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David Craft - One of the best experts on this subject based on the ideXlab platform.
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exploring trade offs between vmat dose Quality and delivery efficiency using a network optimization approach
Physics in Medicine and Biology, 2012Co-Authors: Ehsan Salari, Jeremiah Wala, David CraftAbstract:To formulate and solve the fluence-map merging procedure of the recently-published VMAT treatment-Plan optimization method, called vmerge, as a bi-criteria optimization problem. Using an exact merging method rather than the previously-used heuristic, we are able to better characterize the trade-off between the delivery efficiency and dose Quality. vmerge begins with a solution of the fluence-map optimization problem with 180 equi-spaced beams that yields the ?ideal? dose distribution. Neighboring fluence maps are then successively merged, meaning that they are added together and delivered as a single map. The merging process improves the delivery efficiency at the expense of deviating from the initial high-Quality dose distribution. We replace the original merging heuristic by considering the merging problem as a discrete bi-criteria optimization problem with the objectives of maximizing the treatment efficiency and minimizing the deviation from the ideal dose. We formulate this using a network-flow model that represents the merging problem. Since the problem is discrete and thus non-convex, we employ a customized box algorithm to characterize the Pareto frontier. The Pareto frontier is then used as a benchmark to evaluate the performance of the standard vmerge algorithm as well as two other similar heuristics. We test the exact and heuristic merging approaches on a pancreas and a prostate cancer case. For both cases, the shape of the Pareto frontier suggests that starting from a high-Quality Plan, we can obtain efficient VMAT Plans through merging neighboring fluence maps without substantially deviating from the initial dose distribution. The trade-off curves obtained by the various heuristics are contrasted and shown to all be equally capable of initial Plan simplifications, but to deviate in Quality for more drastic efficiency improvements. This work presents a network optimization approach to the merging problem. Contrasting the trade-off curves of the merging heuristics against the Pareto approximation validates that heuristic approaches are capable of achieving high-Quality merged Plans that lie close to the Pareto frontier.
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multicriteria vmat optimization
Medical Physics, 2012Co-Authors: David Craft, Ehsan Salari, Jeremiah Wala, D Mcquaid, Wei Chen, Thomas BortfeldAbstract:Purpose: To make the Planning of volumetric modulated arc therapy (VMAT) faster and to explore the tradeoffs between Planning objectives and delivery efficiency. Methods: A convex multicriteria dose optimization problem is solved for an angular grid of 180 equi-spaced beams. This allows the Planner to navigate the ideal dose distribution Pareto surface and select a Plan of desired target coverage versus organ at risk sparing. The selected Plan is then made VMAT deliverable by a fluence map merging and sequencing algorithm, which combines neighboring fluence maps based on a similarity score and then delivers the merged maps together, simplifying delivery. Successive merges are made as long as the dose distribution Quality is maintained. The complete algorithm is called VMERGE. Results:VMERGE is applied to three cases: a prostate, a pancreas, and a brain. In each case, the selected Pareto-optimal Plan is matched almost exactly with the VMAT merging routine, resulting in a high Quality Plan delivered with a single arc in less than 5 min on average. Conclusions:VMERGE offers significant improvements over existing VMAT algorithms. The first is the multicriteria Planning aspect, which greatly speeds up Planning time and allows the user to select the Plan, which represents the most desirable compromise between target coverage and organ at risk sparing. The second is the user-chosen epsilon-optimality guarantee of the final VMAT Plan. Finally, the user can explore the tradeoff between delivery time and Plan Quality, which is a fundamental aspect of VMAT that cannot be easily investigated with current commercial Planning systems.
