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D Henderson - One of the best experts on this subject based on the ideXlab platform.
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su dd a2 04 use of the adjoint analysis based Greedy Heuristic algorithms in treatment planning for ldr brachytherapy of the prostate and hdr brachytherapy using multicatheter breast implant technique
Medical Physics, 2009Co-Authors: V Chaswal, B Thomadsen, D HendersonAbstract:Purpose: To demonstrate use of the adjoint analysis based Greedy Heuristic (GH) algorithms in treatment planning for LDR brachytherapy of the prostate using directional sources and multicatheter‐HDR breast implant. Method and Materials: Two adjoint analysis based GH treatment planning tools are developed; The first, for a directional LDR brachytherapy application and the second, for a HDR‐multicatheter brachytherapy application. Both the problems have an extra degree of freedom compared with conventional, binary LDR treatment planning — the seed rotational orientation in directional LDR brachytherapy and the variable dwell‐time in HDR brachytherapy. The Greedy Heuristic treatment planning algorithm uses adjoint‐based ROI‐sensitivity‐fields to search for the best available source type and orientation, or dwell time increment at a source‐location in steps to build either a seed‐needle‐source‐orientation distribution or dwell‐location‐dwell‐time distribution solution non‐iteratively. The GH treatment planning for LDR technique is based on dose‐distributionoptimization and that for HDR technique is based on dose‐homogeneityoptimization.Results: The treatment plans generated by the Greedy Heuristic algorithm using the directional sources resulted in target coverage with V100 >98% and remarkably better OAR‐sparing owing to the directional dose properties of the sources, as seen from the DVH analysis and evaluation parameters comparison on 6 prostate cases when compared with conventional LDR‐brachytherapy. The multicatheter‐HDR brachytherapy dwell‐time optimization generated treatment plan with a target coverage V100 >96%, skin‐sparing (D85=4.5%), few hot‐spots and a dose homogeneity index of 0.82. Conclusion: Greedy Heuristic algorithms coupled with Greedy criterions based on ROI‐sensitivity fields of dose response and dose‐homogeneity response are utilized as efficient tools for fast and reproducible treatment planning solutions in LDR and HDR brachytherapy.
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SU‐DD‐A2‐04: Use of the Adjoint Analysis Based Greedy Heuristic Algorithms in Treatment Planning for LDR Brachytherapy of the Prostate and HDR Brachytherapy Using Multicatheter Breast Implant Technique
Medical Physics, 2009Co-Authors: V Chaswal, B Thomadsen, D HendersonAbstract:Purpose: To demonstrate use of the adjoint analysis based Greedy Heuristic (GH) algorithms in treatment planning for LDR brachytherapy of the prostate using directional sources and multicatheter‐HDR breast implant. Method and Materials: Two adjoint analysis based GH treatment planning tools are developed; The first, for a directional LDR brachytherapy application and the second, for a HDR‐multicatheter brachytherapy application. Both the problems have an extra degree of freedom compared with conventional, binary LDR treatment planning — the seed rotational orientation in directional LDR brachytherapy and the variable dwell‐time in HDR brachytherapy. The Greedy Heuristic treatment planning algorithm uses adjoint‐based ROI‐sensitivity‐fields to search for the best available source type and orientation, or dwell time increment at a source‐location in steps to build either a seed‐needle‐source‐orientation distribution or dwell‐location‐dwell‐time distribution solution non‐iteratively. The GH treatment planning for LDR technique is based on dose‐distributionoptimization and that for HDR technique is based on dose‐homogeneityoptimization.Results: The treatment plans generated by the Greedy Heuristic algorithm using the directional sources resulted in target coverage with V100 >98% and remarkably better OAR‐sparing owing to the directional dose properties of the sources, as seen from the DVH analysis and evaluation parameters comparison on 6 prostate cases when compared with conventional LDR‐brachytherapy. The multicatheter‐HDR brachytherapy dwell‐time optimization generated treatment plan with a target coverage V100 >96%, skin‐sparing (D85=4.5%), few hot‐spots and a dose homogeneity index of 0.82. Conclusion: Greedy Heuristic algorithms coupled with Greedy criterions based on ROI‐sensitivity fields of dose response and dose‐homogeneity response are utilized as efficient tools for fast and reproducible treatment planning solutions in LDR and HDR brachytherapy.
