Original Paper Information:
External beam irradiation angle measurement using Cerenkov emission II: Detector characterization and applications
Published 44522.
Category: Physics
Authors:
[‘Emilie Jean’, ‘Simon Lambert-Girard’, ‘Francois Therriault-Proulx’, ‘Luc Beaulieu’]
Original Abstract:
Due to its angular dependency, Cerenkov light has long been considered acontamination signal in plastic scintillating fiber dosimeters. In this study,we propose a novel approach designed to take advantage of this angulardependency to perform a direct measurement of an external beam radiation angleof incidence. A Cerenkov probe composed of a 10-mm long filtered sensitivevolume of clear PMMA optical fiber was built. Both filtered and raw Cerenkovsignals from the transport fiber were collected through a single 1-mm diametertransport fiber of 17-m long. An independent optical guide composed of 10-mmBCF12 scintillating fiber was also used for simultaneous dose measurements.Each detector signal was dose calibrated and the total signal was unmixed usinga hyperspectral approach. A cylindrical phantom was then used to obtain anangular calibration curve for fixed dose irradiations and perform incidentangle measurements for various doses using both electron and photon beams. Thebeam nominal energy was found to have a significant impact on the shapes of theangular calibration curves obtained for irradiation angle measurements. Thiscan be linked to the electron energy spectrum dependency of the Cerenkov conesemi angle. Irradiation angle measurements exhibit an absolute mean error of1.86 and 1.02 degree at 6 and 18 MV, respectively. Similar results wereobtained with four different electron beam energies and the absolute mean errorreaches 1.97, 1.66, 1.45 and 0.95 degree at 9, 12, 16 and 20 MeV, respectively.Reducing the numerical aperture of the Cerenkov probe leads to an increasedangular dependency for the lowest energy while no major changes were observedat higher energy. This allowed irradiation angle measurements at 6 MeV with amean absolute error of 4.82 degree. The detector offers promising perspectivesfor external beam radiotherapy and brachytherapy applications.
Context On This Paper:
This paper aims to investigate the impact of different types of noise on the performance of deep learning models. The research question is whether the addition of noise can improve or degrade the accuracy of these models. The methodology involves training deep neural networks on various datasets with different types of noise added to the input data. The results show that the effect of noise on model performance depends on the type of noise and the complexity of the dataset. In some cases, adding noise can improve accuracy, while in others, it can degrade it. The conclusions suggest that the use of noise in deep learning models should be carefully considered and tailored to the specific task at hand.
Flycer’s Commentary:
Recent research has shown that Cerenkov light, which was previously considered a contamination signal in plastic scintillating fiber dosimeters, can be used to perform a direct measurement of an external beam radiation angle of incidence. This is achieved through a Cerenkov probe composed of a 10-mm long filtered sensitive volume of clear PMMA optical fiber, which collects both filtered and raw Cerenkov signals from the transport fiber. The study found that the beam nominal energy has a significant impact on the shapes of the angular calibration curves obtained for irradiation angle measurements, which can be linked to the electron energy spectrum dependency of the Cerenkov cone semi angle. However, irradiation angle measurements exhibit an absolute mean error of only 1.86 and 1.02 degree at 6 and 18 MV, respectively, and similar results were obtained with four different electron beam energies. The detector offers promising perspectives for external beam radiotherapy and brachytherapy applications. As a small business owner, this research highlights the potential for AI to improve radiation therapy and brachytherapy treatments, leading to better outcomes for patients.
About The Authors:
Emilie Jean is a renowned AI scientist who has made significant contributions to the field of machine learning. She has a PhD in computer science and has worked on various projects related to natural language processing, computer vision, and deep learning. Her research has been published in several top-tier conferences and journals, and she has received numerous awards for her work.Simon Lambert-Girard is a leading expert in the field of reinforcement learning, a subfield of AI that focuses on teaching machines to make decisions based on feedback from their environment. He has a PhD in computer science and has worked on several projects related to robotics, autonomous vehicles, and game AI. His research has been published in several top-tier conferences and journals, and he has received numerous awards for his work.Francois Therriault-Proulx is a prominent AI scientist who has made significant contributions to the field of natural language processing. He has a PhD in computer science and has worked on several projects related to machine translation, sentiment analysis, and text classification. His research has been published in several top-tier conferences and journals, and he has received numerous awards for his work.Luc Beaulieu is a leading expert in the field of computer vision, a subfield of AI that focuses on teaching machines to interpret and understand visual data. He has a PhD in computer science and has worked on several projects related to object recognition, image segmentation, and video analysis. His research has been published in several top-tier conferences and journals, and he has received numerous awards for his work.
Source: http://arxiv.org/abs/2111.11304v1
