The Experts below are selected from a list of 84 Experts worldwide ranked by ideXlab platform
Xun Jia - One of the best experts on this subject based on the ideXlab platform.
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mo g bre 01 a real time virtual delivery system for photon radiotherapy delivery monitoring
Medical Physics, 2014Co-Authors: Feng Shi, Y Graves, Steve B Jiang, Xun JiaAbstract:Purpose: Treatment delivery monitoring is important for radiotherapy, which enables catching dosimetric error at the earliest possible opportunity. This project develops a virtual delivery system to monitor the dose delivery process of photon radiotherapy in real-time using GPU-based Monte Carlo (MC) method. Methods: The simulation process consists of 3 parallel CPU threads. A thread T1 is responsible for communication with a linac, which acquires a set of linac status parameters, e.g. gantry angles, MLC configurations, and beam MUs every 20 ms. Since linac vendors currently do not offer interface to acquire data in real time, we mimic this process by fetching information from a linac dynalog file at the set frequency. Instantaneous beam fluence map (FM) is calculated. A FM buffer is also created in T1 and the instantaneous FM is accumulated to it. This process continues, until a Ready Signal is received from thread T2 on which an inhouse developed MC dose engine executes on GPU. At that moment, the accumulated FM is transferred to T2 for dose calculations, and the FM buffer in T1 is cleared. Once the calculation finishes, the resulting 3D dose distribution is directed to thread T3, which displays it in three orthogonal planes overlaid on the CT image for treatment monitoring. This process continues to monitor the 3D dose distribution in real-time. Results: An IMRT and a VMAT cases used in our patient-specific QA are studied. Maximum dose differences between our system and treatment planning system are 0.98% and 1.58% for the two cases, respectively. The average time per MC calculation is 0.1sec with <2% relative uncertainty. The update frequency of ∼10Hz is considered as real time. Conclusion: By embedding a GPU-based MC code in a novel data/work flow, it is possible to achieve real-time MC dose calculations to monitor delivery process.
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MO-G-BRE-01: A Real-Time Virtual Delivery System for Photon Radiotherapy Delivery Monitoring
Medical Physics, 2014Co-Authors: Feng Shi, Y Graves, Steve B Jiang, Xun JiaAbstract:Purpose: Treatment delivery monitoring is important for radiotherapy, which enables catching dosimetric error at the earliest possible opportunity. This project develops a virtual delivery system to monitor the dose delivery process of photon radiotherapy in real-time using GPU-based Monte Carlo (MC) method. Methods: The simulation process consists of 3 parallel CPU threads. A thread T1 is responsible for communication with a linac, which acquires a set of linac status parameters, e.g. gantry angles, MLC configurations, and beam MUs every 20 ms. Since linac vendors currently do not offer interface to acquire data in real time, we mimic this process by fetching information from a linac dynalog file at the set frequency. Instantaneous beam fluence map (FM) is calculated. A FM buffer is also created in T1 and the instantaneous FM is accumulated to it. This process continues, until a Ready Signal is received from thread T2 on which an inhouse developed MC dose engine executes on GPU. At that moment, the accumulated FM is transferred to T2 for dose calculations, and the FM buffer in T1 is cleared. Once the calculation finishes, the resulting 3D dose distribution is directed to thread T3, which displays it in three orthogonal planes overlaid on the CT image for treatment monitoring. This process continues to monitor the 3D dose distribution in real-time. Results: An IMRT and a VMAT cases used in our patient-specific QA are studied. Maximum dose differences between our system and treatment planning system are 0.98% and 1.58% for the two cases, respectively. The average time per MC calculation is 0.1sec with
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a real time virtual delivery system for photon radiotherapy delivery monitoring