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a Greedy Heuristic using adjoint functions for the optimization of seed and needle configurations in prostate seed implant
Physics in Medicine and Biology, 2007Co-Authors: S Yoo, Michael E Kowalok, Bruce R Thomadsen, D HendersonAbstract:We continue our work on the development of an efficient treatment-planning algorithm for prostate seed implants by incorporation of an automated seed and needle configuration routine. The treatment-planning algorithm is based on region of interest (ROI) adjoint functions and a Greedy Heuristic. As defined in this work, the adjoint function of an ROI is the sensitivity of the average dose in the ROI to a unit-strength brachytherapy source at any seed position. The Greedy Heuristic uses a ratio of target and critical structure adjoint functions to rank seed positions according to their ability to irradiate the target ROI while sparing critical structure ROIs. Because seed positions are ranked in advance and because the Greedy Heuristic does not modify previously selected seed positions, the Greedy Heuristic constructs a complete seed configuration quickly. Isodose surface constraints determine the search space and the needle constraint limits the number of needles. This study additionally includes a methodology that scans possible combinations of these constraint values automatically. This automated selection scheme saves the user the effort of manually searching constraint values. With this method, clinically acceptable treatment plans are obtained in less than 2 min. For comparison, the branch-and-bound method used to solve a mixed integer-programming model took close to 2.5 h to arrive at a feasible solution. Both methods achieved good treatment plans, but the speedup provided by the Greedy Heuristic was a factor of approximately 100. This attribute makes this algorithm suitable for intra-operative real-time treatment planning.
V Chaswal - One of the best experts on this subject based on the ideXlab platform.
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su dd a2 04 use of the adjoint analysis based Greedy Heuristic algorithms in treatment planning for ldr brachytherapy of the prostate and hdr brachytherapy using multicatheter breast implant technique
Medical Physics, 2009Co-Authors: V Chaswal, B Thomadsen, D HendersonAbstract:Purpose: To demonstrate use of the adjoint analysis based Greedy Heuristic (GH) algorithms in treatment planning for LDR brachytherapy of the prostate using directional sources and multicatheter‐HDR breast implant. Method and Materials: Two adjoint analysis based GH treatment planning tools are developed; The first, for a directional LDR brachytherapy application and the second, for a HDR‐multicatheter brachytherapy application. Both the problems have an extra degree of freedom compared with conventional, binary LDR treatment planning — the seed rotational orientation in directional LDR brachytherapy and the variable dwell‐time in HDR brachytherapy. The Greedy Heuristic treatment planning algorithm uses adjoint‐based ROI‐sensitivity‐fields to search for the best available source type and orientation, or dwell time increment at a source‐location in steps to build either a seed‐needle‐source‐orientation distribution or dwell‐location‐dwell‐time distribution solution non‐iteratively. The GH treatment planning for LDR technique is based on dose‐distributionoptimization and that for HDR technique is based on dose‐homogeneityoptimization.Results: The treatment plans generated by the Greedy Heuristic algorithm using the directional sources resulted in target coverage with V100 >98% and remarkably better OAR‐sparing owing to the directional dose properties of the sources, as seen from the DVH analysis and evaluation parameters comparison on 6 prostate cases when compared with conventional LDR‐brachytherapy. The multicatheter‐HDR brachytherapy dwell‐time optimization generated treatment plan with a target coverage V100 >96%, skin‐sparing (D85=4.5%), few hot‐spots and a dose homogeneity index of 0.82. Conclusion: Greedy Heuristic algorithms coupled with Greedy criterions based on ROI‐sensitivity fields of dose response and dose‐homogeneity response are utilized as efficient tools for fast and reproducible treatment planning solutions in LDR and HDR brachytherapy.
