The Experts below are selected from a list of 92685 Experts worldwide ranked by ideXlab platform
R Jeraj - One of the best experts on this subject based on the ideXlab platform.
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the effect of dose calculation accuracy on inverse treatment planning
Physics in Medicine and Biology, 2002Co-Authors: R Jeraj, P Keall, J SiebersAbstract:The effect of dose calculation accuracy during inverse treatment planning for intensity modulated radiotherapy (IMRT) was studied in this work. Three dose calculation methods were compared: Monte Carlo, superposition and pencil beam. These algorithms were used to calculate beamlets, which were subsequently used by a simulated annealing algorithm to determine beamlet weights which comprised the optimal solution to the objective function. Three different cases (lung, prostate and head and neck) were investigated and several different objective functions were tested for their effect on inverse treatment planning. It is shown that the use of inaccurate dose calculation introduces two Errors in a treatment plan, a systematic Error and a Convergence Error. The systematic Error is present because of the inaccuracy of the dose calculation algorithm. The Convergence Error appears because the optimal intensity distribution for inaccurate beamlets differs from the optimal solution for the accurate beamlets. While the systematic Error for superposition was found to be ~1% of Dmax in the tumour and slightly larger outside, the Error for the pencil beam method is typically ~5% of Dmax and is rather insensitive to the given objectives. On the other hand, the Convergence Error was found to be very sensitive to the objective function, is only slightly correlated to the systematic Error and should be determined for each case individually. Our results suggest that because of the large systematic and Convergence Errors, inverse treatment planning systems based on pencil beam algorithms alone should be upgraded either to superposition or Monte Carlo based dose calculations.
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the effect of statistical uncertainty on inverse treatment planning based on monte carlo dose calculation
Physics in Medicine and Biology, 2000Co-Authors: R Jeraj, P KeallAbstract:The effect of the statistical uncertainty, or noise, in inverse treatment planning for intensity modulated radiotherapy (IMRT) based on Monte Carlo dose calculation was studied. Sets of Monte Carlo beamlets were calculated to give uncertainties at Dmax ranging from 0.2% to 4% for a lung tumour plan. The weights of these beamlets were optimized using a previously described procedure based on a simulated annealing optimization algorithm. Several different objective functions were used. It was determined that the use of Monte Carlo dose calculation in inverse treatment planning introduces two Errors in the calculated plan. In addition to the statistical Error due to the statistical uncertainty of the Monte Carlo calculation, a noise Convergence Error also appears. For the statistical Error it was determined that apparently successfully optimized plans with a noisy dose calculation (3% 1σ at Dmax ), which satisfied the required uniformity of the dose within the tumour, showed as much as 7% underdose when recalculated with a noise-free dose calculation. The statistical Error is larger towards the tumour and is only weakly dependent on the choice of objective function. The noise Convergence Error appears because the optimum weights are determined using a noisy calculation, which is different from the optimum weights determined for a noise-free calculation. Unlike the statistical Error, the noise Convergence Error is generally larger outside the tumour, is case dependent and strongly depends on the required objectives.
Radha Poovendran - One of the best experts on this subject based on the ideXlab platform.
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Minimizing Convergence Error in multi-agent systems via leader selection: A supermodular optimization approach
IEEE Transactions on Automatic Control, 2014Co-Authors: Andrew Clark, Basel Alomair, Linda Bushnell, Radha PoovendranAbstract:In a leader-follower multi-agent system (MAS), the leader agents act as control inputs and influence the states of the remaining follower agents. The rate at which the follower agents converge to their desired states, as well as the Errors in the follower agent states prior to Convergence, are determined by the choice of leader agents. In this paper, we study leader selection in order to minimize Convergence Errors experienced by the follower agents, which we define as a norm of the distance between the follower agents' intermediate states and the convex hull of the leader agent states. By introducing a novel connection to random walks on the network graph, we show that the Convergence Error has an inherent supermodular structure as a function of the leader set. Supermodularity enables development of efficient discrete optimization algorithms that directly approximate the optimal leader set, provide provable performance guarantees, and do not rely on continuous relaxations. We formulate two leader selection problems within the supermodular optimization framework, namely, the problem of selecting a fixed number of leader agents in order to minimize the Convergence Error, as well as the problem of selecting the minimum-size set of leader agents to achieve a given bound on the Convergence Error. We introduce algorithms for approximating the optimal solution to both problems in static networks, dynamic networks with known topology distributions, and dynamic networks with unknown and unpredictable topology distributions. Our approach is shown to provide significantly lower Convergence Errors than existing random and degree-based leader selection methods in a numerical study.
