AIMC Topic: Particle Accelerators
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Last vertex splitting: a new retroactive Monte Carlo splitting technique applied to LINAC out-of-field dose computation.
Physics in medicine and biology
Aug 1, 2025
We propose a new variance reduction technique called last vertex splitting (LVS) designed to reduce computation time in Monte Carlo (MC) simulations for particles traversing high-attenuating media, such as the collimator and other beam-limiting devic...
Deep-learning-based linac beam modelling with sparse beam data measurements.
Physics in medicine and biology
Jul 16, 2025
This paper introduces linac beam modelling network (LBMnet), a deep-learning-based approach for efficient linac beam modelling, generating percentage depth dose (PDD) and beam profiles by predicting beam data from sparse single-field measurements, th...
Portal dose image prediction using Monte Carlo generated transmission energy fluence maps of dynamic radiotherapy treatment plans: a deep learning approach.
Biomedical physics & engineering express
Apr 9, 2025
This work aims to develop and investigate the feasibility of a hybrid model combining Monte Carlo (MC) simulations and deep learning (DL) to predict electronic portal imaging device (EPID) images based on MC-generated exit phase space energy fluence ...
Advanced prediction of multi-leaf collimator leaf position using artificial neural network.
Medical physics
Feb 18, 2025
BACKGROUND: Multi-leaf collimators (MLCs) are crucial for modern radiotherapy as they ensure precise target irradiation through accurate leaf positioning. Accurate prediction of MLC leaf positions is vital for the effectiveness and safety of treatmen...
MR-linac: role of artificial intelligence and automation.
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
Jan 22, 2025
The integration of artificial intelligence (AI) into radiotherapy has advanced significantly during the past 5 years, especially in terms of automating key processes like organ at risk delineation and treatment planning. These innovations have enhanc...
Artificial intelligence for treatment delivery: image-guided radiotherapy.
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
Aug 13, 2024
Radiation therapy (RT) is a highly digitized field relying heavily on computational methods and, as such, has a high affinity for the automation potential afforded by modern artificial intelligence (AI). This is particularly relevant where imaging is...
From plan to delivery: Machine learning based positional accuracy prediction of multi-leaf collimator and estimation of delivery effect in volumetric modulated arc therapy.
Journal of applied clinical medical physics
Jun 20, 2024
PURPOSE: The positional accuracy of MLC is an important element in establishing the exact dosimetry in VMAT. We comprehensively analyzed factors that may affect MLC positional accuracy in VMAT, and constructed a model to predict MLC positional deviat...
Assessment of the deep learning-based gamma passing rate prediction system for 1.5 T magnetic resonance-guided linear accelerator.
Radiological physics and technology
Apr 30, 2024
Measurement-based verification is impossible for the patient-specific quality assurance (QA) of online adaptive magnetic resonance imaging-guided radiotherapy (oMRgRT) because the patient remains on the couch throughout the session. We assessed a dee...
Custom-Trained Deep Learning-Based Auto-Segmentation for Male Pelvic Iterative CBCT on C-Arm Linear Accelerators.
Practical radiation oncology
Feb 6, 2024
PURPOSE: The purpose of this investigation was to evaluate the clinical applicability of a commercial artificial intelligence-driven deep learning auto-segmentation (DLAS) tool on enhanced iterative cone beam computed tomography (iCBCT) acquisitions ...
Performance assessment of variant UNet-based deep-learning dose engines for MR-Linac-based prostate IMRT plans.
Physics in medicine and biology
Aug 11, 2023
. UNet-based deep-learning (DL) architectures are promising dose engines for traditional linear accelerator (Linac) models. Current UNet-based engines, however, were designed differently with various strategies, making it challenging to fairly compar...
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