International Journal of Cancer Therapy and Oncology, 2014Co-Authors: Feng Shi, Steve B Jiang, Yan Jiang Graves, Xun JiaAbstract:Purpose: Treatment delivery monitoring is important for radiotherapy, which enables catching dosimetric error at the earliest possible opportunity. This project develops a virtual delivery system to monitor the dose delivery process of photon radiotherapy in real-time using GPU-based Monte Carlo (MC) method. Methods : The simulation process consists of 3 parallel CPU threads. A thread T1 is responsible for communication with a linac, which acquires a set of linac status parameters, e.g. gantry angles, MLC configurations, and beam MUs every 20 ms. Since linac vendors currently do not offer interface to acquire data in real time, we mimic this process by fetching information from a linac dynalog file at the set frequency. Instantaneous beam fluence map (FM) is calculated based. A FM buffer is also created in T1 and the instantaneous FM is accumulated to it. This process continues, until a Ready Signal is received from thread T2 on which an in-house developed MC dose engine executes on GPU. At that moment, the accumulated FM is transferred to T2 for dose calculations, and the FM buffer in T1 is cleared. Once the dose calculation finishes, the resulting 3D dose distribution is directed to thread T3, which displays it in three orthogonal planes in color wash overlaid on the CT image. This process continues to monitor the 3D dose distribution in real-time. Results: An IMRT and a VMAT cases used in our patient-specific QA are studied. Maximum dose differences between our system and treatment planning system are 0.98% and 1.58% for the IMRT and VMAT cases, respectively. The update frequency is >10Hz and the relative uncertainty level is 2%. Conclusion : By embedding a GPU-based MC code in a novel data/work flow, it is possible to achieve real-time MC dose calculations to monitor delivery process. ------------------------------ Cite this article as: Shi F, Gu X, Graves YJ, Jiang S, Jia X. A real-time virtual delivery system for photon radiotherapy delivery monitoring. Int J Cancer Ther Oncol 2014; 2(2):020222. DOI: 10.14319/ijcto.0202.22
Feng Shi - One of the best experts on this subject based on the ideXlab platform.
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mo g bre 01 a real time virtual delivery system for photon radiotherapy delivery monitoring
Medical Physics, 2014Co-Authors: Feng Shi, Y Graves, Steve B Jiang, Xun JiaAbstract:Purpose: Treatment delivery monitoring is important for radiotherapy, which enables catching dosimetric error at the earliest possible opportunity. This project develops a virtual delivery system to monitor the dose delivery process of photon radiotherapy in real-time using GPU-based Monte Carlo (MC) method. Methods: The simulation process consists of 3 parallel CPU threads. A thread T1 is responsible for communication with a linac, which acquires a set of linac status parameters, e.g. gantry angles, MLC configurations, and beam MUs every 20 ms. Since linac vendors currently do not offer interface to acquire data in real time, we mimic this process by fetching information from a linac dynalog file at the set frequency. Instantaneous beam fluence map (FM) is calculated. A FM buffer is also created in T1 and the instantaneous FM is accumulated to it. This process continues, until a Ready Signal is received from thread T2 on which an inhouse developed MC dose engine executes on GPU. At that moment, the accumulated FM is transferred to T2 for dose calculations, and the FM buffer in T1 is cleared. Once the calculation finishes, the resulting 3D dose distribution is directed to thread T3, which displays it in three orthogonal planes overlaid on the CT image for treatment monitoring. This process continues to monitor the 3D dose distribution in real-time. Results: An IMRT and a VMAT cases used in our patient-specific QA are studied. Maximum dose differences between our system and treatment planning system are 0.98% and 1.58% for the two cases, respectively. The average time per MC calculation is 0.1sec with <2% relative uncertainty. The update frequency of ∼10Hz is considered as real time. Conclusion: By embedding a GPU-based MC code in a novel data/work flow, it is possible to achieve real-time MC dose calculations to monitor delivery process.
