PO-0916 Energy layer switching sequence optimization algorithm for an efficiency proton arc therapy delivery

Ding, X. H.; Li, X.; Liu, G.; Stevens, C.A.; Yan, Danying; Kabolizadeh, Peyman

doi:10.1016/s0167-8140(19)31336-2

Cited by 2 publications

(5 citation statements)

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“…To match the two plans, in SPArc optimization, we pushed the number of layers per beam to be 1 for as many beams as possible, leaving only a few beams to have two energy layers. Similarly, we created SPArc plans with 21 with ELO-SPAT. The SPArc method with or without ES is denoted as SPArc-ES or SPArc-noES, and the ELO-SPAT method with or without ES is denoted as ELO-ES or ELO-noES All plans were normalized to deliver prescription dose to 95% of volume.…”

Section: D Evaluationmentioning

confidence: 99%

“…By this method, 1–3 energy layers remain active at each control point, ensuring acceptable delivery time. Later this algorithm was updated to optimize the energy delivery sequence from high to low instead of arbitrary switching to further shorten the delivery time 21 . Retrospective studies show the potential of SPArc plans that improve plan dosimetry compared with intensity‐modulated proton therapy (IMPT) to lung cancer, 19,22 prostate cancer, 23 and whole‐brain radiotherapy 24 .…”

Section: Introductionmentioning

confidence: 99%

“…Later this algorithm was updated to optimize the energy delivery sequence from high to low instead of arbitrary switching to further shorten the delivery time. 21 Retrospective studies show the potential of SPArc plans that improve plan dosimetry compared with intensity-modulated proton therapy (IMPT) to lung cancer, 19,22 prostate cancer, 23 and whole-brain radiotherapy. 24 Recently, the first prototype of SPArc delivery was performed on a clinical IBA Proteus One proton machine, with a Proton Dynamic Arc Delivery (PDAD) module.…”

Section: Introductionmentioning

confidence: 99%

“…25 However, in the current SPArc algorithm, the energy layer selection and optimization are greedy and heuristic. Due to the separate sequencing and plan optimization steps, 21 the optimality of the plan delivery efficiency and the dosimetric quality cannot be promised. The alternating back-and-forth operation between the fluence map optimization and energy layer processing required for final plan creation is computationally inefficient.…”

Section: Introductionmentioning

confidence: 99%

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A novel energy layer optimization framework for spot‐scanning proton arc therapy

Ruan

Lyu

et al. 2020

Medical Physics

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Purpose: Spot-scanning proton arc therapy (SPAT) is an emerging modality to improve plan conformality and delivery efficiency. A greedy and heuristic method is proposed in the existing SPAT algorithm to select energy layers and sequence energy switching with gantry rotation, which does not promise optimality in either dosimetry or efficiency. We aim to develop a method to solve the energy layer switching and dosimetry optimization problems in an integrated framework for SPAT. Methods: In an integrated approach, energy layer optimization for spot-scanning proton arc therapy (ELO-SPAT) is formulated with a dose fidelity term, a group sparsity regularization, a log barrier regularization, and an energy sequencing (ES) penalty. The combination of L2,1/2-norm group sparsity regularization and log barrier function allows one energy layer being selected per control point. The ES regularization term sorts the delivery sequence from high energy to low energy to reduce the total energy layer switching time (ELST) and subsequently the total delivery time. Within the ES penalty, the gradient of layer weights between adjacent beams is first calculated along beam direction and then along energy direction. The gradients indicate energy switch patterns between two adjacent beams. The time-wise costly energy switch-up is more heavily penalized in the ES term. This ELO-SPAT method was tested on one frontal base-of-skull (BOS) patient, one chordoma (CHDM) patient with a simultaneous integrated boost, one bilateral head-and-neck (H&N) patient, and one lung (LNG) patient. We compared ELO-SPAT with intensity-modulated proton therapy (IMPT) using discrete beams and SPArc by Ding et al. For the two arc algorithms, both the plans with and without energy sequencing were created and compared. Results: Energy layer optimization for spot-scanning proton arc therapy reduced the runtime of optimization by 84% on average compared with the greedy SPArc method. In both the ELO-SPAT plans with and without ES, one energy layer per control point was selected. Without ES regularization, the energy sequence was arbitrary, with around 40-60 switch-up for the tested cases. After adding ES regularization, the number of energy switch-up was reduced to less than 20. Compared with the energy sequenced SPArc plans, the ELO-SPAT plans with ES led to 24% less total ELST for synchrotron plans and 14% less for cyclotron plans. Both the ELO-SPAT and SPArc plans achieved better sparing compared with the IMPT plans for most Organs-at-risks (OARs), with or without ES. Without ES, the ELO-SPAT plans achieved further improvement of the OARs compared with the SPArc plans, with an averaged reduction of OAR [Dmean, Dmax] by [1.57, 3.34] GyRBE. Adding the ES regularization degraded the plan quality, but the ELO-SPAT plans still had comparable or slightly better sparing than the SPArc plans with ES, with an averaged reduction of OAR [Dmean, Dmax] by [1.42, 2.34] GyRBE. Conclusion: We developed a computationally efficient spot-scanning proton arc optimization method, which ...

