Effectively delivering radiation to CR targets requires understanding and accounting for geometric uncertainties. One important uncertainty is motion induced by the cardiac and respiratory cycles, which can be accounted for by expanding the targeted region by a margin accounting for the motion's effect on dosimetry. Purpose To investigate margins to account for cardiac and respiratory motions in CR and compare different methods of computing these margins. Methods Eighty four hundred cardiorespiratory motion traces were created by joining 1050 cardiac motions derived from 30 Hz magnetic resonance images with eight respiratory motions from 5 Hz bi‐planar kV fluoroscopy. Cardiac motions for each of the 17 segments of the left ventricle were acquired for 50 heart failure patients with a reduced ejection fraction. Respiratory motions were derived from the implantable cardioverter defibrillator lead's tip for eight CR patients. The margins needed to account for random errors were found using the convolution method by blurring a dose penumbra (Gaussian fall‐off = 3.2 mm) with the motion. The motion margin was computed as the shift in the 95% dose level after blurring. Since these dosimetric margins do not consider rotations and shape deformation, they are considered a lower limit to account for cardiorespiratory motions. These motion margins were compared to (i) a sum of cardiac and respiratory motion amplitudes, similar to using an internal target volume (ITV); (ii) the van Herk et al. margin formula (MF = ); and (iii) the amplitude of respiratory motion alone, similar to using a respiratory ITV. Results The sum of cardiac and respiratory motion amplitudes significantly overestimated the motion margins by [2.2±0.7 right‐left, 2.6±0.9 ant‐post, 2.7±0.7 inf‐sup] mm. The margin formula accurately calculated the motion margins with average differences from the convolution method of [0.00±0.06, 0.0±0.1, 0.0±0.1] mm. Accounting for the amplitude of respiratory motion alone was on average sufficient but not robust, as it could underestimate the motion margin by up to 5 mm. Conclusions Margins to account for cardiorespiratory motion in CR can be calculated using a margin formula. The conservative approach of accounting for the amplitude of cardiorespiratory motion can significantly overestimate the needed margin which may result in excess healthy tissue damage.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".