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MO-G-BRE-01: A Real-Time Virtual Delivery System for Photon Radiotherapy Delivery Monitoring
Medical Physics, 2014Co-Authors: Feng Shi, Y Graves, Steve B Jiang, Xun JiaAbstract:Purpose: Treatment delivery monitoring is important for radiotherapy, which enables catching dosimetric error at the earliest possible opportunity. This project develops a virtual delivery system to monitor the dose delivery process of photon radiotherapy in real-time using GPU-based Monte Carlo (MC) method. Methods: The simulation process consists of 3 parallel CPU threads. A thread T1 is responsible for communication with a linac, which acquires a set of linac status parameters, e.g. gantry angles, MLC configurations, and beam MUs every 20 ms. Since linac vendors currently do not offer interface to acquire data in real time, we mimic this process by fetching information from a linac dynalog file at the set frequency. Instantaneous beam fluence map (FM) is calculated. A FM buffer is also created in T1 and the instantaneous FM is accumulated to it. This process continues, until a Ready Signal is received from thread T2 on which an inhouse developed MC dose engine executes on GPU. At that moment, the accumulated FM is transferred to T2 for dose calculations, and the FM buffer in T1 is cleared. Once the calculation finishes, the resulting 3D dose distribution is directed to thread T3, which displays it in three orthogonal planes overlaid on the CT image for treatment monitoring. This process continues to monitor the 3D dose distribution in real-time. Results: An IMRT and a VMAT cases used in our patient-specific QA are studied. Maximum dose differences between our system and treatment planning system are 0.98% and 1.58% for the two cases, respectively. The average time per MC calculation is 0.1sec with
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a real time virtual delivery system for photon radiotherapy delivery monitoring
International Journal of Cancer Therapy and Oncology, 2014Co-Authors: Feng Shi, Steve B Jiang, Yan Jiang Graves, Xun JiaAbstract:Purpose: Treatment delivery monitoring is important for radiotherapy, which enables catching dosimetric error at the earliest possible opportunity. This project develops a virtual delivery system to monitor the dose delivery process of photon radiotherapy in real-time using GPU-based Monte Carlo (MC) method. Methods : The simulation process consists of 3 parallel CPU threads. A thread T1 is responsible for communication with a linac, which acquires a set of linac status parameters, e.g. gantry angles, MLC configurations, and beam MUs every 20 ms. Since linac vendors currently do not offer interface to acquire data in real time, we mimic this process by fetching information from a linac dynalog file at the set frequency. Instantaneous beam fluence map (FM) is calculated based. A FM buffer is also created in T1 and the instantaneous FM is accumulated to it. This process continues, until a Ready Signal is received from thread T2 on which an in-house developed MC dose engine executes on GPU. At that moment, the accumulated FM is transferred to T2 for dose calculations, and the FM buffer in T1 is cleared. Once the dose calculation finishes, the resulting 3D dose distribution is directed to thread T3, which displays it in three orthogonal planes in color wash overlaid on the CT image. This process continues to monitor the 3D dose distribution in real-time. Results: An IMRT and a VMAT cases used in our patient-specific QA are studied. Maximum dose differences between our system and treatment planning system are 0.98% and 1.58% for the IMRT and VMAT cases, respectively. The update frequency is >10Hz and the relative uncertainty level is 2%. Conclusion : By embedding a GPU-based MC code in a novel data/work flow, it is possible to achieve real-time MC dose calculations to monitor delivery process. ------------------------------ Cite this article as: Shi F, Gu X, Graves YJ, Jiang S, Jia X. A real-time virtual delivery system for photon radiotherapy delivery monitoring. Int J Cancer Ther Oncol 2014; 2(2):020222. DOI: 10.14319/ijcto.0202.22
Steve B Jiang - One of the best experts on this subject based on the ideXlab platform.
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mo g bre 01 a real time virtual delivery system for photon radiotherapy delivery monitoring
Medical Physics, 2014Co-Authors: Feng Shi, Y Graves, Steve B Jiang, Xun JiaAbstract:Purpose: Treatment delivery monitoring is important for radiotherapy, which enables catching dosimetric error at the earliest possible opportunity. This project develops a virtual delivery system to monitor the dose delivery process of photon radiotherapy in real-time using GPU-based Monte Carlo (MC) method. Methods: The simulation process consists of 3 parallel CPU threads. A thread T1 is responsible for communication with a linac, which acquires a set of linac status parameters, e.g. gantry angles, MLC configurations, and beam MUs every 20 ms. Since linac vendors currently do not offer interface to acquire data in real time, we mimic this process by fetching information from a linac dynalog file at the set frequency. Instantaneous beam fluence map (FM) is calculated. A FM buffer is also created in T1 and the instantaneous FM is accumulated to it. This process continues, until a Ready Signal is received from thread T2 on which an inhouse developed MC dose engine executes on GPU. At that moment, the accumulated FM is transferred to T2 for dose calculations, and the FM buffer in T1 is cleared. Once the calculation finishes, the resulting 3D dose distribution is directed to thread T3, which displays it in three orthogonal planes overlaid on the CT image for treatment monitoring. This process continues to monitor the 3D dose distribution in real-time. Results: An IMRT and a VMAT cases used in our patient-specific QA are studied. Maximum dose differences between our system and treatment planning system are 0.98% and 1.58% for the two cases, respectively. The average time per MC calculation is 0.1sec with <2% relative uncertainty. The update frequency of ∼10Hz is considered as real time. Conclusion: By embedding a GPU-based MC code in a novel data/work flow, it is possible to achieve real-time MC dose calculations to monitor delivery process.
