A fast beam orientation optimization method that enforces geometric constraints in IMRT for total marrow irradiation

Lee, Chieh-Hsiu Jason; Aleman, Dionne M.; Sharpe, Michael B.

doi:10.1111/itor.12093

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…) and i, j ä δ Θ . This ensures a maximum separation between selected nodes to produce a plan with higher quality (Yuan et al 2018, Lee et al 2015. The importance of each part of the reward can be determined by weighting them; however, in our experiments, we set an equal weight on all terms except for the beam separation which is set by the choice of the parameter K.…”

Section: Reinforcement Learning Environment For Boomentioning

confidence: 99%

Graph neural networks and deep reinforcement learning for simultaneous beam orientation and trajectory optimization of Cyberknife

et al. 2021

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Objective. Despite the high-quality treatment, the long treatment time of the Cyberknife system is believed to be a drawback. The high flexibility of its robotic arm requires meticulous path-finding algorithms to deliver the prescribed dose in the shortest time. Approach. We proposed a Deep Q-learning based on Graph Neural Networks to find the subset of the beams and the order to traverse them. A complex reward function is defined to minimize the distance covered by the robotic arm while avoiding the selection of close beams. Individual beam scores are also generated based on their effect on the beam intensity and are incorporated in the reward function. Main results. The performance of the presented method is evaluated on three clinical cases suffering from lung cancer. Applying this approach leads to an average of 35% reduction in the treatment time while delivering the prescribed dose provided by the physicians. Significance. Shorter treatment times result in a better treatment experience for individual patients, reduces discomfort and the sides effects of inadvertent movements for them. Additionally, it creates the opportunity to treat a higher number of patients in a given time period at the radiation therapy centers.

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Section: Reinforcement Learning Environment For Boomentioning

confidence: 99%

Graph neural networks and deep reinforcement learning for simultaneous beam orientation and trajectory optimization of Cyberknife

et al. 2021

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Deep reinforcement learning in radiation therapy planning optimization: A comprehensive review

Li,

Guo,

Lin

et al. 2024

Physica Medica

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Automated planning of curved needle channels in 3D printed patient-tailored applicators for cervical cancer brachytherapy

Straathof,

van Vliet-Pérez,

Kolkman-Deurloo

et al. 2024

Phys. Med. Biol.

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PurposePatient-tailored intracavitary/interstitial (IC/IS) brachytherapy (BT) applicators may increase dose conformity in cervical cancer patients. Current configuration planning methods in these custom applicators rely on manual specification or a small set of (straight) needles. This work introduces and validates a two-stage approach for establishing channel configurations in the 3D printed patient-tailored ARCHITECT applicator. MethodsFor each patient, the patient-tailored applicator shape was based on the first BT application with a commercial applicator and integrated connectors to a commercial (Geneva) intrauterine tube and two lunar ring channels. First, a large candidate set was generated of channels that steer the needle to desired poses in the target region and are contained in the applicator. The channels’ centrelines were represented by Bézier curves. Channels running between straight target segments and entry points were optimised and refined to ensure (dynamic) feasibility. Second, channel configurations were selected using geometric coverage optimisation. This workflow was applied to establish patient-tailored geometries for twenty-two patients previously treated using the Venezia applicator. Treatment plans were automatically generated using the in-house developed algorithm BiCycle. Plans for the clinically used configuration, TPclin, and patient-tailored configuration, TParch, were compared.ResultsChannel configurations could be generated in clinically feasible time (median: 2651s, range 1826-3812s). All TParch and TPclin plans were acceptable, but planning aims were more frequently attained with patient-tailored configurations (115/132 versus 100/132 instances). Median CTVIR D 98 and bladder D 2cm3 doses significantly improved (p < 0.001 and p < 0.01 respectively) in TParch plans in comparison with TPclin plans, and in approximately half of the patients dosimetric indices improved. ConclusionAutomated patient-tailored BT channel configuration planning for 3D printed applicators is clinically feasible. A treatment planning study showed that all plans met planning limits for the patient-tailored configurations, and in selected cases improved the plan quality in comparison with commercial applicator configurations.

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A fast beam orientation optimization method that enforces geometric constraints in IMRT for total marrow irradiation

Cited by 3 publications

References 24 publications

Graph neural networks and deep reinforcement learning for simultaneous beam orientation and trajectory optimization of Cyberknife

Graph neural networks and deep reinforcement learning for simultaneous beam orientation and trajectory optimization of Cyberknife

Deep reinforcement learning in radiation therapy planning optimization: A comprehensive review

Automated planning of curved needle channels in 3D printed patient-tailored applicators for cervical cancer brachytherapy

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