AIMC Topic: Radiometry

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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.
Humans Neural Networks, Computer Brain Retrospective Studies Magnetic Resonance Imaging Radiotherapy Skull Radiometry Tomography, X-Ray Computed Brain Neoplasms Deep Learning Contrast Media
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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...
Head and Neck Neoplasms Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Proton Therapy Middle Aged Cone-Beam Computed Tomography Image Processing, Computer-Assisted Radiometry Humans Neural Networks, Computer Female Aged Aged, 80 and over Adult Male
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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 ...
Brachytherapy Radiotherapy Dosage Time Factors Radiometry Prostatic Neoplasms Rectum Workflow Iodine Radioisotopes Radiotherapy Planning, Computer-Assisted Radiation Dosage Postoperative Period Machine Learning Aged Male Middle Aged Humans
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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...
Humans Neural Networks, Computer Radiation Dosage Radiometry Photons Particle Accelerators
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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...
Magnetic Resonance Imaging Head Radiometry Radio Waves Humans Machine Learning Image Processing, Computer-Assisted
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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...
Image Processing, Computer-Assisted Humans Radiotherapy, Intensity-Modulated Algorithms Scattering, Radiation Magnetic Resonance Imaging Reproducibility of Results Phantoms, Imaging Rectal Neoplasms Deep Learning Radiotherapy Planning, Computer-Assisted Radiometry
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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...
Radiometry Aged Laryngeal Neoplasms Aged, 80 and over Oropharyngeal Neoplasms Humans Adult Carcinoma, Non-Small-Cell Lung Machine Learning Male Cluster Analysis Software Middle Aged Tomography, X-Ray Computed Regression Analysis Algorithms Decision Support Systems, Clinical Principal Component Analysis Female Databases, Factual Lung Neoplasms Carcinoma, Squamous Cell Retrospective Studies Squamous Cell Carcinoma of Head and Neck
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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...
Neoplasms Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Humans Neural Networks, Computer Algorithms Radiometry Anisotropy
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A novel deep learning model using dosimetric and clinical information for grade 4 radiotherapy-induced lymphopenia prediction.

Physics in medicine and biology Feb 4, 2020
Radiotherapy-induced lymphopenia has increasingly been shown to reduce cancer survivorship. We developed a novel hybrid deep learning model to efficiently integrate an entire set of dosimetric parameters of a radiation treatment plan with a patient's...
Radiotherapy Radiation Injuries Radiometry Lymphopenia Humans Deep Learning Models, Statistical Esophageal Neoplasms
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Feasibility of two-dimensional dose distribution deconvolution using convolution neural networks.

Medical physics Nov 6, 2019
PURPOSE: The purpose of this study was to investigate the feasibility of two-dimensional (2D) dose distribution deconvolution using convolutional neural networks (CNNs) instead of an analytical approach for an in-house scintillation detector that has...
Radiometry Radiotherapy Planning, Computer-Assisted Humans Radiation Dosage Neural Networks, Computer Radiotherapy Dosage Feasibility Studies Radiotherapy, Intensity-Modulated
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