Treatment planning of Electrochemotherapy (ECT) is designed by means of a genetic multi-objective optimization method: the needle position maximizing the electric field in the treated volume is searched for. NSGA algorithm is coupled with penalty function technique in order to identify the constrained Pareto front to select the best compromise solutions and discard the unfeasible ones. A 3D model is proposed to assess electric field distribution in a clinical case.
Optimization for ECT treatment planning
E. Sieni
2012-01-01
Abstract
Treatment planning of Electrochemotherapy (ECT) is designed by means of a genetic multi-objective optimization method: the needle position maximizing the electric field in the treated volume is searched for. NSGA algorithm is coupled with penalty function technique in order to identify the constrained Pareto front to select the best compromise solutions and discard the unfeasible ones. A 3D model is proposed to assess electric field distribution in a clinical case.
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