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leader selection for minimizing Convergence Error in leader follower systems a supermodular optimization approach
Modeling and Optimization in Mobile Ad-Hoc and Wireless Networks, 2012Co-Authors: Andrew Clark, Linda Bushnell, Radha PoovendranAbstract:In leader-follower systems, follower nodes receive inputs from a set of leader nodes, exchange information, and update their states according to an iterative algorithm. In such algorithms, the node states may deviate from their desired values before the algorithm converges, leading to disruptions in network performance. In this paper, we study the problem of choosing leader nodes in order to minimize Convergence Errors. We first develop a connection between a class of weighted averaging algorithms and random walks on graphs, and then show that the Convergence Error is a supermodular function of the set of leader nodes. Based on the supermodularity of the Convergence Error, we derive efficient algorithms for selecting leader nodes that are within a provable bound of the optimum. Our approach is demonstrated through a simulation study.
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WiOpt - Leader selection for minimizing Convergence Error in leader-follower systems: A supermodular optimization approach
2012Co-Authors: Andrew Clark, Linda Bushnell, Radha PoovendranAbstract:In leader-follower systems, follower nodes receive inputs from a set of leader nodes, exchange information, and update their states according to an iterative algorithm. In such algorithms, the node states may deviate from their desired values before the algorithm converges, leading to disruptions in network performance. In this paper, we study the problem of choosing leader nodes in order to minimize Convergence Errors. We first develop a connection between a class of weighted averaging algorithms and random walks on graphs, and then show that the Convergence Error is a supermodular function of the set of leader nodes. Based on the supermodularity of the Convergence Error, we derive efficient algorithms for selecting leader nodes that are within a provable bound of the optimum. Our approach is demonstrated through a simulation study.
P Keall - One of the best experts on this subject based on the ideXlab platform.
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the effect of dose calculation accuracy on inverse treatment planning
Physics in Medicine and Biology, 2002Co-Authors: R Jeraj, P Keall, J SiebersAbstract:The effect of dose calculation accuracy during inverse treatment planning for intensity modulated radiotherapy (IMRT) was studied in this work. Three dose calculation methods were compared: Monte Carlo, superposition and pencil beam. These algorithms were used to calculate beamlets, which were subsequently used by a simulated annealing algorithm to determine beamlet weights which comprised the optimal solution to the objective function. Three different cases (lung, prostate and head and neck) were investigated and several different objective functions were tested for their effect on inverse treatment planning. It is shown that the use of inaccurate dose calculation introduces two Errors in a treatment plan, a systematic Error and a Convergence Error. The systematic Error is present because of the inaccuracy of the dose calculation algorithm. The Convergence Error appears because the optimal intensity distribution for inaccurate beamlets differs from the optimal solution for the accurate beamlets. While the systematic Error for superposition was found to be ~1% of Dmax in the tumour and slightly larger outside, the Error for the pencil beam method is typically ~5% of Dmax and is rather insensitive to the given objectives. On the other hand, the Convergence Error was found to be very sensitive to the objective function, is only slightly correlated to the systematic Error and should be determined for each case individually. Our results suggest that because of the large systematic and Convergence Errors, inverse treatment planning systems based on pencil beam algorithms alone should be upgraded either to superposition or Monte Carlo based dose calculations.
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The effect of dose calculation accuracy on inverse treatment planning.