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SU‐DD‐A2‐04: Use of the Adjoint Analysis Based Greedy Heuristic Algorithms in Treatment Planning for LDR Brachytherapy of the Prostate and HDR Brachytherapy Using Multicatheter Breast Implant Technique
Medical Physics, 2009Co-Authors: V Chaswal, B Thomadsen, D HendersonAbstract:Purpose: To demonstrate use of the adjoint analysis based Greedy Heuristic (GH) algorithms in treatment planning for LDR brachytherapy of the prostate using directional sources and multicatheter‐HDR breast implant. Method and Materials: Two adjoint analysis based GH treatment planning tools are developed; The first, for a directional LDR brachytherapy application and the second, for a HDR‐multicatheter brachytherapy application. Both the problems have an extra degree of freedom compared with conventional, binary LDR treatment planning — the seed rotational orientation in directional LDR brachytherapy and the variable dwell‐time in HDR brachytherapy. The Greedy Heuristic treatment planning algorithm uses adjoint‐based ROI‐sensitivity‐fields to search for the best available source type and orientation, or dwell time increment at a source‐location in steps to build either a seed‐needle‐source‐orientation distribution or dwell‐location‐dwell‐time distribution solution non‐iteratively. The GH treatment planning for LDR technique is based on dose‐distributionoptimization and that for HDR technique is based on dose‐homogeneityoptimization.Results: The treatment plans generated by the Greedy Heuristic algorithm using the directional sources resulted in target coverage with V100 >98% and remarkably better OAR‐sparing owing to the directional dose properties of the sources, as seen from the DVH analysis and evaluation parameters comparison on 6 prostate cases when compared with conventional LDR‐brachytherapy. The multicatheter‐HDR brachytherapy dwell‐time optimization generated treatment plan with a target coverage V100 >96%, skin‐sparing (D85=4.5%), few hot‐spots and a dose homogeneity index of 0.82. Conclusion: Greedy Heuristic algorithms coupled with Greedy criterions based on ROI‐sensitivity fields of dose response and dose‐homogeneity response are utilized as efficient tools for fast and reproducible treatment planning solutions in LDR and HDR brachytherapy.
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Interstitial prostate implant brachytherapy using an automated, 3-D Greedy Heuristic optimization and I-125 directional sources
Brachytherapy, 2007Co-Authors: V Chaswal, Bruce R Thomadsen, Liyong Lin, Douglass L. HendersonAbstract:REFERENCES 1. S. Yoo, et al “Treatment planning for prostate brachytherapy using region of interest adjoint functions and a Greedy Heuristic” Phys. Med. Biol. 48(24), 4077-90 (2003) 2. V. Chaswal, et al “Multi-species prostate implant treatment-plans incorporating Ir and I using a Greedy Heuristic based 3-D optimization algorithm”, in Med. Phys. 34(2), 436-444 (2007) 3. L. Lin, V. Chaswal, et al. “Novel prostate treatment using newly developing directional brachytherapy sources” Oral poster presentation at the AAPM annual meeting. July 2005. ABSTRACT
Jaspreet Singh Dhillon - One of the best experts on this subject based on the ideXlab platform.
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A Simple Opposition-based Greedy Heuristic Search for Dynamic Economic Thermal Power Dispatch
Electric Power Components and Systems, 2016Co-Authors: Manmohan Singh, Jaspreet Singh DhillonAbstract:AbstractThis article proposes a simple opposition-based Greedy Heuristic search to solve a dynamic thermal power dispatch problem as a non-linear constrained optimization problem in the constrained search space. Opposition-based learning is applied at two stages. First, an initial population is generated to select good candidates by extensively exploring the search space. Second, it is implemented for migration to maintain diversity in the set of feasible solutions. The proposed method applies a mutation strategy by perturbing the genes Heuristically and seeking a better one, which introduces parallelism and makes the algorithm Greedy for a better solution. The greediness and randomness pulls the algorithm toward a global solution. Acceleration of the algorithm is independent of any parameter tuning. Feasible solutions are achieved Heuristically by modifying the generation schedules within operating generation limits. Opposition-based Greedy Heuristic search has been implemented to analyze dynamic economi...
