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Radiometry

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Geometric and Dosimetric Evaluation of a RayStation Deep Learning Model for Auto-Segmentation of Organs at Risk in a Real-World Head and Neck Cancer Dataset.

Clinical oncology (Royal College of Radiologists (Great Britain))
AIMS: To assess geometric accuracy and dosimetric impact of a deep learning segmentation (DLS) model on a large, diverse dataset of head and neck cancer (HNC) patients treated with intensity-modulated proton therapy (IMPT).

Introduction of a hybrid approach based on statistical shape model and Adaptive Neural Fuzzy Inference System (ANFIS) to assess dosimetry uncertainty: A Monte Carlo study.

Computers in biology and medicine
The increasing use of ionizing radiation has raised concerns about adverse and long-term health risks for individuals. Therefore, to evaluate the range of risks and protection against ionizing radiation, it is necessary to assess the dosimetry calcul...

Deep learning-based segmentation of head and neck organs at risk on CBCT images with dosimetric assessment for radiotherapy.

Physics in medicine and biology
Cone beam computed tomography (CBCT) has become an essential tool in head and neck cancer (HNC) radiotherapy (RT) treatment delivery. Automatic segmentation of the organs at risk (OARs) on CBCT can trigger and accelerate treatment replanning but is s...

Initial characterization of a novel dual-robot orthovoltage radiotherapy system.

Biomedical physics & engineering express
Adequate access to radiotherapy is a critical global concern affecting low-resource settings such as low- and middle-income countries and rural regions. We propose to reduce this disparity by developing a novel low-cost radiotherapy device that treat...

Power absorption and temperature rise in deep learning based head models for local radiofrequency exposures.

Physics in medicine and biology
Computational uncertainty and variability of power absorption and temperature rise in humans for radiofrequency (RF) exposure is a critical factor in ensuring human protection. This aspect has been emphasized as a priority. However, accurately modeli...

Evaluating the dosimetric and positioning accuracy of a deep learning based synthetic-CT model for liver radiotherapy treatment planning.

Biomedical physics & engineering express
An MRI-only workflow requires synthetic computed tomography (sCT) images to enable dose calculation. This study evaluated the dosimetric and patient positioning accuracy of deep learning-generated sCT for liver radiotherapy.sCT images were generated ...

Rapid dose prediction for lung CyberKnife radiotherapy plans utilizing a deep learning approach by incorporating dosimetric features delivered by noncoplanar beams.

Biomedical physics & engineering express
. The dose distribution of lung cancer patients treated with the CyberKnife (CK) system is influenced by various factors, including tumor location and the direction of CK beams. The objective of this study is to present a deep learning approach that ...

Characterization of Effective Half-Life for Instant Single-Time-Point Dosimetry Using Machine Learning.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Single-time-point (STP) image-based dosimetry offers a more convenient approach for clinical practice in radiopharmaceutical therapy (RPT) compared with conventional multiple-time-point image-based dosimetry. Despite numerous advancements, current ST...

RadField3D: a data generator and data format for deep learning in radiation-protection dosimetry for medical applications.

Journal of radiological protection : official journal of the Society for Radiological Protection
In this research work, we present our open-source Geant4-based Monte-Carlo simulation application, called RadField3D, for generating three-dimensional radiation field datasets for dosimetry. Accompanying, we introduce a fast, machine-interpretable da...

Multicenter development of a deep learning radiomics and dosiomics nomogram to predict radiation pneumonia risk in non-small cell lung cancer.

Scientific reports
Radiation pneumonia (RP) is the most common side effect of chest radiotherapy, and can affect patients' quality of life. This study aimed to establish a combined model of radiomics, dosiomics, deep learning (DL) based on simulated location CT and dos...