Unshielded silicon diode detector supported clinical electron beam dosimetry
The SunSILICON detector directly measured electron beam depth doses, profiles, and output factors with close agreement to reference detectors.
The SunSILICON detector directly measured electron beam depth doses, profiles, and output factors with close agreement to reference detectors.
Upright computed tomography showed stable computed tomography numbers and greater than 99% proton dose gamma agreement versus conventional computed tomography.
All eight radiotherapy planning computed tomography scanners showed measurable geometric distortion, with maximum values up to 1.88 millimetres.
An automated report-based chart checker detected residual errors in 37.1% of manually reviewed external beam radiotherapy plans.
Aperture irregularity and modulation complexity score were the strongest plan-complexity predictors of patient-specific quality assurance pass rate.
N4 bias correction with z-score normalization produced the most stable radiomic features on a 0.35 tesla magnetic resonance linear accelerator.
A thick gas electron multiplier air chamber showed strong dose linearity under conventional and ultra-high dose rate conditions.
No single patient-specific quality assurance method covered all stereotactic radiotherapy risks, supporting combinations of measurement, independent calculation, imaging, and monitoring.
Multimodal registration, motion management, and arrhythmia substrate definition were the highest-risk steps in stereotactic arrhythmia radioablation.
A reusable phantom-based test verified simulation-omitted adaptive workflows on cone-beam computed tomography-guided and magnetic resonance-guided treatment systems.
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.
Reference datasets can support linear accelerator beam modelling, but machine-specific measurements remain essential for small fields, complex delivery, and long-term verification.
Anatomy- and dose-based machine learning predicted gamma passing rates accurately for organs at risk, but less reliably for target volumes.