AIMC Topic: Particle Accelerators
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Robust deep learning-based forward dose calculations for VMAT on the 1.5T MR-linac.
Physics in medicine and biology
Nov 18, 2022
In this work we present a framework for robust deep learning-based VMAT forward dose calculations for the 1.5T MR-linac. A convolutional neural network was trained on the dose of individual multi-leaf-collimator VMAT segments and was used to predict ...
Machine learning models to predict the delivered positions of Elekta multileaf collimator leaves for volumetric modulated arc therapy.
Journal of applied clinical medical physics
Jun 7, 2022
PURPOSE: Accurate positioning of multileaf collimator (MLC) leaves during volumetric modulated arc therapy (VMAT) is essential for accurate treatment delivery. We developed a linear regression, support vector machine, random forest, extreme gradient ...
Deep learning-based 3Ddose reconstruction with an electronic portal imaging device for magnetic resonance-linear accelerators: a proof of concept study.
Physics in medicine and biology
Dec 2, 2021
To develop a novel deep learning-based 3Ddose reconstruction framework with an electronic portal imaging device (EPID) for magnetic resonance-linear accelerators (MR-LINACs).The proposed method directly back-projected 2D portal dose into 3D patient c...
DeepWL: Robust EPID based Winston-Lutz analysis using deep learning, synthetic image generation and optical path-tracing.
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)
Sep 4, 2021
Radiation therapy requires clinical linear accelerators to be mechanically and dosimetrically calibrated to a high standard. One important quality assurance test is the Winston-Lutz test which localises the radiation isocentre of the linac. In the cu...
DeepBeam: a machine learning framework for tuning the primary electron beam of the PRIMO Monte Carlo software.
Radiation oncology (London, England)
Jun 29, 2021
BACKGROUND: Any Monte Carlo simulation of dose delivery using medical accelerator-generated megavolt photon beams begins by simulating electrons of the primary electron beam interacting with a target. Because the electron beam characteristics of any ...
Deep learning-based synthetic CT generation for MR-only radiotherapy of prostate cancer patients with 0.35T MRI linear accelerator.
Journal of applied clinical medical physics
Jun 28, 2021
PURPOSE: To develop a deep learning model to generate synthetic CT for MR-only radiotherapy of prostate cancer patients treated with 0.35 T MRI linear accelerator.
AAPM Task Group 264: The safe clinical implementation of MLC tracking in radiotherapy.
Medical physics
Mar 23, 2021
The era of real-time radiotherapy is upon us. Robotic and gimbaled linac tracking are clinically established technologies with the clinical realization of couch tracking in development. Multileaf collimators (MLCs) are a standard equipment for most c...
DeepDose: a robust deep learning-based dose engine for abdominal tumours in a 1.5 T MRI radiotherapy system.
Physics in medicine and biology
Mar 9, 2021
We present a robust deep learning-based framework for dose calculations of abdominal tumours in a 1.5 T MRI radiotherapy system. For a set of patient plans, a convolutional neural network is trained on the dose of individual multi-leaf-collimator seg...
Radiotherapy, Intensity-Modulated
Monte Carlo Method
Abdominal Neoplasms
Particle Accelerators
Male
Deep Learning
Radiotherapy Planning, Computer-Assisted
Prostatic Neoplasms
Magnetic Resonance Imaging
Neoplasm Metastasis
Humans
Rectal Neoplasms
Radiotherapy Dosage
Neural Networks, Computer
Reproducibility of Results
DeepDose: Towards a fast dose calculation engine for radiation therapy using deep learning.
Physics in medicine and biology
Apr 8, 2020
We present DeepDose, a deep learning framework for fast dose calculations in radiation therapy. Given a patient anatomy and linear-accelerator IMRT multi-leaf-collimator shape or segment, a novel set of physics-based inputs is calculated that encode ...
Evaluation of a neural network-based photon beam profile deconvolution method.
Journal of applied clinical medical physics
Mar 30, 2020
PURPOSE: The authors have previously shown the feasibility of using an artificial neural network (ANN) to eliminate the volume average effect (VAE) of scanning ionization chambers (ICs). The purpose of this work was to evaluate the method when applie...
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