Physics in medicine and biology, 2002Co-Authors: Robert Jeraj, P Keall, Jeffrey V. SiebersAbstract:The effect of dose calculation accuracy during inverse treatment planning for intensity modulated radiotherapy (IMRT) was studied in this work. Three dose calculation methods were compared: Monte Carlo, superposition and pencil beam. These algorithms were used to calculate beamlets. which were subsequently used by a simulated annealing algorithm to determine beamlet weights which comprised the optimal solution to the objective function. Three different cases (lung, prostate and head and neck) were investigated and several different objective functions were tested for their effect on inverse treatment planning. It is shown that the use of inaccurate dose calculation introduces two Errors in a treatment plan, a systematic Error and a Convergence Error. The systematic Error is present because of the inaccuracy of the dose calculation algorithm. The Convergence Error appears because the optimal intensity distribution for inaccurate beamlets differs from the optimal solution for the accurate beamlets. While the systematic Error for superposition was found to be approximately 1% of Dmax in the tumour and slightly larger outside, the Error for the pencil beam method is typically approximately 5% of Dmax and is rather insensitive to the given objectives. On the other hand, the Convergence Error was found to be very sensitive to the objective function, is only slightly correlated to the systematic Error and should be determined for each case individually. Our results suggest that because of the large systematic and Convergence Errors, inverse treatment planning systems based on pencil beam algorithms alone should be upgraded either to superposition or Monte Carlo based dose calculations.
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the effect of statistical uncertainty on inverse treatment planning based on monte carlo dose calculation
Physics in Medicine and Biology, 2000Co-Authors: R Jeraj, P KeallAbstract:The effect of the statistical uncertainty, or noise, in inverse treatment planning for intensity modulated radiotherapy (IMRT) based on Monte Carlo dose calculation was studied. Sets of Monte Carlo beamlets were calculated to give uncertainties at Dmax ranging from 0.2% to 4% for a lung tumour plan. The weights of these beamlets were optimized using a previously described procedure based on a simulated annealing optimization algorithm. Several different objective functions were used. It was determined that the use of Monte Carlo dose calculation in inverse treatment planning introduces two Errors in the calculated plan. In addition to the statistical Error due to the statistical uncertainty of the Monte Carlo calculation, a noise Convergence Error also appears. For the statistical Error it was determined that apparently successfully optimized plans with a noisy dose calculation (3% 1σ at Dmax ), which satisfied the required uniformity of the dose within the tumour, showed as much as 7% underdose when recalculated with a noise-free dose calculation. The statistical Error is larger towards the tumour and is only weakly dependent on the choice of objective function. The noise Convergence Error appears because the optimum weights are determined using a noisy calculation, which is different from the optimum weights determined for a noise-free calculation. Unlike the statistical Error, the noise Convergence Error is generally larger outside the tumour, is case dependent and strongly depends on the required objectives.
Andrew Clark - One of the best experts on this subject based on the ideXlab platform.
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Minimizing Convergence Error in multi-agent systems via leader selection: A supermodular optimization approach
IEEE Transactions on Automatic Control, 2014Co-Authors: Andrew Clark, Basel Alomair, Linda Bushnell, Radha PoovendranAbstract:In a leader-follower multi-agent system (MAS), the leader agents act as control inputs and influence the states of the remaining follower agents. The rate at which the follower agents converge to their desired states, as well as the Errors in the follower agent states prior to Convergence, are determined by the choice of leader agents. In this paper, we study leader selection in order to minimize Convergence Errors experienced by the follower agents, which we define as a norm of the distance between the follower agents' intermediate states and the convex hull of the leader agent states. By introducing a novel connection to random walks on the network graph, we show that the Convergence Error has an inherent supermodular structure as a function of the leader set. Supermodularity enables development of efficient discrete optimization algorithms that directly approximate the optimal leader set, provide provable performance guarantees, and do not rely on continuous relaxations. We formulate two leader selection problems within the supermodular optimization framework, namely, the problem of selecting a fixed number of leader agents in order to minimize the Convergence Error, as well as the problem of selecting the minimum-size set of leader agents to achieve a given bound on the Convergence Error. We introduce algorithms for approximating the optimal solution to both problems in static networks, dynamic networks with known topology distributions, and dynamic networks with unknown and unpredictable topology distributions. Our approach is shown to provide significantly lower Convergence Errors than existing random and degree-based leader selection methods in a numerical study.
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leader selection for minimizing Convergence Error in leader follower systems a supermodular optimization approach
Modeling and Optimization in Mobile Ad-Hoc and Wireless Networks, 2012Co-Authors: Andrew Clark, Linda Bushnell, Radha PoovendranAbstract:In leader-follower systems, follower nodes receive inputs from a set of leader nodes, exchange information, and update their states according to an iterative algorithm. In such algorithms, the node states may deviate from their desired values before the algorithm converges, leading to disruptions in network performance. In this paper, we study the problem of choosing leader nodes in order to minimize Convergence Errors. We first develop a connection between a class of weighted averaging algorithms and random walks on graphs, and then show that the Convergence Error is a supermodular function of the set of leader nodes. Based on the supermodularity of the Convergence Error, we derive efficient algorithms for selecting leader nodes that are within a provable bound of the optimum. Our approach is demonstrated through a simulation study.
