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Gamma Rays

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Predicting gamma passing rates for portal dosimetry-based IMRT QA using machine learning.

Medical physics
PURPOSE: Intensity-modulated radiation therapy (IMRT) quality assurance (QA) measurements are routinely performed prior to treatment delivery to verify dose calculation and delivery accuracy. In this work, we applied a machine learning-based approach...

A Super-Learner Model for Tumor Motion Prediction and Management in Radiation Therapy: Development and Feasibility Evaluation.

Scientific reports
In cancer radiation therapy, large tumor motion due to respiration can lead to uncertainties in tumor target delineation and treatment delivery, thus making active motion management an essential step in thoracic and abdominal tumor treatment. In curr...

A deep learning approach for converting prompt gamma images to proton dose distributions: A Monte Carlo simulation study.

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)
PURPOSE: In proton therapy, imaging prompt gamma (PG) rays has the potential to verify proton dose (PD) distribution. Despite the fact that there is a strong correlation between the gamma-ray emission and PD, they are still different in terms of the ...

Deep learning based methods for gamma ray interaction location estimation in monolithic scintillation crystal detectors.

Physics in medicine and biology
In this work, we explore deep learning based techniques using the information from mean detector response functions (MDRFs) as a new method to estimate gamma ray interaction location in monolithic scintillation crystal detectors. Compared with search...

Utilization of Artificial Neural Network in Predicting the Total Organic Carbon in Devonian Shale Using the Conventional Well Logs and the Spectral Gamma Ray.

Computational intelligence and neuroscience
Due to high oil and gas production and consumption, unconventional reservoirs attracted significant interest. Total organic carbon (TOC) is a significant measure of the quality of unconventional resources. Conventionally, TOC is measured experimental...

Detecting MLC modeling errors using radiomics-based machine learning in patient-specific QA with an EPID for intensity-modulated radiation therapy.

Medical physics
PURPOSE: We sought to develop machine learning models to detect multileaf collimator (MLC) modeling errors with the use of radiomic features of fluence maps measured in patient-specific quality assurance (QA) for intensity-modulated radiation therapy...

Systematic method for a deep learning-based prediction model for gamma evaluation in patient-specific quality assurance of volumetric modulated arc therapy.

Medical physics
PURPOSE: This study aimed to develop and evaluate a novel strategy for establishing a deep learning-based gamma passing rate (GPR) prediction model for volumetric modulated arc therapy (VMAT) using dummy target plan data, one measurement process, and...

Artificial neural networks for positioning of gamma interactions in monolithic PET detectors.

Physics in medicine and biology
To detect gamma rays with good spatial, timing and energy resolution while maintaining high sensitivity we need accurate and efficient algorithms to estimate the first gamma interaction position from the measured light distribution. Furthermore, mono...

Lyapunov stability criteria in terms of class K functions for Riemann-Liouville nabla fractional order systems.

ISA transactions
This paper focuses on the problem of stability analysis for Riemann-Liouville nabla fractional order systems. On one hand, a useful comparison principle is built and then a rigorous proof is constructed for the well-known Lyapunov stability criterion...

Use of gamma radiation and artificial neural network techniques to monitor characteristics of polyduct transport of petroleum by-products.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
This study presents a methodology based on the dual-mode gamma densitometry technique in combination with artificial neural networks to simultaneously determine type and quantity of four different fluids (Gasoline, Glycerol, Kerosene and Fuel Oil) to...