Purpose We sought to develop machine learning models to detect multileaf collimator (MLC)
modeling errors with the use of radiomic features of fluence maps measured in patient …

Purpose The aim of this work was to provide a novel description of how the radiotherapy
community configures treatment planning system (TPS) radiation beam models for clinically …

Purpose The purpose of this study was to create and evaluate deep learning‐based models
to detect and classify errors of multi‐leaf collimator (MLC) modeling parameters in volumetric …

Modelling of the multi-leaf collimator (MLC) in treatment planning systems (TPS) is crucial for
the dose calculation accuracy of intensity-modulated radiation therapy plans. However, no …

Purpose To quantify the impact of treatment planning system beam model parameters,
based on the actual spread in radiotherapy community data, on clinical treatment plans and …

Abstract Background Monte Carlo simulation is considered as the most accurate method for
dose calculation in radiotherapy. PRIMO is a Monte-Carlo program with a user-friendly …

Purpose The dosimetric leaf gap (DLG) and multileaf collimator (MLC) transmission are two
important systematic parameters used to model the rounded MLC leaf ends effect when …

Modern treatment machines have shown small inter-unit variability regarding beam data.
Recently, vendor-provided average beam data, such as the Representative Beam Data …

The Halcyon™ platform is self‐contained, combining a treatment planning (Eclipse) system
TPS) with information management and radiation delivery components. The standard TPS …

To optimize the collimator angles in dual-arc volumetric modulated arc therapy (VMAT)
plans for whole-brain radiotherapy with hippocampus and inner ear sparing (HIS-WBRT) …