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MO-G-BRE-01: A Real-Time Virtual Delivery System for Photon Radiotherapy Delivery Monitoring
Medical Physics, 2014Co-Authors: Feng Shi, Y Graves, Steve B Jiang, Xun JiaAbstract:Purpose: Treatment delivery monitoring is important for radiotherapy, which enables catching dosimetric error at the earliest possible opportunity. This project develops a virtual delivery system to monitor the dose delivery process of photon radiotherapy in real-time using GPU-based Monte Carlo (MC) method. Methods: The simulation process consists of 3 parallel CPU threads. A thread T1 is responsible for communication with a linac, which acquires a set of linac status parameters, e.g. gantry angles, MLC configurations, and beam MUs every 20 ms. Since linac vendors currently do not offer interface to acquire data in real time, we mimic this process by fetching information from a linac dynalog file at the set frequency. Instantaneous beam fluence map (FM) is calculated. A FM buffer is also created in T1 and the instantaneous FM is accumulated to it. This process continues, until a Ready Signal is received from thread T2 on which an inhouse developed MC dose engine executes on GPU. At that moment, the accumulated FM is transferred to T2 for dose calculations, and the FM buffer in T1 is cleared. Once the calculation finishes, the resulting 3D dose distribution is directed to thread T3, which displays it in three orthogonal planes overlaid on the CT image for treatment monitoring. This process continues to monitor the 3D dose distribution in real-time. Results: An IMRT and a VMAT cases used in our patient-specific QA are studied. Maximum dose differences between our system and treatment planning system are 0.98% and 1.58% for the two cases, respectively. The average time per MC calculation is 0.1sec with
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a real time virtual delivery system for photon radiotherapy delivery monitoring
International Journal of Cancer Therapy and Oncology, 2014Co-Authors: Feng Shi, Steve B Jiang, Yan Jiang Graves, Xun JiaAbstract:Purpose: Treatment delivery monitoring is important for radiotherapy, which enables catching dosimetric error at the earliest possible opportunity. This project develops a virtual delivery system to monitor the dose delivery process of photon radiotherapy in real-time using GPU-based Monte Carlo (MC) method. Methods : The simulation process consists of 3 parallel CPU threads. A thread T1 is responsible for communication with a linac, which acquires a set of linac status parameters, e.g. gantry angles, MLC configurations, and beam MUs every 20 ms. Since linac vendors currently do not offer interface to acquire data in real time, we mimic this process by fetching information from a linac dynalog file at the set frequency. Instantaneous beam fluence map (FM) is calculated based. A FM buffer is also created in T1 and the instantaneous FM is accumulated to it. This process continues, until a Ready Signal is received from thread T2 on which an in-house developed MC dose engine executes on GPU. At that moment, the accumulated FM is transferred to T2 for dose calculations, and the FM buffer in T1 is cleared. Once the dose calculation finishes, the resulting 3D dose distribution is directed to thread T3, which displays it in three orthogonal planes in color wash overlaid on the CT image. This process continues to monitor the 3D dose distribution in real-time. Results: An IMRT and a VMAT cases used in our patient-specific QA are studied. Maximum dose differences between our system and treatment planning system are 0.98% and 1.58% for the IMRT and VMAT cases, respectively. The update frequency is >10Hz and the relative uncertainty level is 2%. Conclusion : By embedding a GPU-based MC code in a novel data/work flow, it is possible to achieve real-time MC dose calculations to monitor delivery process. ------------------------------ Cite this article as: Shi F, Gu X, Graves YJ, Jiang S, Jia X. A real-time virtual delivery system for photon radiotherapy delivery monitoring. Int J Cancer Ther Oncol 2014; 2(2):020222. DOI: 10.14319/ijcto.0202.22
Y Graves - One of the best experts on this subject based on the ideXlab platform.