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multicriteria vmat optimization
arXiv: Medical Physics, 2011Co-Authors: David Craft, Ehsan Salari, Jeremiah Wala, D Mcquaid, Wei Chen, Thomas BortfeldAbstract:Purpose: To make the Planning of volumetric modulated arc therapy (VMAT) faster and to explore the tradeoffs between Planning objectives and delivery efficiency. Methods: A convex multicriteria dose optimization problem is solved for an angular grid of 180 equi-spaced beams. This allows the Planner to navigate the ideal dose distribution Pareto surface and select a Plan of desired target coverage versus organ at risk sparing. The selected Plan is then made VMAT deliverable by a fluence map merging and sequencing algorithm, which combines neighboring fluence maps based on a similarity score and then delivers the merged maps together, simplifying delivery. Successive merges are made as long as the dose distribution Quality is maintained. The complete algorithm is called VMERGE. Results: VMERGE is applied to three cases: a prostate, a pancreas, and a brain. In each case, the selected Pareto-optimal Plan is matched almost exactly with the VMAT merging routine, resulting in a high Quality Plan delivered with a single arc in less than five minutes on average. VMERGE offers significant improvements over existing VMAT algorithms. The first is the multicriteria Planning aspect, which greatly speeds up Planning time and allows the user to select the Plan which represents the most desirable compromise between target coverage and organ at risk sparing. The second is the user-chosen epsilon-optimality guarantee of the final VMAT Plan. Finally, the user can explore the tradeoff between delivery time and Plan Quality, which is a fundamental aspect of VMAT that cannot be easily investigated with current commercial Planning systems.
Thomas Bortfeld - One of the best experts on this subject based on the ideXlab platform.
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multicriteria vmat optimization
Medical Physics, 2012Co-Authors: David Craft, Ehsan Salari, Jeremiah Wala, D Mcquaid, Wei Chen, Thomas BortfeldAbstract:Purpose: To make the Planning of volumetric modulated arc therapy (VMAT) faster and to explore the tradeoffs between Planning objectives and delivery efficiency. Methods: A convex multicriteria dose optimization problem is solved for an angular grid of 180 equi-spaced beams. This allows the Planner to navigate the ideal dose distribution Pareto surface and select a Plan of desired target coverage versus organ at risk sparing. The selected Plan is then made VMAT deliverable by a fluence map merging and sequencing algorithm, which combines neighboring fluence maps based on a similarity score and then delivers the merged maps together, simplifying delivery. Successive merges are made as long as the dose distribution Quality is maintained. The complete algorithm is called VMERGE. Results:VMERGE is applied to three cases: a prostate, a pancreas, and a brain. In each case, the selected Pareto-optimal Plan is matched almost exactly with the VMAT merging routine, resulting in a high Quality Plan delivered with a single arc in less than 5 min on average. Conclusions:VMERGE offers significant improvements over existing VMAT algorithms. The first is the multicriteria Planning aspect, which greatly speeds up Planning time and allows the user to select the Plan, which represents the most desirable compromise between target coverage and organ at risk sparing. The second is the user-chosen epsilon-optimality guarantee of the final VMAT Plan. Finally, the user can explore the tradeoff between delivery time and Plan Quality, which is a fundamental aspect of VMAT that cannot be easily investigated with current commercial Planning systems.
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multicriteria vmat optimization
arXiv: Medical Physics, 2011Co-Authors: David Craft, Ehsan Salari, Jeremiah Wala, D Mcquaid, Wei Chen, Thomas BortfeldAbstract:Purpose: To make the Planning of volumetric modulated arc therapy (VMAT) faster and to explore the tradeoffs between Planning objectives and delivery efficiency. Methods: A convex multicriteria dose optimization problem is solved for an angular grid of 180 equi-spaced beams. This allows the Planner to navigate the ideal dose distribution Pareto surface and select a Plan of desired target coverage versus organ at risk sparing. The selected Plan is then made VMAT deliverable by a fluence map merging and sequencing algorithm, which combines neighboring fluence maps based on a similarity score and then delivers the merged maps together, simplifying delivery. Successive merges are made as long as the dose distribution Quality is maintained. The complete algorithm is called VMERGE. Results: VMERGE is applied to three cases: a prostate, a pancreas, and a brain. In each case, the selected Pareto-optimal Plan is matched almost exactly with the VMAT merging routine, resulting in a high Quality Plan delivered with a single arc in less than five minutes on average. VMERGE offers significant improvements over existing VMAT algorithms. The first is the multicriteria Planning aspect, which greatly speeds up Planning time and allows the user to select the Plan which represents the most desirable compromise between target coverage and organ at risk sparing. The second is the user-chosen epsilon-optimality guarantee of the final VMAT Plan. Finally, the user can explore the tradeoff between delivery time and Plan Quality, which is a fundamental aspect of VMAT that cannot be easily investigated with current commercial Planning systems.
