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Predicting Three-Dimensional Dose Distribution of Prostate Volumetric Modulated Arc Therapy Using Deep Learning
(2021-12-01)
Kummanee, Patiparn
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Chancharoen, Wares
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Tangtisanon, Kanut
;
Fuangrod, Todsaporn
Background: Volumetric modulated arc therapy (VMAT) planning is a time-consuming process of radiation therapy. With a deep learning approach, 3D dose distribution can be predicted without the need for an actual dose calculation. This approach can accelerate the process by guiding and confirming the achievable dose distribution in order to reduce the replanning iterations while maintaining the plan quality. Methods: In this study, three dose distribution predictive models of VMAT for prostate cancer were developed, evaluated, and compared. Each model was designed with a different input data structure to train and test the model: (1) patient CT alone (PCT alone), (2) patient CT and generalized organ structure (PCTGOS), and (3) patient CT and specific organ structure (PCTSOS). The generative adversarial network (GAN) model was used as a core learning algorithm. The models were trained slice-by-slice using 46 VMAT plans for prostate cancer, and then used to predict and evaluate the dose distribution from 8 independent plans. Results: VMAT dose distribution was generated with a mean prediction time of approximately 3.5 s per patient, whereas the PCTSOS model was excluded due to a mean prediction time of approximately 17.5 s per patient. The highest average 3D gamma passing rate was 80.51 ± 5.94, while the lowest overall percentage difference of dose-volume histogram (DVH) parameters was 6.01 ± 5.44% for the prescription dose from the PCTGOS model. However, the PCTSOS model was the most reliable for the evaluation of multiple parameters. Conclusions: This dose prediction model could accelerate the iterative optimization process for the planning of VMAT treatment by guiding the planner with the desired dose distribution.
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Predictive quality assurance of a linear accelerator based on the machine performance check application using statistical process control and ARIMA forecast modeling
(2020-08-01)
Puyati, Wayo
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Khawne, Amnach
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Barnes, Michael
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Zwan, Benjamin
;
Greer, Peter
Purpose: A predictive linac quality assurance system based on the output of the Machine Performance Check (MPC) application was developed using statistical process control and autoregressive integrated moving average forecast modeling. The aim of this study is to demonstrate the feasibility of predictive quality assurance based on MPC tests that allow proactive preventative maintenance procedures to be carried out to better ensure optimal linac performance and minimize downtime. Method and Materials: Daily MPC data were acquired for a total of 490 measurements. The initial 85% of data were used in prediction model learning with the autoregressive integrated moving average technique and in calculating upper and lower control limits for statistical process control analysis. The remaining 15% of data were used in testing the accuracy of the predictions of the proposed system. Two types of prediction were studied, namely, one-step-ahead values for predicting the next day's quality assurance results and six-step-ahead values for predicting up to a week ahead. Results that fall within the upper and lower control limits indicate a normal stage of machine performance, while the tolerance, determined from AAPM TG-142, is the clinically required performance. The gap between the control limits and the clinical tolerances (as the warning stage) provides a window of opportunity for rectifying linac performance issues before they become clinically significant. The accuracy of the predictive model was tested using the root-mean-square error, absolute error, and average accuracy rate for all MPC test parameters. Results: The accuracy of the predictive model is considered high (average root-mean-square error and absolute error for all parameters of less than 0.05). The average accuracy rate for indicating the normal/warning stages was higher than 85.00%. Conclusion: Predictive quality assurance with the MPC will allow preventative maintenance, which could lead to improved linac performance and a reduction in unscheduled linac downtime.
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A quick scene search with constructed mapped charts for TV sport programs
(2006-12-01)
Fuangrod, Todsaporn
;
Khawne, Amnach
This paper presents the fast scene search algorithm using fixed-image regions of the superimposed-text in a video. Our scheme focuses on the fixed-image region of the superimposed-text which is often an intuitive clue for an end-user for specifying a certain frame in a sport video. The video scene, consisting of superimposed-text detection using MFI algorithm, and Mapped Chart (MC) which constructed the index of a pattern matching, will be searched by the user. Our method found that the searching method with MC was faster (reduced the time consuming more than 50%) and more robust. ©2006 IEEE.
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End-user customizable scene search for TV programs
(2005-12-01)
Fuangrod, Todsaporn
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Nakayama, Ken
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Kobayashi, Yoshitake
;
Maekawa, Mamoru
This paper is purposed to scene search in TV program which allowed end-user can be define the pattern. Every user has different interests in TV program. Our scheme focuses on fixed background recognition. The process of this paper divided two parts; the first part is user interaction that consist manual definition of pattern and pattern matching inside video file. The second part is definition to use automatic pattern assistance tool. Simulated with Japanese baseball game, the system can search the scene following the pattern. © 2005 IEEE.

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