H. Zubair, Yi-Fei Tan, A. Basaif, A. Oresegun, H. Zin, David Bradley, H. Azhar, Abdul Rashid
{"title":"Determination of Radiation Delivery Parameters of Medical Linear Accelerators using Data Analytics Pipeline","authors":"H. Zubair, Yi-Fei Tan, A. Basaif, A. Oresegun, H. Zin, David Bradley, H. Azhar, Abdul Rashid","doi":"10.1109/IECBES54088.2022.10079318","DOIUrl":null,"url":null,"abstract":"Radiotherapy treatments involve the delivery of sharp radiation pulses of 2 to 4 microseconds duration over typical total periods of 30 to 300 seconds at a rate of up to 400 pulses per second. Recent developments in optical fiber-based radioluminescence/scintillator systems offer radiation-sensing capabilities that capture signals from individual pulses. Each of these signals has unique characteristics which provide insights into the parameters of radiation delivery. Current data acquisition methods commonly rely on hardware-based charge integration methods for radiation dose calculations and have limited utilization of the acquired data for further insights or applications. In this paper, a data analytics pipeline for the extraction and processing of data from a Ge-doped real-time dosimetry system is presented. The data, as obtained for an Elekta Synergy radiotherapy system, is then analyzed for dose distribution, dose-rates determination, and signal clustering. The gathering and processing of such time-resolved data would enable applications such as fault analysis, auto-calibration, and equipment fault prediction in medical radiation facilities in addition to enhancing the routine QA process.","PeriodicalId":146681,"journal":{"name":"2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IECBES54088.2022.10079318","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Radiotherapy treatments involve the delivery of sharp radiation pulses of 2 to 4 microseconds duration over typical total periods of 30 to 300 seconds at a rate of up to 400 pulses per second. Recent developments in optical fiber-based radioluminescence/scintillator systems offer radiation-sensing capabilities that capture signals from individual pulses. Each of these signals has unique characteristics which provide insights into the parameters of radiation delivery. Current data acquisition methods commonly rely on hardware-based charge integration methods for radiation dose calculations and have limited utilization of the acquired data for further insights or applications. In this paper, a data analytics pipeline for the extraction and processing of data from a Ge-doped real-time dosimetry system is presented. The data, as obtained for an Elekta Synergy radiotherapy system, is then analyzed for dose distribution, dose-rates determination, and signal clustering. The gathering and processing of such time-resolved data would enable applications such as fault analysis, auto-calibration, and equipment fault prediction in medical radiation facilities in addition to enhancing the routine QA process.