Ehsan Salari - One of the best experts on this subject based on the ideXlab platform.
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exploring trade offs between vmat dose Quality and delivery efficiency using a network optimization approach
Physics in Medicine and Biology, 2012Co-Authors: Ehsan Salari, Jeremiah Wala, David CraftAbstract:To formulate and solve the fluence-map merging procedure of the recently-published VMAT treatment-Plan optimization method, called vmerge, as a bi-criteria optimization problem. Using an exact merging method rather than the previously-used heuristic, we are able to better characterize the trade-off between the delivery efficiency and dose Quality. vmerge begins with a solution of the fluence-map optimization problem with 180 equi-spaced beams that yields the ?ideal? dose distribution. Neighboring fluence maps are then successively merged, meaning that they are added together and delivered as a single map. The merging process improves the delivery efficiency at the expense of deviating from the initial high-Quality dose distribution. We replace the original merging heuristic by considering the merging problem as a discrete bi-criteria optimization problem with the objectives of maximizing the treatment efficiency and minimizing the deviation from the ideal dose. We formulate this using a network-flow model that represents the merging problem. Since the problem is discrete and thus non-convex, we employ a customized box algorithm to characterize the Pareto frontier. The Pareto frontier is then used as a benchmark to evaluate the performance of the standard vmerge algorithm as well as two other similar heuristics. We test the exact and heuristic merging approaches on a pancreas and a prostate cancer case. For both cases, the shape of the Pareto frontier suggests that starting from a high-Quality Plan, we can obtain efficient VMAT Plans through merging neighboring fluence maps without substantially deviating from the initial dose distribution. The trade-off curves obtained by the various heuristics are contrasted and shown to all be equally capable of initial Plan simplifications, but to deviate in Quality for more drastic efficiency improvements. This work presents a network optimization approach to the merging problem. Contrasting the trade-off curves of the merging heuristics against the Pareto approximation validates that heuristic approaches are capable of achieving high-Quality merged Plans that lie close to the Pareto frontier.
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multicriteria vmat optimization
Medical Physics, 2012Co-Authors: David Craft, Ehsan Salari, Jeremiah Wala, D Mcquaid, Wei Chen, Thomas BortfeldAbstract:Purpose: To make the Planning of volumetric modulated arc therapy (VMAT) faster and to explore the tradeoffs between Planning objectives and delivery efficiency. Methods: A convex multicriteria dose optimization problem is solved for an angular grid of 180 equi-spaced beams. This allows the Planner to navigate the ideal dose distribution Pareto surface and select a Plan of desired target coverage versus organ at risk sparing. The selected Plan is then made VMAT deliverable by a fluence map merging and sequencing algorithm, which combines neighboring fluence maps based on a similarity score and then delivers the merged maps together, simplifying delivery. Successive merges are made as long as the dose distribution Quality is maintained. The complete algorithm is called VMERGE. Results:VMERGE is applied to three cases: a prostate, a pancreas, and a brain. In each case, the selected Pareto-optimal Plan is matched almost exactly with the VMAT merging routine, resulting in a high Quality Plan delivered with a single arc in less than 5 min on average. Conclusions:VMERGE offers significant improvements over existing VMAT algorithms. The first is the multicriteria Planning aspect, which greatly speeds up Planning time and allows the user to select the Plan, which represents the most desirable compromise between target coverage and organ at risk sparing. The second is the user-chosen epsilon-optimality guarantee of the final VMAT Plan. Finally, the user can explore the tradeoff between delivery time and Plan Quality, which is a fundamental aspect of VMAT that cannot be easily investigated with current commercial Planning systems.
