AIMC Topic: Radiometry

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DoseGAN: a generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation.

Scientific reports Jul 6, 2020
Deep learning algorithms have recently been developed that utilize patient anatomy and raw imaging information to predict radiation dose, as a means to increase treatment planning efficiency and improve radiotherapy plan quality. Current state-of-the...
Image Processing, Computer-Assisted Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiometry Humans Neural Networks, Computer Algorithms Neoplasms
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Boosting radiotherapy dose calculation accuracy with deep learning.

Journal of applied clinical medical physics Jun 19, 2020
In radiotherapy, a trade-off exists between computational workload/speed and dose calculation accuracy. Calculation methods like pencil-beam convolution can be much faster than Monte-Carlo methods, but less accurate. The dose difference, mostly cause...
Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Radiometry Humans Algorithms Deep Learning
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Dosimetry-Driven Quality Measure of Brain Pseudo Computed Tomography Generated From Deep Learning for MRI-Only Radiation Therapy Treatment Planning.

International journal of radiation oncology, biology, physics May 14, 2020
PURPOSE: This study aims to evaluate the impact of key parameters on the pseudo computed tomography (pCT) quality generated from magnetic resonance imaging (MRI) with a 3-dimensional (3D) convolutional neural network.
Brain Neoplasms Neural Networks, Computer Deep Learning Brain Magnetic Resonance Imaging Contrast Media Retrospective Studies Radiotherapy Humans Tomography, X-Ray Computed Skull Radiometry
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Comparison of CBCT based synthetic CT methods suitable for proton dose calculations in adaptive proton therapy.

Physics in medicine and biology Apr 28, 2020
In-room imaging is a prerequisite for adaptive proton therapy. The use of onboard cone-beam computed tomography (CBCT) imaging, which is routinely acquired for patient position verification, can enable daily dose reconstructions and plan adaptation d...
Radiotherapy Dosage Proton Therapy Radiometry Neural Networks, Computer Female Image Processing, Computer-Assisted Aged Head and Neck Neoplasms Aged, 80 and over Radiotherapy Planning, Computer-Assisted Adult Male Middle Aged Cone-Beam Computed Tomography Humans
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Conventional vs machine learning-based treatment planning in prostate brachytherapy: Results of a Phase I randomized controlled trial.

Brachytherapy Apr 19, 2020
PURPOSE: The purpose of this study was to evaluate the noninferiority of Day 30 dosimetry between a machine learning-based treatment planning system for prostate low-dose-rate (LDR) brachytherapy and the conventional, manual planning technique. As a ...
Aged Male Middle Aged Time Factors Humans Machine Learning Radiotherapy Planning, Computer-Assisted Radiation Dosage Postoperative Period Brachytherapy Prostatic Neoplasms Rectum Workflow Iodine Radioisotopes Radiotherapy Dosage Radiometry
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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...
Particle Accelerators Neural Networks, Computer Radiation Dosage Humans Radiometry Photons
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Learning-based estimation of dielectric properties and tissue density in head models for personalized radio-frequency dosimetry.

Physics in medicine and biology Mar 11, 2020
Radio-frequency dosimetry is an important process in assessments for human exposure safety and for compliance of related products. Recently, computational human models generated from medical images have often been used for such assessment, especially...
Humans Head Radiometry Machine Learning Radio Waves Image Processing, Computer-Assisted Magnetic Resonance Imaging
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A Deep Learning-based correction to EPID dosimetry for attenuation and scatter in the Unity MR-Linac system.

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) Mar 2, 2020
PURPOSE: EPID dosimetry in the Unity MR-Linac system allows for reconstruction of absolute dose distributions within the patient geometry. Dose reconstruction is accurate for the parts of the beam arriving at the EPID through the MRI central unattenu...
Rectal Neoplasms Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Scattering, Radiation Phantoms, Imaging Radiometry Reproducibility of Results Deep Learning Image Processing, Computer-Assisted Humans Magnetic Resonance Imaging Algorithms
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Machine learning helps identifying volume-confounding effects in radiomics.

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) Feb 20, 2020
Highlighting the risk of biases in radiomics-based models will help improve their quality and increase usage as decision support systems in the clinic. In this study we use machine learning-based methods to identify the presence of volume-confounding...
Carcinoma, Non-Small-Cell Lung Cluster Analysis Regression Analysis Female Algorithms Humans Carcinoma, Squamous Cell Radiometry Principal Component Analysis Databases, Factual Decision Support Systems, Clinical Machine Learning Aged Laryngeal Neoplasms Retrospective Studies Aged, 80 and over Software Lung Neoplasms Tomography, X-Ray Computed Oropharyngeal Neoplasms Adult Squamous Cell Carcinoma of Head and Neck Male Middle Aged
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Deep DoseNet: a deep neural network for accurate dosimetric transformation between different spatial resolutions and/or different dose calculation algorithms for precision radiation therapy.

Physics in medicine and biology Feb 4, 2020
The purpose of this work is to introduce a novel deep learning strategy to obtain highly accurate dose plan by transforming from a dose distribution calculated using a low-cost algorithm (or algorithmic settings). 25 168 slices of dose distribution a...
Anisotropy Neural Networks, Computer Algorithms Neoplasms Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Radiometry Humans
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