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Section: D Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel energy layer optimization framework for spot‐scanning proton arc therapy

Ruan

Lyu

et al. 2020

Medical Physics

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“…The flexibility of PBS has lent itself well to algorithmic approaches to deliver a treatment using rotational arcs. Delivery-efficient PBS proton arc therapy algorithms have adapted control-point resampling, energy layer redistribution, and filtering to distribute a subset of energies among discretized arc segments using gradient descent methods 3 , 4 . The results from the proposed delivery technique show promising results, up to an 8% reduction in the integral dose and reduced skin dose by as much as 60% in comparison to two-field IMPT.…”

Section: Introductionmentioning

confidence: 99%

A novel optimization algorithm for enabling dynamically collimated proton arc therapy

Smith

Flynn

Hyer

2022

Sci Rep

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The advent of energy-specific collimation in pencil beam scanning (PBS) proton therapy has led to an improved lateral dose conformity for a variety of treatment sites, resulting in better healthy tissue sparing. Arc PBS delivery has also been proposed to enhance high-dose conformity about the intended target, reduce skin toxicity, and improve plan robustness. The goal of this work was to determine if the combination of proton arc and energy-specific collimation can generate better dose distributions as a logical next step to maximize the dosimetric advantages of proton therapy. Plans were optimized using a novel DyNamically collimated proton Arc (DNA) genetic optimization algorithm that was designed specifically for the application of proton arc therapy. A treatment planning comparison study was performed by generating an uncollimated two-field intensity modulated proton therapy and partial arc treatments and then replanning these treatments using energy-specific collimation as delivered by a dynamic collimation system, which is a novel collimation technology for PBS. As such, we refer to this novel treatment paradigm as Dynamically Collimated Proton Arc Therapy (DC-PAT). Arc deliveries achieved a superior target conformity and improved organ at risk (OAR) sparing relative to their two-field counterparts at the cost of an increase to the low-dose, high-volume region of the healthy brain. The incorporation of DC-PAT using the DNA optimizer was shown to further improve the tumor dose conformity. When compared to the uncollimated proton arc treatments, the mean dose to the 10mm of surrounding healthy tissue was reduced by 11.4% with the addition of collimation without meaningfully affecting the maximum skin dose (less than 1% change) relative to a multi-field treatment. In this case study, DC-PAT could better spare specific OARs while maintaining better target coverage compared to uncollimated proton arc treatments. While this work presents a proof-of-concept integration of two emerging technologies, the results are promising and suggest that the addition of these two techniques can lead to superior treatment plans warranting further development.

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PO-0916 Energy layer switching sequence optimization algorithm for an efficiency proton arc therapy delivery

Cited by 2 publications

References 0 publications

A novel energy layer optimization framework for spot‐scanning proton arc therapy

A novel energy layer optimization framework for spot‐scanning proton arc therapy

A novel optimization algorithm for enabling dynamically collimated proton arc therapy

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