The first investigation of spot-scanning proton arc (SPArc) delivery time and accuracy with different delivery tolerance window settings

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Objective: Proton therapy reduces the integral dose to the patient compared to conventional photon treatments. However, in vivo proton range uncertainties remain a considerable hurdle. Range uncertainty reduction benefits depend on clinical practices. During intensity-modulated proton therapy (IMPT), the target is irradiated from only a few directions, but proton arc therapy (PAT), for which the target is irradiated from dozens of angles, may see clinical implementation by the time considerable range uncertainty reductions are achieved. It is therefore crucial to determine the impact of PAT on range uncertainty reduction benefits.Approach: For twenty head-and-neck cancer patients, four different treatment plans were created: an IMPT and a PAT treatment plan assuming current clinical range uncertainties of 3.5% (IMPT3.5% and PAT3.5%), and an IMPT and a PAT treatment plan assuming that range uncertainties can be reduced to 1% (IMPT1% and PAT1%). Plans were evaluated with respect to target coverage and organ-at-risk doses as well as normal tissue complication probabilities (NTCPs) for parotid glands (endpoint: parotid gland flow < 25%) and larynx (endpoint: larynx edema). Main results: Implementation of PAT (IMPT3.5%-PAT3.5%) reduced mean NTCPs in the nominal and worst-case scenario by 3.2 percentage points (pp) and 4.2 pp, respectively. Reducing range uncertainties from 3.5% to 1% during use of IMPT (IMPT3.5%-IMPT1%) reduced evaluated NTCPs by 0.9 pp and 2.0 pp. Benefits of range uncertainty reductions subsequently to PAT implementation (PAT3.5%- PAT1%) were 0.2 pp and 1.0 pp, with considerably higher benefits in bilateral compared to unilateral cases.Significance: The mean clinical benefit of implementing PAT was more than twice as high as the benefit of a 3.5% to 1% range uncertainty reduction. Range uncertainty reductions are expected to remain beneficial even after PAT implementation, especially in cases with target positions allowing for full leveraging of the higher number of gantry angles during PAT.

Section: Relative Impact Of Proton Arc Therapymentioning

confidence: 99%

Impact of and interplay between proton arc therapy and range uncertainties in proton therapy for head-and-neck cancer

Tattenberg,

Liu,

Mulhem

et al. 2024

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“…The idea is that this window should be small enough that it does not matter at which exact angle a spot is delivered, as long as it is within the specified window. The dose perturbation should be small enough to be negligible (Liu et al 2023). See figure 1 for an illustration of this concept.…”

Section: Proton Arc Preliminariesmentioning

confidence: 99%

Optimizing the traversal time for gantry trajectories for proton arc therapy treatment plans

Wase,

Marthin,

Fredriksson

et al. 2024

Abstract. Background. Proton arc therapy is an emerging radiation therapy technique where either the gantry or the patient continuously rotates during the irradiation treatment. One of the perceived advantages of proton arc therapy is the reduced treatment time, but it is still unclear exactly how long these treatment times will be, given that no machine capable of its delivery is available on the market at the time of writing. Objective. We introduce the algorithm Arc Trajectory Optimization Method (ATOM), which aims to determine an efficient velocity profile for the gantry for rapid delivery of a given proton arc treatment plan. This algorithm could be used to minimize the delivery time of a proton arc plan without changing the plan or updating the machine. Approach. ATOM computes the trajectory with the shortest delivery time while ensuring there is enough time to deliver all spots in each energy layer and switch energy between layers. The feasibility of the dynamic gantry movement was assured by enforcing maximum and minimum limits for velocity, acceleration, and jerk. This was achieved by discretizing the gantry velocity and combining the A* algorithm with the open-source motion generation library Ruckig. The algorithm was tested on a synthetic data set as well as a liver case, a prostate case and a head and neck case. Main Results. Arc trajectories for plans with 360 energy layers were calculated in under a second using 256 discrete velocities. The delivery time of the liver case, the prostate case and the head and neck case were 284, 288 and 309 s respectively, for 180 energy layers. Significance. ATOM is an open-source C++ library with a Python interface that rapidly generates velocity profiles, making it a highly efficient tool for determining proton arc delivery times, which could be integrated into the treatment planning process.

A novel planning framework for efficient spot-scanning proton arc therapy via particle swarm optimization (SPArc-_{particle swarm})

Qian,

Fan,

Dao

et al. 2023

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Objective. Delivery efficiency is the bottleneck of spot-scanning proton arc therapy (SPArc) because of the numerous energy layers (ELs) ascending switches. This study aims to develop a new algorithm to mitigate the need for EL ascending via water equivalent thickness (WET) sector selection followed by particle swarm optimization (SPArc-particle swarm). Approach. SPArc-particle swarm divided the full arc trajectory into the optimal sectors based on K-means clustering analysis of the relative mean WET. Within the sector, particle swarm optimization was used to minimize the total energy switch time, optimizing the energy selection integrated with the EL delivery sequence and relationship. This novel planning framework was implemented on the open-source platform matRad (Department of Medical Physics in Radiation Oncology, German Cancer Research Center-DKFZ). Three representative cases (brain, liver, and prostate cancer) were selected for testing purposes. Two kinds of plans were generated: SPArc_seq and SPArc-particle swarm. The plan quality and delivery efficiency were evaluated. Main results. With a similar plan quality, the delivery efficiency was significantly improved using SPArc-particle swarm compared to SPArc_seq. More specifically, it reduces the number of ELs ascending switching compared to the SPArc_seq (from 21 to 7 in the brain, from 21 to 5 in the prostate, from 21 to 6 in the liver), leading to a 16%–26% reduction of the beam delivery time (BDT) in the SPArc treatment. Significance. A novel planning framework, SPArc-particle swarm, could significantly improve the delivery efficiency, which paves the roadmap towards routine clinical implementation.