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multicriteria vmat optimization
arXiv: Medical Physics, 2011Co-Authors: David Craft, Ehsan Salari, Jeremiah Wala, D Mcquaid, Wei Chen, Thomas BortfeldAbstract:Purpose: To make the Planning of volumetric modulated arc therapy (VMAT) faster and to explore the tradeoffs between Planning objectives and delivery efficiency. Methods: A convex multicriteria dose optimization problem is solved for an angular grid of 180 equi-spaced beams. This allows the Planner to navigate the ideal dose distribution Pareto surface and select a Plan of desired target coverage versus organ at risk sparing. The selected Plan is then made VMAT deliverable by a fluence map merging and sequencing algorithm, which combines neighboring fluence maps based on a similarity score and then delivers the merged maps together, simplifying delivery. Successive merges are made as long as the dose distribution Quality is maintained. The complete algorithm is called VMERGE. Results: VMERGE is applied to three cases: a prostate, a pancreas, and a brain. In each case, the selected Pareto-optimal Plan is matched almost exactly with the VMAT merging routine, resulting in a high Quality Plan delivered with a single arc in less than five minutes on average. VMERGE offers significant improvements over existing VMAT algorithms. The first is the multicriteria Planning aspect, which greatly speeds up Planning time and allows the user to select the Plan which represents the most desirable compromise between target coverage and organ at risk sparing. The second is the user-chosen epsilon-optimality guarantee of the final VMAT Plan. Finally, the user can explore the tradeoff between delivery time and Plan Quality, which is a fundamental aspect of VMAT that cannot be easily investigated with current commercial Planning systems.
Jeremiah Wala - One of the best experts on this subject based on the ideXlab platform.
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exploring trade offs between vmat dose Quality and delivery efficiency using a network optimization approach
Physics in Medicine and Biology, 2012Co-Authors: Ehsan Salari, Jeremiah Wala, David CraftAbstract:To formulate and solve the fluence-map merging procedure of the recently-published VMAT treatment-Plan optimization method, called vmerge, as a bi-criteria optimization problem. Using an exact merging method rather than the previously-used heuristic, we are able to better characterize the trade-off between the delivery efficiency and dose Quality. vmerge begins with a solution of the fluence-map optimization problem with 180 equi-spaced beams that yields the ?ideal? dose distribution. Neighboring fluence maps are then successively merged, meaning that they are added together and delivered as a single map. The merging process improves the delivery efficiency at the expense of deviating from the initial high-Quality dose distribution. We replace the original merging heuristic by considering the merging problem as a discrete bi-criteria optimization problem with the objectives of maximizing the treatment efficiency and minimizing the deviation from the ideal dose. We formulate this using a network-flow model that represents the merging problem. Since the problem is discrete and thus non-convex, we employ a customized box algorithm to characterize the Pareto frontier. The Pareto frontier is then used as a benchmark to evaluate the performance of the standard vmerge algorithm as well as two other similar heuristics. We test the exact and heuristic merging approaches on a pancreas and a prostate cancer case. For both cases, the shape of the Pareto frontier suggests that starting from a high-Quality Plan, we can obtain efficient VMAT Plans through merging neighboring fluence maps without substantially deviating from the initial dose distribution. The trade-off curves obtained by the various heuristics are contrasted and shown to all be equally capable of initial Plan simplifications, but to deviate in Quality for more drastic efficiency improvements. This work presents a network optimization approach to the merging problem. Contrasting the trade-off curves of the merging heuristics against the Pareto approximation validates that heuristic approaches are capable of achieving high-Quality merged Plans that lie close to the Pareto frontier.
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multicriteria vmat optimization
Medical Physics, 2012Co-Authors: David Craft, Ehsan Salari, Jeremiah Wala, D Mcquaid, Wei Chen, Thomas BortfeldAbstract:Purpose: To make the Planning of volumetric modulated arc therapy (VMAT) faster and to explore the tradeoffs between Planning objectives and delivery efficiency. Methods: A convex multicriteria dose optimization problem is solved for an angular grid of 180 equi-spaced beams. This allows the Planner to navigate the ideal dose distribution Pareto surface and select a Plan of desired target coverage versus organ at risk sparing. The selected Plan is then made VMAT deliverable by a fluence map merging and sequencing algorithm, which combines neighboring fluence maps based on a similarity score and then delivers the merged maps together, simplifying delivery. Successive merges are made as long as the dose distribution Quality is maintained. The complete algorithm is called VMERGE. Results:VMERGE is applied to three cases: a prostate, a pancreas, and a brain. In each case, the selected Pareto-optimal Plan is matched almost exactly with the VMAT merging routine, resulting in a high Quality Plan delivered with a single arc in less than 5 min on average. Conclusions:VMERGE offers significant improvements over existing VMAT algorithms. The first is the multicriteria Planning aspect, which greatly speeds up Planning time and allows the user to select the Plan, which represents the most desirable compromise between target coverage and organ at risk sparing. The second is the user-chosen epsilon-optimality guarantee of the final VMAT Plan. Finally, the user can explore the tradeoff between delivery time and Plan Quality, which is a fundamental aspect of VMAT that cannot be easily investigated with current commercial Planning systems.