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mo g bre 01 a real time virtual delivery system for photon radiotherapy delivery monitoring
Medical Physics, 2014Co-Authors: Feng Shi, Y Graves, Steve B Jiang, Xun JiaAbstract:Purpose: Treatment delivery monitoring is important for radiotherapy, which enables catching dosimetric error at the earliest possible opportunity. This project develops a virtual delivery system to monitor the dose delivery process of photon radiotherapy in real-time using GPU-based Monte Carlo (MC) method. Methods: The simulation process consists of 3 parallel CPU threads. A thread T1 is responsible for communication with a linac, which acquires a set of linac status parameters, e.g. gantry angles, MLC configurations, and beam MUs every 20 ms. Since linac vendors currently do not offer interface to acquire data in real time, we mimic this process by fetching information from a linac dynalog file at the set frequency. Instantaneous beam fluence map (FM) is calculated. A FM buffer is also created in T1 and the instantaneous FM is accumulated to it. This process continues, until a Ready Signal is received from thread T2 on which an inhouse developed MC dose engine executes on GPU. At that moment, the accumulated FM is transferred to T2 for dose calculations, and the FM buffer in T1 is cleared. Once the calculation finishes, the resulting 3D dose distribution is directed to thread T3, which displays it in three orthogonal planes overlaid on the CT image for treatment monitoring. This process continues to monitor the 3D dose distribution in real-time. Results: An IMRT and a VMAT cases used in our patient-specific QA are studied. Maximum dose differences between our system and treatment planning system are 0.98% and 1.58% for the two cases, respectively. The average time per MC calculation is 0.1sec with <2% relative uncertainty. The update frequency of ∼10Hz is considered as real time. Conclusion: By embedding a GPU-based MC code in a novel data/work flow, it is possible to achieve real-time MC dose calculations to monitor delivery process.
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MO-G-BRE-01: A Real-Time Virtual Delivery System for Photon Radiotherapy Delivery Monitoring
Medical Physics, 2014Co-Authors: Feng Shi, Y Graves, Steve B Jiang, Xun JiaAbstract:Purpose: Treatment delivery monitoring is important for radiotherapy, which enables catching dosimetric error at the earliest possible opportunity. This project develops a virtual delivery system to monitor the dose delivery process of photon radiotherapy in real-time using GPU-based Monte Carlo (MC) method. Methods: The simulation process consists of 3 parallel CPU threads. A thread T1 is responsible for communication with a linac, which acquires a set of linac status parameters, e.g. gantry angles, MLC configurations, and beam MUs every 20 ms. Since linac vendors currently do not offer interface to acquire data in real time, we mimic this process by fetching information from a linac dynalog file at the set frequency. Instantaneous beam fluence map (FM) is calculated. A FM buffer is also created in T1 and the instantaneous FM is accumulated to it. This process continues, until a Ready Signal is received from thread T2 on which an inhouse developed MC dose engine executes on GPU. At that moment, the accumulated FM is transferred to T2 for dose calculations, and the FM buffer in T1 is cleared. Once the calculation finishes, the resulting 3D dose distribution is directed to thread T3, which displays it in three orthogonal planes overlaid on the CT image for treatment monitoring. This process continues to monitor the 3D dose distribution in real-time. Results: An IMRT and a VMAT cases used in our patient-specific QA are studied. Maximum dose differences between our system and treatment planning system are 0.98% and 1.58% for the two cases, respectively. The average time per MC calculation is 0.1sec with
Joseph V. Brady - One of the best experts on this subject based on the ideXlab platform.
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Cocaine's effects on detection, discrimination, and identification of auditory stimuli by baboons.
Pharmacology biochemistry and behavior, 2003Co-Authors: Robert D. Hienz, Michael R. Weed, Troy J. Zarcone, Joseph V. BradyAbstract:Abstract The perceptual effects of cocaine were examined under conditions that required baboons to detect the presence of tones as well as to identify tones of different pitches, and the results compared to the results of prior studies on cocaine's effects on the detection of tones, the discrimination of different tone pitches, and the discrimination of different human vowel sounds of similar pitch. A reaction time procedure was employed in which baboons were trained to press a lever in the presence of a visual “Ready” Signal, and release the lever only when one tone pitch occurred, but not release the lever when a second, different tone pitch occurred. Changes in the percentage of correct detections and median reaction times for each tone were measured following intramuscular administration of cocaine (0.01–1.0 mg/kg). Cocaine impaired tone identification and shortened reaction times to the tones in all baboons. Cocaine's effects on accuracy, however, were primarily due to elevations in false alarm rates, as opposed to detection of the stimuli themselves. The results demonstrate that cocaine impairs the discriminability of tone pitches in baboons, and that such impairments can depend upon the type of stimuli employed (tones vs. speech sounds) and the type of procedure employed (discrimination vs. identification).
