arXiv subjects

K. Souris

Publications and source records attributed to K. Souris.

4 recordsLinked to original sources

arXiv2024

Proton arc therapy plan optimization with energy layer pre-selection driven by organ at risk sparing and delivery time

Objective. As proton arc therapy (PAT) approaches clinical implementation, optimizing treatment plans for this innovative delivery modality remains challenging, especially in addressing arc delivery time. Existing algorithms for minimizing delivery time are either optimal but computationally demanding or fast but at the expense of sacrificing many degrees of freedom. In this study, we introduce a flexible method for pre-selecting energy layers (EL) in PAT treatment planning before the actual robust spot weight optimization. Our EL pre-selection method employs metaheuristics to minimize a bi-objective function, considering a dynamic delivery time proxy and tumor geometrical coverage penalized as a function of selected organs-at-risk crossing. It is capable of parallelizing multiple instances of the problem. We evaluate the method using three different treatment sites, providing a comprehensive dosimetric analysis benchmarked against dynamic proton arc plans generated with early energy layer selection and spot assignment (ELSA) and IMPT plans in RayStation TPS. The algorithm efficiently generates Pareto-optimal EL pre-selections in approximately 5 minutes. Subsequent PAT treatment plans derived from these selections and optimized within the TPS, demonstrate high-quality target coverage, achieving a high conformity index, and effective sparing of organs at risk. These plans meet clinical goals while achieving a 20 to 40% reduction in delivery time compared to ELSA plans. The proposed algorithm offers speed and efficiency, producing high-quality PAT plans by placing proton arc sectors to efficiently reduce delivery time while maintaining good target coverage and healthy tissues sparing.

physics.med-ph↗

arXiv2023

Efficient proton arc optimization and delivery through energy layer pre-selection and post-filtering

Proton arc therapy (PAT) has emerged as a promising approach for improving dose distribution, but also enabling simpler and faster treatment delivery in comparison to conventional proton treatments. However, the delivery speed achievable in proton arc relies on dedicated algorithms, which currently do not generate plans with a clear speed-up and sometimes even result in increased delivery time. This study aims to address the challenge of minimizing delivery time through a hybrid method combining a fast geometry-based energy layer (EL) pre-selection with a dose-based EL filtering. Three methods of EL filtering were developed; The unrestricted method filters the lowest weighted EL while the SU gap filtering removes the EL around a new SU to minimize the gantry rotation braking. The SU filtering removes the lowest weighted group of EL that includes a SU. These filters were combined with the RayStation dynamic proton arc optimization framework (ELSA). Four bilateral oropharyngeal and four lung cancer patients' data were used for evaluation. Objective function values, target coverage robustness, organ-at-risk doses and NTCP evaluations, as well as comparisons to IMPT plans, were used to assess plan quality. The SU gap filtering algorithm performed best in five out of the eight cases, maintaining plan quality within tolerance while reducing beam delivery time, in particular for the oropharyngeal cohort. It achieved up to approximately 22% and 15% reduction in delivery time for oropharyngeal and lung treatment sites, respectively. The unrestricted filtering algorithm followed closely. In contrast, the SU filtering showed limited improvement, suppressing one or two SU without substantial delivery time shortening. Robust target coverage was kept within 1% of variation compared to the PAT baseline plan while organs-at-risk doses slightly decreased or kept about the same for all patients.

physics.med-ph↗

arXiv2023

Beamlet-free optimization for Monte Carlo based treatment planning in proton therapy

Background: Dose calculation and optimization algorithms in proton therapy treatment planning often have high computational requirements regarding time and memory. This can hinder the implementation of efficient workflows in clinics and prevent the use of new, elaborate treatment techniques aiming to improve clinical outcomes like robust optimization, arc and adaptive proton therapy. Purpose: A new method, namely, the beamlet-free algorithm, is presented to address the aforementioned issue by combining Monte Carlo dose calculation and optimization into a single algorithm and omitting the calculation of the time-consuming and costly dose influence matrix. Methods: The beamlet-free algorithm simulates the dose in proton batches of randomly chosen spots and evaluates their relative impact on the objective function at each iteration. Based on the approximated gradient, the spot weights are then updated and used to generate a new spot probability distribution. The beamlet-free method is compared against a conventional, beamlet-based treatment planning algorithm on a brain case. Results: The beamlet-free algorithm maintained a comparable plan quality while reducing the computation time by 70% and the peak memory usage by 95%. Conclusion: The implementation of a beamlet-free treatment planning algorithm for proton therapy is feasible and capable of achieving a considerable reduction of time and memory requirements.

physics.med-ph↗

arXiv2023

OpenTPS -- Open-source treatment planning system for research in proton therapy

Introduction. Treatment planning systems (TPS) are an essential component for simulating and optimizing a radiation therapy treatment before administering it to the patient. It ensures that the tumor is well covered and the dose to the healthy tissues is minimized. However, the TPS provided by commercial companies often come with a large panel of tools, each implemented in the form of a black-box making it difficult for researchers to use them for implementing and testing new ideas. To address this issue, we have developed an open-source TPS. Approach. We have developed an open-source software platform, OpenTPS (opentps.org), to generate treatment plans for external beam radiation therapy, and in particular for proton therapy. It is designed to be a flexible and user-friendly platform (coded with the freely usable Python language) that can be used by medical physicists, radiation oncologists, and other members of the radiation therapy community to create customized treatment plans for educational and research purposes. Result. OpenTPS includes a range of tools and features that can be used to analyze patient anatomy, simulate the delivery of the radiation beam, and optimize the treatment plan to achieve the desired dose distribution. It can be used to create treatment plans for a variety of cancer types and was designed to be extended to other treatment modalities. Significance. A new open-source treatment planning system has been built for research in proton therapy. Its flexibility allows an easy integration of new techniques and customization of treatment plans. It is freely available for use and is regularly updated and supported by a community of users and developers who contribute to the ongoing development and improvement of the software.

physics.med-ph↗