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Multiobjective thermal power dispatch using opposition-based Greedy Heuristic search
International Journal of Electrical Power & Energy Systems, 2016Co-Authors: Manmohan Singh, Jaspreet Singh DhillonAbstract:Abstract This paper proposes an opposition-based Greedy Heuristic search (OGHS) strategy to solve multi-objective thermal power dispatch problem as a non-linear constrained optimization problem considering operating cost and pollutant emissions as competing objectives. The optimization problem is solved to find global solution, in case any one objective function is non-convex and non-differentiable. To generate initial population opposition-based learning is applied to select good candidates by exploring the search space extensively. Further, opposition-based learning is exploited for migration to maintain the diversity in the set of feasible solutions. Proposed method applies mutation strategy by perturbing the genes Heuristically and seeking better one. This concept introduces parallelism and makes the algorithm always Greedy for better solution. The greediness and randomness pulls the algorithm towards the global solution. The algorithm is also self sufficient without the need of tuning any parameter that effects acceleration of the algorithm. Fuzzy-theory is employed for decision-making that selects best solution from available non-inferior solutions. Feasible solution is also achieved Heuristically that modifies the generation-schedule and avoids violation of operating generation limits. Proposed method has been implemented to analyze economic and multi-objective thermal power dispatch problems considering ramp-rate limits, prohibited-operating-zones, valve-point-loading effects, multiple-fuel options, environmental effects, and exact transmission losses encountered in realistic power system operation. The validity of proposed method is demonstrated on medium and large power systems. Proposed optimization technique is emerged out to compete with existing solution techniques. Wilcoxon signed-rank test for independent samples also proves the supremacy of proposed algorithm OGHS.
B Thomadsen - One of the best experts on this subject based on the ideXlab platform.
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SU‐DD‐A2‐04: Use of the Adjoint Analysis Based Greedy Heuristic Algorithms in Treatment Planning for LDR Brachytherapy of the Prostate and HDR Brachytherapy Using Multicatheter Breast Implant Technique
Medical Physics, 2009Co-Authors: V Chaswal, B Thomadsen, D HendersonAbstract:Purpose: To demonstrate use of the adjoint analysis based Greedy Heuristic (GH) algorithms in treatment planning for LDR brachytherapy of the prostate using directional sources and multicatheter‐HDR breast implant. Method and Materials: Two adjoint analysis based GH treatment planning tools are developed; The first, for a directional LDR brachytherapy application and the second, for a HDR‐multicatheter brachytherapy application. Both the problems have an extra degree of freedom compared with conventional, binary LDR treatment planning — the seed rotational orientation in directional LDR brachytherapy and the variable dwell‐time in HDR brachytherapy. The Greedy Heuristic treatment planning algorithm uses adjoint‐based ROI‐sensitivity‐fields to search for the best available source type and orientation, or dwell time increment at a source‐location in steps to build either a seed‐needle‐source‐orientation distribution or dwell‐location‐dwell‐time distribution solution non‐iteratively. The GH treatment planning for LDR technique is based on dose‐distributionoptimization and that for HDR technique is based on dose‐homogeneityoptimization.Results: The treatment plans generated by the Greedy Heuristic algorithm using the directional sources resulted in target coverage with V100 >98% and remarkably better OAR‐sparing owing to the directional dose properties of the sources, as seen from the DVH analysis and evaluation parameters comparison on 6 prostate cases when compared with conventional LDR‐brachytherapy. The multicatheter‐HDR brachytherapy dwell‐time optimization generated treatment plan with a target coverage V100 >96%, skin‐sparing (D85=4.5%), few hot‐spots and a dose homogeneity index of 0.82. Conclusion: Greedy Heuristic algorithms coupled with Greedy criterions based on ROI‐sensitivity fields of dose response and dose‐homogeneity response are utilized as efficient tools for fast and reproducible treatment planning solutions in LDR and HDR brachytherapy.
