A conceptual study on real-time adaptive radiation therapy optimization through ultra-fast beamlet control

Wiersma, R; Liu, Xinmin

doi:10.1088/2057-1976/ab3ba9

Cited by 2 publications

(2 citation statements)

References 45 publications

(55 reference statements)

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“…Still, the potential benefits have been acknowledged. 17 One approach for intrafractional replanning by Antico et al is based on a library of simulated target motions to decide for the beam which promises the closest dose delivery compared to the prescribed dose distribution. 18 However, the treatment can be suboptimal due to deviations between the corresponding scenario in the library and the actual target motion as has been shown for inter-fractional replanning.…”

Section: Introductionmentioning

confidence: 99%

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Towards fast adaptive replanning by constrained reoptimization for intra‐fractional non‐periodic motion during robotic SBRT

Gerlach

Hofmann²,

Fürweger³

et al. 2023

Medical Physics

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Background Periodic and slow target motion is tracked by synchronous motion of the treatment beams in robotic stereotactic body radiation therapy (SBRT). However, spontaneous, non‐periodic displacement or drift of the target may completely change the treatment geometry. Simple motion compensation is not sufficient to guarantee the best possible treatment, since relative motion between the target and organs at risk (OARs) can cause substantial deviations of dose in the OARs. This is especially evident when considering the temporally heterogeneous dose delivery by many focused beams which is typical for robotic SBRT. Instead, a reoptimization of the remaining treatment plan after a large target motion during the treatment could potentially reduce the actually delivered dose to OARs and improve target coverage. This reoptimization task, however, is challenging due to time constraints and limited human supervision. Purpose To study the detrimental effect of spontaneous target motion relative to surrounding OARs on the delivered dose distribution and to analyze how intra‐fractional constrained replanning could improve motion compensated robotic SBRT of the prostate. Methods We solve the inverse planning problem by optimizing a linear program. When considering intra‐fractional target motion resulting in a change of geometry, we adapt the linear program to account for the changed dose coefficients and delivered dose. We reduce the problem size by only reweighting beams from the reference treatment plan without motion. For evaluation we simulate target motion and compare our approach for intra‐fractional replanning to the conventional compensation by synchronous beam motion. Results are generated retrospectively on data of 50 patients. Results Our results show that reoptimization can on average retain or improve coverage in case of target motion compared to the reference plan without motion. Compared to the conventional compensation, coverage is improved from 87.83 % to 94.81 % for large target motion. Our approach for reoptimization ensures fixed upper constraints on the dose even after motion, enabling safer intra‐fraction adaption, compared to conventional motion compensation where overdosage in OARs can lead to 21.79 % higher maximum dose than planned. With an average reoptimization time of 6 s for 200 reoptimized beams our approach shows promising performance for intra‐fractional application. Conclusions We show that intra‐fractional constrained reoptimization for adaption to target motion can improve coverage compared to the conventional approach of beam translation while ensuring that upper dose constraints on VOIs are not violated.

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Section: Introductionmentioning

confidence: 99%

“…While fast treatment planning approaches have been explored, 16 only few approaches for intra‐fraction replanning have been shown. Still, the potential benefits have been acknowledged 17 . One approach for intra‐fractional replanning by Antico et al.…”

Section: Introductionmentioning

confidence: 99%

Towards fast adaptive replanning by constrained reoptimization for intra‐fractional non‐periodic motion during robotic SBRT

Gerlach

Hofmann²,

Fürweger³

et al. 2023

Medical Physics

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Leveraging Multicore Servers for Enhanced IMRT Radiotherapy Planning

Riado,

Martín,

Redondo

et al. 2023

Preprint

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This study enhances the computational efficiency of Radiotherapy Plans (RP) utilized in Intensity Modulated Radiotherapy (IMRT). IMRT is a technique that employs radiation beams with varying angles and intensities to deliver radiation to cancerous tissues while safeguarding healthy organs. The planning methods reliant on the gEUD metric yield radiation plans with exceptional PTV (Planning Target Volume) coverage.Nevertheless, computing these plans is a resource-intensive task that entails adjusting numerous parameters and conducting multiple model evaluations. To address this, we have introduced a novel approach that automates the adjustment of gEUD parameters. This is achieved by combining the EUD model, solved through a gradient descent algorithm, with an evolutionary optimization method that explores the EUD parameter space.Given the high computational demands of this approach, integrating it into clinical settings poses a challenge. Our goal is to tackle this challenge by introducing parallelization and batching strategies that leverage the capabilities of multicore servers, aiming to significantly accelerate the optimization process.To evaluate our proposal, we conducted extensive benchmarking on three distinct multicore platforms with varying micro-architectures, assessed across different batch sizes and threads configurations. Our testing dataset consisted of three Head and Neck (H\&N) patients who were treated using IMRT with nine beams.The results showcase that our approach provides substantial computational speed improvements while consistently generating high-quality RT (Radiation Therapy) plans that conform to clinical constraints.

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A conceptual study on real-time adaptive radiation therapy optimization through ultra-fast beamlet control

Cited by 2 publications

References 45 publications

Towards fast adaptive replanning by constrained reoptimization for intra‐fractional non‐periodic motion during robotic SBRT

Towards fast adaptive replanning by constrained reoptimization for intra‐fractional non‐periodic motion during robotic SBRT

Leveraging Multicore Servers for Enhanced IMRT Radiotherapy Planning

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