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multicriteria vmat optimization
arXiv: Medical Physics, 2011Co-Authors: David Craft, Ehsan Salari, Jeremiah Wala, D Mcquaid, Wei Chen, Thomas BortfeldAbstract:Purpose: To make the Planning of volumetric modulated arc therapy (VMAT) faster and to explore the tradeoffs between Planning objectives and delivery efficiency. Methods: A convex multicriteria dose optimization problem is solved for an angular grid of 180 equi-spaced beams. This allows the Planner to navigate the ideal dose distribution Pareto surface and select a Plan of desired target coverage versus organ at risk sparing. The selected Plan is then made VMAT deliverable by a fluence map merging and sequencing algorithm, which combines neighboring fluence maps based on a similarity score and then delivers the merged maps together, simplifying delivery. Successive merges are made as long as the dose distribution Quality is maintained. The complete algorithm is called VMERGE. Results: VMERGE is applied to three cases: a prostate, a pancreas, and a brain. In each case, the selected Pareto-optimal Plan is matched almost exactly with the VMAT merging routine, resulting in a high Quality Plan delivered with a single arc in less than five minutes on average. VMERGE offers significant improvements over existing VMAT algorithms. The first is the multicriteria Planning aspect, which greatly speeds up Planning time and allows the user to select the Plan which represents the most desirable compromise between target coverage and organ at risk sparing. The second is the user-chosen epsilon-optimality guarantee of the final VMAT Plan. Finally, the user can explore the tradeoff between delivery time and Plan Quality, which is a fundamental aspect of VMAT that cannot be easily investigated with current commercial Planning systems.
Asmat Sanchez, Marlyn Elizabeth - One of the best experts on this subject based on the ideXlab platform.
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Modelo de gestión de calidad para la elaboración de expedientes técnicos de infraestructura educativa a cargo de la empresa 5YMAS
'Baishideng Publishing Group Inc.', 2021Co-Authors: Asmat Sanchez, Marlyn ElizabethAbstract:El presente trabajo de investigación trata sobre una propuesta de un Plan de Gestión de Calidad para la Elaboración de Expedientes Técnicos de Infraestructura Educativa a cargo de la Empresa 5YMAS, ante la inexistencia de un Plan de Calidad por parte de la empresa en mención, buscando minimizar la cantidad de Expedientes Técnicos que no son técnicamente adecuados para utilizarse en la fase de ejecución. Los procedimientos para el desarrollo de los objetivos contemplaron el uso de técnicas y herramientas. Las técnicas que se utilizaron para el desarrollo de los objetivos fueron el Juicio de Expertos, Estudios Comparativos, Tormenta de Ideas y las herramientas que se utilizaron fueron el Análisis de Costo de la Calidad, Matriz de Priorización y Diagrama de Flujos. Dentro de los resultados más importantes se diseñó una Matriz de Gestión de Calidad que contempla la secuencia de etapas que recorre el Expediente Técnico durante su elaboración. Como conclusión principal se obtuvo que para el presente caso los Costos de No Conformidad si eran mayores a los Costos de Conformidad, sustentando de ese modo la importante de la implementación de un Plan de Gestión de Calidad para la Empresa 5YMAS.This research work deals with a proposal for a Quality Management Plan for the Preparation of Technical Files on Educational Infrastructure for the 5YMAS Company, in the absence of a Quality Plan by the company in question, seeking minimum the number of Technical Files that are not technically adequate for the execution phase. The procedures for developing the objectives included the use of techniques and tools. The techniques that used for the development of the objectives were the Expert Judgment, Comparative Studies, Brainstorming and the tools that were used are the Quality Cost Analysis, Prioritization Matrix, Flow Diagram and Mind Mapping. Among the most important results, a Quality Management Matrix was designed to contemplates the sequence of stages recorded by the Technical File during its preparation. As a main conclusion, for the present case the NonConformity Costs were higher than the Conformity Costs, thus supporting the important of the implementation of a Quality Management Plan for the 5YMAS Company.Tesi