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su dd a2 04 use of the adjoint analysis based Greedy Heuristic algorithms in treatment planning for ldr brachytherapy of the prostate and hdr brachytherapy using multicatheter breast implant technique
Medical Physics, 2009Co-Authors: V Chaswal, B Thomadsen, D HendersonAbstract:Purpose: To demonstrate use of the adjoint analysis based Greedy Heuristic (GH) algorithms in treatment planning for LDR brachytherapy of the prostate using directional sources and multicatheter‐HDR breast implant. Method and Materials: Two adjoint analysis based GH treatment planning tools are developed; The first, for a directional LDR brachytherapy application and the second, for a HDR‐multicatheter brachytherapy application. Both the problems have an extra degree of freedom compared with conventional, binary LDR treatment planning — the seed rotational orientation in directional LDR brachytherapy and the variable dwell‐time in HDR brachytherapy. The Greedy Heuristic treatment planning algorithm uses adjoint‐based ROI‐sensitivity‐fields to search for the best available source type and orientation, or dwell time increment at a source‐location in steps to build either a seed‐needle‐source‐orientation distribution or dwell‐location‐dwell‐time distribution solution non‐iteratively. The GH treatment planning for LDR technique is based on dose‐distributionoptimization and that for HDR technique is based on dose‐homogeneityoptimization.Results: The treatment plans generated by the Greedy Heuristic algorithm using the directional sources resulted in target coverage with V100 >98% and remarkably better OAR‐sparing owing to the directional dose properties of the sources, as seen from the DVH analysis and evaluation parameters comparison on 6 prostate cases when compared with conventional LDR‐brachytherapy. The multicatheter‐HDR brachytherapy dwell‐time optimization generated treatment plan with a target coverage V100 >96%, skin‐sparing (D85=4.5%), few hot‐spots and a dose homogeneity index of 0.82. Conclusion: Greedy Heuristic algorithms coupled with Greedy criterions based on ROI‐sensitivity fields of dose response and dose‐homogeneity response are utilized as efficient tools for fast and reproducible treatment planning solutions in LDR and HDR brachytherapy.
Changhai Nie - One of the best experts on this subject based on the ideXlab platform.
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Greedy Heuristic algorithms to generate variable strength combinatorial test suite
International Conference on Quality Software, 2008Co-Authors: Ziyuan Wang, Changhai NieAbstract:Combinatorial testing is a practical software testing approach that has been widely used in practice. Most research and applications of such approach focus on N-way combinatorial testing that provides a minimum coverage of all N-way interactions among factor. However, the strengths of different interactions may not be a fixed integer N, but a variable. Therefore, variable strength combinatorial testing approach is necessary in applications. Existing variable strength combinatorial testing, which allows some interactions have a higher strength than others, has a limitation that such higher-strength interactions must be disjoint. To avoid such a limitation, an improved variable strength combinatorial testing approach, which makes a more sufficient consideration on actual interaction relationship, is proposed in this article. Furthermore, two Greedy Heuristic algorithms are also proposed to generate combinatorial test suite. Compared to some existing algorithms and tools, the proposed algorithms have advantages on both the execution effectiveness and the optimality of generated test suite. Experimental results can prove such advantages.
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QSIC - Greedy Heuristic Algorithms to Generate Variable Strength Combinatorial Test Suite
2008 The Eighth International Conference on Quality Software, 2008Co-Authors: Ziyuan Wang, Changhai NieAbstract:Combinatorial testing is a practical software testing approach that has been widely used in practice. Most research and applications of such approach focus on N-way combinatorial testing that provides a minimum coverage of all N-way interactions among factor. However, the strengths of different interactions may not be a fixed integer N, but a variable. Therefore, variable strength combinatorial testing approach is necessary in applications. Existing variable strength combinatorial testing, which allows some interactions have a higher strength than others, has a limitation that such higher-strength interactions must be disjoint. To avoid such a limitation, an improved variable strength combinatorial testing approach, which makes a more sufficient consideration on actual interaction relationship, is proposed in this article. Furthermore, two Greedy Heuristic algorithms are also proposed to generate combinatorial test suite. Compared to some existing algorithms and tools, the proposed algorithms have advantages on both the execution effectiveness and the optimality of generated test suite. Experimental results can prove such advantages.
