Robust optimization for intensity‐modulated proton therapy with soft spot sensitivity regularization

Lyu

et al. 2020

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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 ...

Section: Discussionmentioning

confidence: 99%

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

Lyu

et al. 2020

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“…The combined effects of spreading out distal edges and the random uncertainties of individual beams may be patient specific and need to be carefully assessed in future work. In addition, we have proposed an integrated robust BOO framework for IMPT, 15 which generates robust beam orientation and robust fluence map efficiently in a single step. Sensitivity regularization and heterogeneity weighting were added to the group sparsity‐based BOO framework to encourage robustness.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to the dose conformity, the plan robustness can improve as well. We showed that beams from different angles could be compensatory toward range and positioning uncertainties 13,15 . On the other hand, using a larger number of beams in IMPT can be impractical.…”

Section: Introductionmentioning

confidence: 89%

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Fraction‐variant beam orientation optimization for intensity‐modulated proton therapy

O’Connor

et al. 2020

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Purpose: To achieve a superior balance between dosimetry and the delivery efficiency of intensitymodulated proton therapy (IMPT) using as few beams as possible in a single fraction, we optimally vary beams in different fractions. Methods: In the optimization, 400~800 feasible noncoplanar beams were included in the candidate pool. For each beam, the doses of all scanning spots covering the target volume and a margin were calculated. The fraction-variant beam orientation optimization (FVBOO) problem was formulated to include three terms: two quadratic dose fidelity terms to penalize the deviation of planning target volume fractional dose and organs at risk (OAR) cumulative doses from prescription, respectively; an L2,1/2-norm group sparsity term to control the number of active beams per fraction to between 1 and 4. The Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) was applied to solve this problem. FVBOO was tested on a patient with base-of-skull (BOS) tumor of 5 fractions (5f) and 30 fractions (30f) with an average number of active beams per fraction varying between 4 and 1. In addition, one bilateral head-and-neck (H&N) patient, and one esophageal cancer (ESG) patient of 30f were tested with about three active beams per fraction. The results were compared with IMPT plans that use fixed beams in each fraction. The fixed beams were selected using the group sparsity term with a fraction-invariant BOO (FIBOO) constraint. Results: Varying beams were chosen in either the 5f or 30f FVBOO plans. While similar number of beams per fraction was selected as the FIBOO plan, the FVBOO plans were able to spare the OARs better, with an average reduction of [Dmean, Dmax] from the FIBOO plans by [0.85, 2.08] Relative Biological Effective Gy (GyRBE) in the 5f plan and [1.87, 4.06] GyRBE in the 30f plans. While reducing the number of beams per fraction in the BOS patient, a three-beam/fraction 5f FVBOO plan performs comparably as the four-beam FIBOO plan and a two-beam/fraction 30f FVBOO plan still provides superior dosimetry. Conclusion: Fraction-variant beam orientation optimization allows the utilization of a larger beam solution space for superior dose distribution in IMPT while maintaining a practical number of beams in each fraction.

“…The plan robustness is better maintained at the cost of substantially increased computational cost . To avoid the additional burden of calculating the worst cases and provide the robustness consideration as a soft constraint, we recently modeled the scanning spot sensitivity concerning range and positioning uncertainties as a regularization term in the optimization . We showed improved dosimetry, robustness to larger range uncertainties, and an order of magnitude faster optimization time than the worst case approach.…”

Section: Introductionmentioning

confidence: 99%

Robust beam orientation optimization for intensity‐modulated proton therapy

Neph

et al. 2019

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Purpose Dose conformality and robustness are equally important in intensity modulated proton therapy (IMPT). Despite the obvious implication of beam orientation on both dosimetry and robustness, an automated, robust beam orientation optimization algorithm has not been incorporated due to the problem complexity and paramount computational challenge. In this study, we developed a novel IMPT framework that integrates robust beam orientation optimization (BOO) and robust fluence map optimization (FMO) in a unified framework. Methods The unified framework is formulated to include a dose fidelity term, a heterogeneity‐weighted group sparsity term, and a sensitivity regularization term. The L2, 1/2‐norm group sparsity is used to reduce the number of active beams from the initial 1162 evenly distributed noncoplanar candidate beams, to between two and four. A heterogeneity index, which evaluates the lateral tissue heterogeneity of a beam, is used to weigh the group sparsity term. With this index, beams more resilient to setup uncertainties are encouraged. There is a symbiotic relationship between the heterogeneity index and the sensitivity regularization; the integrated optimization framework further improves beam robustness against both range and setup uncertainties. This Sensitivity regularization and Heterogeneity weighting based BOO and FMO framework (SHBOO‐FMO) was tested on two skull‐base tumor (SBT) patients and two bilateral head‐and‐neck (H&N) patients. The conventional CTV‐based optimized plans (Conv) with SHBOO‐FMO beams (SHBOO‐Conv) and manual beams (MAN‐Conv) were compared to investigate the beam robustness of the proposed method. The dosimetry and robustness of SHBOO‐FMO plan were compared against the manual beam plan with CTV‐based voxel‐wise worst‐case scenario approach (MAN‐WC). Results With SHBOO‐FMO method, the beams with superior range robustness over manual beams were selected while the setup robustness was maintained or improved. On average, the lowest [D95%, V95%, V100%] of CTV were increased from [93.85%, 91.06%, 70.64%] in MAN‐Conv plans, to [98.62%, 98.61%, 96.17%] in SHBOO‐Conv plans with range uncertainties. With setup uncertainties, the average lowest [D98%, D95%, V95%, V100%] of CTV were increased from [92.06%, 94.83%, 94.31%, 78.93%] in MAN‐Conv plans, to [93.54%, 96.61%, 97.01%, 91.98%] in SHBOO‐Conv plans. Compared with the MAN‐WC plans, the final SHBOO‐FMO plans achieved comparable plan robustness and better OAR sparing, with an average reduction of [Dmean, Dmax] of [6.31, 6.55] GyRBE for the SBT cases and [1.89, 5.08] GyRBE for the H&N cases from the MAN‐WC plans. Conclusion We developed a novel method to integrate robust BOO and robust FMO into IMPT optimization for a unified solution of both BOO and FMO, generating plans with superior dosimetry and good robustness.