Multimodal registration, motion management, and arrhythmia substrate definition were the highest-risk steps in stereotactic arrhythmia radioablation.
The neural model reduced phase-space storage from 3 gigabytes to 600 kilobytes while maintaining at least 89.9% gamma passing at 1%/1 mm.
The framework achieved 85.5% external-validation accuracy and reduced estimated review time from 822 hours to 2.3–4.8 hours.
Absorbed dose near the applicator varied markedly by tissue composition despite accurate Monte Carlo calculation by the treatment planning system.
A reusable phantom-based test verified simulation-omitted adaptive workflows on cone-beam computed tomography-guided and magnetic resonance-guided treatment systems.
CyberKnife improved conformity and reduced cardiac, pulmonary, and low-dose exposure, while noncoplanar linac techniques remained clinically acceptable alternatives.
A conventional-fractionation model produced clinically acceptable moderately hypofractionated prostate plans, with comparable overall quality and slightly reduced target homogeneity.
Lot-to-lot density variation in lung-equivalent inserts reduced planning target volume coverage by up to 3%, exceeding the stated clinical tolerance.
Discrete pulse counts limited fractional monitor unit precision, particularly for high-dose-per-pulse flattening filter-free beams.
Hydrogen peroxide production decreased as proton dose rate increased, with Geant4-DNA simulations reproducing the oxygen-dependent experimental trend.
Patient-specific surface imaging reconstructed breast radiotherapy dose with a 93.8% gamma passing rate and 42-millisecond latency without additional imaging radiation.
Reference datasets can support linear accelerator beam modelling, but machine-specific measurements remain essential for small fields, complex delivery, and long-term verification.
Prompt-gamma profiles tracked beam range, but stronger neutron backgrounds reduced retrieval precision for carbon ions under clinically relevant conditions.
Measured multi-energy extraction characteristics enabled accurate delivery-time prediction across 605 clinical proton fields, supporting more reliable interplay simulations.
Anatomy- and dose-based machine learning predicted gamma passing rates accurately for organs at risk, but less reliably for target volumes.