A Deterministic Adjoint-Based Semi-Analytical Algorithm for Fast Response Change Computations in Proton Therapy

Tiberiu, Burlacu,; Lathouwers, D.; Perkó, Zoltán

doi:10.1080/23324309.2023.2166077

Cited by 2 publications

(5 citation statements)

References 35 publications

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“…Implementation of this online re-optimization strategy requires not only fast re-optimization, high quality imaging, and appropriate contours, but also fast online QA procedures, which are currently unavailable in clinical settings. However, recent advancements in independent dose computations and log-file-based QA strategies have been made (Li et al 2013, Meier et al 2015, Meijers et al 2020, Burlacu et al 2023. Clinical application of online adaptive approaches also requires fast dose computation algorithms.…”

Section: Discussionmentioning

confidence: 99%

A fast and robust constraint-based online re-optimization approach for automated online adaptive intensity modulated proton therapy in head and neck cancer

Oud,

Breedveld,

Rojo-Santiago

et al. 2024

Phys. Med. Biol.

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Objective – In head-and-neck cancer intensity modulated proton therapy (IMPT), adaptive radiotherapy is currently restricted to offline re-planning, mitigating the effect of slow changes in patient anatomies. Daily online adaptations can potentially improve dosimetry. Here, a new, fully automated online re-optimization strategy is presented. In a retrospective study, this online re-optimization approach was compared to our trigger-based offline re-planning (offlineTB re-planning) schedule, including extensive robustness analyses.Approach – The online re-optimization method employs automated multi-criterial re-optimization, using robust optimization with 1 mm setup-robustness settings (in contrast to 3 mm for offlineTB re-planning). Hard planning constraints and spot addition are used to enforce adequate target coverage, avoid prohibitively large maximum doses and minimize organ-at-risk doses. For 67 repeat-CTs from 15 patients, fraction doses of the two strategies were compared for the CTVs and organs-at-risk. Per repeat-CT, 10.000 fractions with different setup and range robustness settings were simulated using Polynomial Chaos Expansion for fast and accurate dose calculations. Main results – For 14/67 repeat-CTs, offlineTB re-planning resulted in <50% probability of D98%≥95% of the prescribed dose (Dpres) in one or both CTVs, which never happened with online re-optimization. With offlineTB re-planning, eight repeat-CTs had zero probability of obtaining D98%≥95%Dpres for CTV7000, while the minimum probability with online re-optimization was 81%. Risks of xerostomia and dysphagia grade ≥ II were reduced by 3.5 ± 1.7 and 3.9 ± 2.8 percentage point [mean ± SD] (p<10-5 for both). In online re-optimization, adjustment of spot configuration followed by spot-intensity re-optimization took 3.4 minutes on average. Significance – The fast online re-optimization strategy always prevented substantial losses of target coverage caused by day-to-day anatomical variations, as opposed to the clinical trigger-based offline re-planning schedule. On top of this, online re-optimization could be performed with smaller setup robustness settings, contributing to improved organs-at-risk sparing.

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Section: Discussionmentioning

confidence: 99%

A fast and robust constraint-based online re-optimization approach for automated online adaptive intensity modulated proton therapy in head and neck cancer

Oud,

Breedveld,

Rojo-Santiago

et al. 2024

Phys. Med. Biol.

Self Cite

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“…This paper also documents the improvements brought to YODA, i.e. a more stable and an order more accurate numerical integration method, a better elastic scattering model for the proton beam, improved modelling in the Fermi-Eyges (FE) equation, a laterally optimized Gaussian beam splitting scheme and RT DICOM clinical treatment plan reading, compared to the original documented version (Burlacu et al 2023). The details of these changes next to the theoretical framework of YODA are given in section 2.…”

Section: A Hybrid Independent Dose Computation Approachmentioning

confidence: 93%

“…We previously presented a methodologically different approach based on a deterministic solution of the Linear Boltzmann equation (Burlacu et al 2023). This approach, which will henceforth be referred to as Yet anOther Dose Algorithm (YODA), is a hybrid numerical and analytical solution to a physics motivated approximation of the same equation that MC methods solve.…”

Section: A Hybrid Independent Dose Computation Approachmentioning

confidence: 99%

“…In contrast, the adjoint method only performs two FE and FP solutions and N HU inner products. The construction of the right hand side r † and of the adjoint operator L † is illustrated in our previous work (Burlacu et al 2023). This approach can be advantageous for the time constrained cases when the changes in the CT image HU values are small enough.…”

Section: Metric Change Computationmentioning

confidence: 99%

“…When compared to the Crank-Nicholson method in our earlier work(Burlacu et al 2023), this method increased the accuracy of the FP fluxes without degrading the speed of the algorithm.…”

mentioning

confidence: 95%

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Yet anOther Dose Algorithm (YODA) for independent computations of dose and dose changes due to anatomical changes

Burlacu,

Lathouwers,

Perkó

2024

Phys. Med. Biol.

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Objective: To assess the viability of a physics-based, deterministic and adjoint-capable algorithm for performing treatment planning system independent dose calculations and for computing dosimetric differences caused by anatomical changes.Approach: A semi-numerical approach is employed to solve two partial differential equations for the proton phase-space density which determines the deposited dose. Lateral hetereogeneities are accounted for by an optimized (Gaussian) beam splitting scheme. Adjoint theory is applied to approximate the change in the deposited dose caused by a new underlying patient anatomy.Main results: The quality of the dose engine was benchmarked through three-dimensional gamma index comparisons against Monte Carlo simulations done in TOPAS. The worst passing rate for the gamma index with (1 mm, 1 %, 10 % dose cut-off) criteria is 94.55 %. The effect of delivering treatment plans on repeat CTs was also tested. For a non-robustly optimized plan the adjoint component was accurate to 5.7 % while for a robustly optimized plan it was accurate to 4.8 %.Significance: YODA is capable of accurate dose computations in both single and multi spot irradiations when compared to TOPAS. Moreover, it is able to compute dosimetric differences due to anatomical changes with small to moderate errors thereby facilitating its use for patient-specific quality assurance in online adaptive proton therapy.

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A Deterministic Adjoint-Based Semi-Analytical Algorithm for Fast Response Change Computations in Proton Therapy

Cited by 2 publications

References 35 publications

A fast and robust constraint-based online re-optimization approach for automated online adaptive intensity modulated proton therapy in head and neck cancer

A fast and robust constraint-based online re-optimization approach for automated online adaptive intensity modulated proton therapy in head and neck cancer

Yet anOther Dose Algorithm (YODA) for independent computations of dose and dose changes due to anatomical changes

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