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Commissioning Monte Carlo algorithm for robotic radiosurgery using cylindrical 3D-array with variable density inserts.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
INTRODUCTION: To commission the Monte Carlo (MC) algorithm based model of CyberKnife robotic stereotactic system (CK) and evaluate the feasibility of patient specific QA using the ArcCHECK cylindrical 3D-array (AC) with Multiplug inserts (MP).

Deep nets vs expert designed features in medical physics: An IMRT QA case study.

Medical physics
PURPOSE: The purpose of this study was to compare the performance of Deep Neural Networks against a technique designed by domain experts in the prediction of gamma passing rates for Intensity Modulated Radiation Therapy Quality Assurance (IMRT QA).

Temporal separation of Cerenkov radiation and scintillation using artificial neural networks in Clinical LINACs.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
The irradiation of scintillator-fiber optic dosimeters by clinical LINACs results in the measurement of scintillation and Cerenkov radiation. In scintillator-fiber optic dosimetry, the scintillation and Cerenkov radiation responses are separated to d...

Temporal separation of Cerenkov radiation and scintillation using a clinical LINAC and artificial intelligence.

Physics in medicine and biology
Convolutional neural network (CNN) type artificial intelligences were trained to estimate the Cerenkov radiation present in the temporal response of a LINAC irradiated scintillator-fiber optic dosimeter. The CNN estimate of Cerenkov radiation is subt...

Machine learning for automated quality assurance in radiotherapy: A proof of principle using EPID data description.

Medical physics
PURPOSE: Developing automated methods to identify task-driven quality assurance (QA) procedures is key toward increasing safety, efficacy, and efficiency. We investigate the use of machine learning (ML) methods for possible visualization, automation,...

Predicting VMAT patient-specific QA results using a support vector classifier trained on treatment plan characteristics and linac QC metrics.

Physics in medicine and biology
The use of treatment plan characteristics to predict patient-specific quality assurance (QA) measurement results has recently been reported as a strategy to help facilitate automated pre-treatment verification workflows or to provide a virtual assess...

Use of artificial neural network for pretreatment verification of intensity modulation radiation therapy fields.

The British journal of radiology
OBJECTIVE: The accuracy of dose delivery for intensity modulated radiotherapy (IMRT) treatments should be determined by an accurate quality assurance procedure. In this work, we used artificial neural networks (ANNs) as an application for the pre-tre...

Generative adversarial networks (GAN) for compact beam source modelling in Monte Carlo simulations.

Physics in medicine and biology
A method is proposed and evaluated to model large and inconvenient phase space files used in Monte Carlo simulations by a compact generative adversarial network (GAN). The GAN is trained based on a phase space dataset to create a neural network, call...

An artificial neural network to model response of a radiotherapy beam monitoring system.

Medical physics
PURPOSE: The integral quality monitor (IQM) is a real-time radiotherapy beam monitoring system, which consists of a spatially sensitive large-area ion chamber, mounted at the collimator of the linear accelerator (linac), and a calculation algorithm t...