A treatment plan optimization method with direct minimization of number of energy jumps for proton arc therapy

Zhang, Gezhi; Long, Yong; Lin, Yuting; Chen, I‐Chun; Gao, Hao

doi:10.1088/1361-6560/acc4a7

Cited by 10 publications

(3 citation statements)

References 34 publications

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“…Additionally, with IMRT, beam angles optimized via BAO (Shen et al 2023) could be performed to further improve Photon or the photon component of Hybrid. The replacement of IMPT by proton ARC (Zhang et al 2022(Zhang et al , 2023a) could potentially further improve Proton or the proton component of Hybrid.…”

Section: Discussionmentioning

confidence: 99%

Biological optimization for hybrid proton-photon radiotherapy

Li,

Lin,

et al. 2024

Phys. Med. Biol.

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Objective:Hybrid proton-photon radiotherapy (RT) is a cancer treatment option to broaden access to proton RT. Additionally, with a refined treatment planning method, hybrid RT has the potential to offer superior plan quality compared to proton-only or photon-only RT, particularly in terms of target coverage and sparing organs-at-risk (OAR), when considering robustness to setup and range uncertainties. However, there is a concern regarding the underestimation of the biological effect of protons on OAR, especially those in close proximity to targets. This study seeks to develop a hybrid treatment planning method with biological dose optimization, suitable for clinical implementation on existing proton and photon machines, with each photon or proton treatment fraction delivering a uniform target dose.Approach:The proposed hybrid biological dose optimization method optimizes proton and photon plan variables, along with the number of fractions, minimizing biological dose to OAR and surrounding normal tissues. Hybrid plans are designed to be deliverable separately and robustly on existing proton and photon machines, with enforced uniform target dose constraints for proton and photon fraction doses. Probabilistic formulation is utilized for robust optimization of setup and range uncertainties for protons and photons. The nonconvex optimization problem, arising from minimum monitor unit (MMU) constraint and dose-volume histogram (DVH) constraints, is solved using an iterative convex relaxation method.Main results:Hybrid planning with biological dose optimization effectively eliminated hot spots of biological dose, particularly in normal tissues surrounding the target, outperforming proton-only planning. It also provided superior overall plan quality and OAR sparing compared to proton-only or photon-only planning strategies.Significance:This study presents a novel hybrid biological treatment planning method capable of generating plans with minimized biological hot spots, superior plan quality to proton-only or photon-only plans, and clinical deliverability on existing proton and photon machines, separately and robustly.

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Section: Discussionmentioning

confidence: 99%

Biological optimization for hybrid proton-photon radiotherapy

Li,

Lin,

et al. 2024

Phys. Med. Biol.

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“…50 On the other hand, LET optimization should be considered to lower OAR biological dose. 51 Moreover, the emerging proton arc delivery 52 and the joint use of protons and photons 53 may further improve physical and biological dose distribution and/or delivery robustness for LATTICE. More advanced proton LATTICE studies will be carried out in the future works.…”

Section: Discussionmentioning

confidence: 99%

Lattice position optimization for LATTICE therapy

et al. 2023

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BackgroundLATTICE radiation therapy delivers 3D heterogenous dose of high peak‐to‐valley dose ratio (PVDR) to the tumor target, with peak dose at lattice vertices inside the target and valley dose for the rest of the target. Although the lattice vertex positions can impact PVDR inside the target and sparing of organs‐at‐risk (OAR), they are fixed as constants and not optimized during treatment planning in current clinical practice.PurposeThis work proposes a new LATTICE plan optimization method that can optimize lattice vertex positions during LATTICE treatment planning, which is the first lattice position optimization study to the best of our knowledge.MethodsThe new LATTICE treatment planning method optimizes lattice vertex positions as well as other plan variables (e.g., photon fluences or proton spot weights), with optimization objectives for target PVDR and OAR sparing. To satisfy mathematical differentiability, the lattice vertices are approximated in sigmoid functions. For geometric feasibility, proper geometry constraints are enforced onto lattice vertex positions. The lattice position optimization problem is solved by iterative convex relaxation (ICR) method and alternating direction method of multipliers (ADMM), and lattice vertex positions and photon/proton plan variables are jointly updated via the Quasi‐Newton method.ResultsBoth photon and proton LATTICE RT were considered, and the optimal lattice vertex positions in terms of plan objectives were found by solving all possible combinations on given discrete positions via exhaustive searching based on standard IMRT/IMPT, which served as the ground truth for validating the new LATTICE method. The results show that the new method indeed provided the optimal lattice vertex positions with the smallest optimization objective, the largest target PVDR, and the best OAR sparing.ConclusionsA new LATTICE treatment planning method is proposed and validated that can optimize lattice vertex positions as well as other photon or proton plan variables for improving target PVDR and OAR sparing.

