Towards the optimization of ballistics in proton therapy using genetic algorithms: implementation issues

• URL: http://arxiv.org/abs/2205.08283v1
• Date: Tue, 17 May 2022 12:31:14 GMT
• Title: Towards the optimization of ballistics in proton therapy using genetic algorithms: implementation issues
• Authors: Fran\c{c}ois Smekens, Nicolas Freud, Bruno Sixou, Guillaume Beslon and Jean M L\'etang
• Abstract summary: We investigate a new optimization framework based on a genetic algorithm approach. The proposed optimization routine takes typically into account several thousands of spots of fixed size. The behavior of the proposed genetic algorithm is illustrated in both elementary and clinically-realistic test cases.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The dose delivered to the planning target volume by proton beams is highly conformal, sparing organs at risk and normal tissues. New treatment planning systems adapted to spot scanning techniques have been recently proposed to simultaneously optimize several fields and thus improve dose delivery. In this paper, we investigate a new optimization framework based on a genetic algorithm approach. This tool is intended to make it possible to explore new schemes of treatment delivery, possibly with future enhanced technologies. The optimization framework is designed to be versatile and to account for many degrees of freedom, without any {\it a priori} technological constraint. To test the behavior of our algorithm, we propose in this paper, as an example, to optimize beam fluences, target points and irradiation directions at the same time. The proposed optimization routine takes typically into account several thousands of spots of fixed size. The evolution is carried out by the three standard genetic operators: mutation, crossover and selection. The figure-of-merit (or fitness) is based on an objective function relative to the dose prescription to the tumor and to the limits set for organs at risk and normal tissues. Fluence optimization is carried out via a specific scheme based on a plain gradient with analytical solution. Several specific genetic algorithm issues are addressed: (i) the mutation rate is tuned to balance the search and selection forces, (ii) the initial population is selected using a bootstrap technique and (iii) to scale down the computation time, dose calculations are carried out with a fast analytical ray tracing method and are multi-threaded. In this paper implementation issues of the optimization framework are thoroughly described. The behavior of the proposed genetic algorithm is illustrated in both elementary and clinically-realistic test cases.

Related papers

• Enhancing CNN Classification with Lamarckian Memetic Algorithms and Local Search [0.0]
  We propose a novel approach integrating a two-stage training technique with population-based optimization algorithms incorporating local search capabilities. Our experiments demonstrate that the proposed method outperforms state-of-the-art gradient-based techniques.
  arXiv  Detail & Related papers  (2024-10-26T17:31:15Z)
• Globally-Optimal Greedy Experiment Selection for Active Sequential Estimation [1.1530723302736279]
  We study the problem of active sequential estimation, which involves adaptively selecting experiments for sequentially collected data. The goal is to design experiment selection rules for more accurate model estimation. We propose a class of greedy experiment selection methods and provide statistical analysis for the maximum likelihood.
  arXiv  Detail & Related papers  (2024-02-13T17:09:29Z)
• Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization [13.131971623143622]
  We discover entirely new genetic algorithms in a data-driven fashion. We parametrize selection and mutation rate adaptation as cross- and self-attention modules. The learned algorithm can be applied to previously unseen optimization problems, search dimensions & evaluation budgets.
  arXiv  Detail & Related papers  (2023-04-08T12:14:15Z)
• An Empirical Evaluation of Zeroth-Order Optimization Methods on AI-driven Molecule Optimization [78.36413169647408]
  We study the effectiveness of various ZO optimization methods for optimizing molecular objectives. We show the advantages of ZO sign-based gradient descent (ZO-signGD) We demonstrate the potential effectiveness of ZO optimization methods on widely used benchmark tasks from the Guacamol suite.
  arXiv  Detail & Related papers  (2022-10-27T01:58:10Z)
• Variable length genetic algorithm with continuous parameters optimization of beam layout in proton therapy [0.0]
  We investigate a new optimization framework based on a genetic approach. We propose to optimize simultaneously the target points and beam incidence angles in a continuous manner. No textita priori technological constraints are taken into account, textiti.e.the beam energy values, incidence directions and target points are free parameters.
  arXiv  Detail & Related papers  (2022-05-17T14:31:33Z)
• On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems [99.59934203759754]
  We introduce a class of first-order methods for smooth constrained optimization. Two distinctive features of our approach are that projections or optimizations over the entire feasible set are avoided. The resulting algorithmic procedure is simple to implement even when constraints are nonlinear.
  arXiv  Detail & Related papers  (2021-07-17T11:45:13Z)
• Unified Convergence Analysis for Adaptive Optimization with Moving Average Estimator [75.05106948314956]
  We show that an increasing large momentum parameter for the first-order moment is sufficient for adaptive scaling. We also give insights for increasing the momentum in a stagewise manner in accordance with stagewise decreasing step size.
  arXiv  Detail & Related papers  (2021-04-30T08:50:24Z)
• IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method [64.15649345392822]
  We introduce a framework for designing primal methods under the decentralized optimization setting where local functions are smooth and strongly convex. Our approach consists of approximately solving a sequence of sub-problems induced by the accelerated augmented Lagrangian method. When coupled with accelerated gradient descent, our framework yields a novel primal algorithm whose convergence rate is optimal and matched by recently derived lower bounds.
  arXiv  Detail & Related papers  (2020-06-11T18:49:06Z)
• Devolutionary genetic algorithms with application to the minimum labeling Steiner tree problem [0.0]
  This paper characterizes and discusses devolutionary genetic algorithms and evaluates their performances in solving the minimum labeling Steiner tree (MLST) problem. We define devolutionary algorithms as the process of reaching a feasible solution by devolving a population of super-optimal unfeasible solutions over time. We show how classical evolutionary concepts, such as crossing, mutation and fitness can be adapted to aim at reaching an optimal or close-to-optimal solution.
  arXiv  Detail & Related papers  (2020-04-18T13:27:28Z)
• Stochastic batch size for adaptive regularization in deep network optimization [63.68104397173262]
  We propose a first-order optimization algorithm incorporating adaptive regularization applicable to machine learning problems in deep learning framework. We empirically demonstrate the effectiveness of our algorithm using an image classification task based on conventional network models applied to commonly used benchmark datasets.
  arXiv  Detail & Related papers  (2020-04-14T07:54:53Z)
• Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization [71.03797261151605]
  Adaptivity is an important yet under-studied property in modern optimization theory. Our algorithm is proved to achieve the best-available convergence for non-PL objectives simultaneously while outperforming existing algorithms for PL objectives.
  arXiv  Detail & Related papers  (2020-02-13T05:42:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.