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WiOpt - Leader selection for minimizing Convergence Error in leader-follower systems: A supermodular optimization approach
2012Co-Authors: Andrew Clark, Linda Bushnell, Radha PoovendranAbstract:In leader-follower systems, follower nodes receive inputs from a set of leader nodes, exchange information, and update their states according to an iterative algorithm. In such algorithms, the node states may deviate from their desired values before the algorithm converges, leading to disruptions in network performance. In this paper, we study the problem of choosing leader nodes in order to minimize Convergence Errors. We first develop a connection between a class of weighted averaging algorithms and random walks on graphs, and then show that the Convergence Error is a supermodular function of the set of leader nodes. Based on the supermodularity of the Convergence Error, we derive efficient algorithms for selecting leader nodes that are within a provable bound of the optimum. Our approach is demonstrated through a simulation study.
Robert Jeraj - One of the best experts on this subject based on the ideXlab platform.
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Optimizer Convergence and local minima Errors and their clinical importance
Physics in Medicine & Biology, 2017Co-Authors: Robert Jeraj, Thomas Rockwell Mackie, Chuan WuAbstract:Two of the Errors common in the inverse treatment planning optimization have been investigated. The first Error is the optimizer Convergence Error, which appears because of non-perfect Convergence to the global or local solution, usually caused by a non-zero stopping criterion. The second Error is the localminima Error, which occurs when the objective function is not convex and/or the feasible solution space is not convex. The magnitude of the Errors,their relative importance in comparison to other Errors as well as their clinical significance in terms of tumour control probability (TCP) and normal tissue complication probability (NTCP) were investigated. Two inherently different optimizers, a stochastic simulated annealing and deterministic gradient method were compared on a clinical example. It was found that for typical optimization the optimizer Convergence Errors are rather small, especially compared to other Convergence Errors, e.g., Convergence Errors due to inaccuracy of the current dose calculation algorithms. This indicates that stopping criteria could often be relaxed leading into optimization speed-ups. The local minima Errors were also found to be relatively small and typically in the range of the dose calculation Convergence Errors. Even for the cases where significantly higher objective function scores were obtained the local minima Errors were not significantly higher. Clinical evaluation of the optimizer Convergence Error showed good correlation between the Convergence of the clinical TCP or NTCP measures and Convergence of the physical dose distribution. On the other hand, the local minima Errors resulted in significantly different TCP or NTCP values (up to a factor of 2) indicating clinical importance of the local minima produced by physical optimization.
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The effect of dose calculation accuracy on inverse treatment planning.
Physics in medicine and biology, 2002Co-Authors: Robert Jeraj, P Keall, Jeffrey V. SiebersAbstract:The effect of dose calculation accuracy during inverse treatment planning for intensity modulated radiotherapy (IMRT) was studied in this work. Three dose calculation methods were compared: Monte Carlo, superposition and pencil beam. These algorithms were used to calculate beamlets. which were subsequently used by a simulated annealing algorithm to determine beamlet weights which comprised the optimal solution to the objective function. Three different cases (lung, prostate and head and neck) were investigated and several different objective functions were tested for their effect on inverse treatment planning. It is shown that the use of inaccurate dose calculation introduces two Errors in a treatment plan, a systematic Error and a Convergence Error. The systematic Error is present because of the inaccuracy of the dose calculation algorithm. The Convergence Error appears because the optimal intensity distribution for inaccurate beamlets differs from the optimal solution for the accurate beamlets. While the systematic Error for superposition was found to be approximately 1% of Dmax in the tumour and slightly larger outside, the Error for the pencil beam method is typically approximately 5% of Dmax and is rather insensitive to the given objectives. On the other hand, the Convergence Error was found to be very sensitive to the objective function, is only slightly correlated to the systematic Error and should be determined for each case individually. Our results suggest that because of the large systematic and Convergence Errors, inverse treatment planning systems based on pencil beam algorithms alone should be upgraded either to superposition or Monte Carlo based dose calculations.