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“…MatRad 17 was used to generate dose influence matrices D with 5 mm spot lateral spacing on 3 mm 3 dose grid. The planning details of IMPT, ARC, and FLASH can be found in 22,24,36,48,32 respectively. All plans were normalized to have D95 = 100% to CTV.…”

Section: Methodsmentioning

confidence: 99%

An orthogonal matching pursuit optimization method for solving minimum‐monitor‐unit problems: Applications to proton IMPT, ARC and FLASH

et al. 2023

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BackgroundThe intensities (i.e., number of protons in monitor unit [MU]) of deliverable proton spots need to be either zero or meet a minimum‐MU (MMU) threshold, which is a nonconvex problem. Since the dose rate is proportionally associated with the MMU threshold, higher‐dose‐rate proton radiation therapy (RT) (e.g., efficient intensity modulated proton therapy (IMPT) and ARC proton therapy, and high‐dose‐rate‐induced FLASH effect needs to solve the MMU problem with larger MMU threshold, which however makes the nonconvex problem more difficult to solve.PurposeThis work will develop a more effective optimization method based on orthogonal matching pursuit (OMP) for solving the MMU problem with large MMU thresholds, compared to state‐of‐the‐art methods, such as alternating direction method of multipliers (ADMM), proximal gradient descent method (PGD), or stochastic coordinate descent method (SCD).MethodsThe new method consists of two essential components. First, the iterative convex relaxation (ICR) method is used to determine the active sets for dose‐volume planning constraints and decouple the MMU constraint from the rest. Second, a modified OMP optimization algorithm is used to handle the MMU constraint: the non‐zero spots are greedily selected via OMP to form the solution set to be optimized, and then a convex constrained subproblem is formed and can be conveniently solved to optimize the spot weights restricted to this solution set via OMP. During this iterative process, the new non‐zero spots localized via OMP will be adaptively added to or removed from the optimization objective.ResultsThe new method via OMP is validated in comparison with ADMM, PGD and SCD for high‐dose‐rate IMPT, ARC, and FLASH problems of large MMU thresholds, and the results suggest that OMP substantially improved the plan quality from PGD, ADMM and SCD in terms of both target dose conformality (e.g., quantified by max target dose and conformity index) and normal tissue sparing (e.g., mean and max dose). For example, in the brain case, the max target dose for IMPT/ARC/FLASH was 368.0%/358.3%/283.4% respectively for PGD, 154.4%/179.8%/150.0% for ADMM, 134.5%/130.4%/123.0% for SCD, while it was <120% in all scenarios for OMP; compared to PGD/ADMM/SCD, OMP improved the conformity index from 0.42/0.52/0.33 to 0.65 for IMPT and 0.46/0.60/0.61 to 0.83 for ARC.ConclusionsA new OMP‐based optimization algorithm is developed to solve the MMU problems with large MMU thresholds, and validated using examples of IMPT, ARC, and FLASH with substantially improved plan quality from ADMM, PGD, and SCD.

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A treatment plan optimization method with direct minimization of number of energy jumps for proton arc therapy

Cited by 10 publications

References 34 publications

Biological optimization for hybrid proton-photon radiotherapy

Biological optimization for hybrid proton-photon radiotherapy

Lattice position optimization for LATTICE therapy

An orthogonal matching pursuit optimization method for solving minimum‐monitor‐unit problems: Applications to proton IMPT, ARC and FLASH

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