A fast inverse direct aperture optimization algorithm for intensity‐modulated radiation therapy

MacFarlane, Michael; Hoover, Douglas A.; Wong, Eugene; Goldman, Pedro; Battista, Jerry; Chen, Jeff Z.

doi:10.1002/mp.13368

Cited by 7 publications

(23 citation statements)

References 20 publications

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“…This economical strategy is not possible with conventional DAO approaches as the objective function value has to effectively be computed before the gradient vector can be calculated. 1 Full details can be found in the original FIDAO paper. 1 The optimization times observed in this study are approaching the reported optimization times of commercial treatment planning systems and by Men et al for GPU-based DAO algorithms for VMAT planning.…”

Section: Discussionmentioning

confidence: 99%

“…1 Full details can be found in the original FIDAO paper. 1 The optimization times observed in this study are approaching the reported optimization times of commercial treatment planning systems and by Men et al for GPU-based DAO algorithms for VMAT planning. 17 standard desktop CPU, without any attempt at parallelization.…”

Section: Discussionmentioning

confidence: 99%

“…21,22 Furthermore, we plan to implement FIDAO within a commercial treatment planning system and compare the FIDAO optimization to a DAO algorithm used clinically as opposed to the research DAO algorithm used in this and the previous study. 1 Finally, we can parallelize FIDAO's computations on a GPU and evaluate its performance.…”

Section: Discussionmentioning

confidence: 99%

“…For fixed-gantry intensity-modulated radiotherapy (IMRT), m ( n, and as a result, FIDAO executes up to 200-times faster than a conventional DAO method. 1 No significant differences in plan quality was observed between the plans optimized with FIDAO and the conventional method.…”

Section: Introductionmentioning

confidence: 93%

“…Medical Physics, 47 (4), April 2020 objective function proposed in the previous FIDO publications. [1][2][3] Details and the derivation of FIDAO's equations can be found in the original FIDAO paper applied to fixed-gantry IMRT. 1 Briefly, let sðl; wÞ be the effective fluence maps produced by the aperture leaf positions l and aperture weights w, d xi be the precomputed dose calculation matrix, p ptv and p oar be the objective penalty weights assigned to the planning target volume (PTV) and organs at risk (OAR), respectively, and d ptv be the dose prescribed to the PTV.…”

Section: Prostatementioning

confidence: 99%

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Technical Note: A fast inverse direct aperture optimization algorithm for volumetric‐modulated arc therapy

et al. 2020

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Purpose: In a recent article, our group proposed a fast direct aperture optimization (DAO) algorithm for fixed-gantry intensity-modulated radiation therapy (IMRT) called fast inverse direct aperture optimization (FIDAO). When tested on fixed-gantry IMRT plans, we observed up to a 200-fold increase in the optimization speed. Compared to IMRT, rotational volumetric-modulated arc therapy (VMAT) is a much larger optimization problem and has many more delivery constraints. The purpose of this work is to extend and evaluate FIDAO for inverse planning of VMAT plans. Methods: A prototype FIDAO algorithm for VMAT treatment planning was developed in MATLAB using the open-source treatment planning toolkit matRad (v2.2 dev_VMAT build). VMAT treatment plans using one 360 0 arc were generated on the AAPM TG-119 phantom, as well as sample clinical liver and prostate cases. The plans were created by first performing fluence map optimization on 28°e quispaced beams, followed by aperture sequencing and arc sequencing with a gantry angular sampling rate of 4°. After arc sequencing, a copy of the plan underwent DAO using the prototype FIDAO algorithm, while another copy of the plan underwent DAO using matRad's DAO method, which served as the conventional algorithm. Results: Both algorithms achieved similar plan quality, although the FIDAO plans had considerably fewer hot spots in the unspecified normal tissue. The optimization time (number of iterations) for FIDAO and the conventional DAO algorithm, respectively, were: 65 s (245) vs 602 s (275) in the TG-119 phantom case; 25 s (85) vs 803 s (159) in the liver case; and 99 s (174) vs 754 s (149) in the prostate case.Conclusions: This study demonstrated promising speed enhancements in using FIDAO for the direct aperture optimization of VMAT plans.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 93%

Section: Prostatementioning

confidence: 99%

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Technical Note: A fast inverse direct aperture optimization algorithm for volumetric‐modulated arc therapy

et al. 2020

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Quantum‐inspired algorithm for radiotherapy planning optimization

et al. 2019

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Purpose: Modern inverse radiotherapy treatment planning requires nonconvex, large-scale optimizations that must be solved within a clinically feasible timeframe. We have developed and tested a quantum-inspired, stochastic algorithm for intensity-modulated radiotherapy (IMRT): quantum tunnel annealing (QTA). By modeling the likelihood probability of accepting a higher energy solution after a particle tunneling through a potential energy barrier, QTA features an additional degree of freedom (the barrier width, w) not shared by traditional stochastic optimization methods such as Simulated Annealing (SA). This additional degree of freedom can improve convergence rates and achieve a more efficient and, potentially, effective treatment planning process. Methods: To analyze the character of the proposed QTA algorithm, we chose two stereotactic body radiation therapy (SBRT) liver cases of variable complexity. The "easy" first case was used to confirm functionality, while the second case, with a more challenging geometry, was used to characterize and evaluate the QTA algorithm performance. Plan quality was assessed using dose-volume histogram-based objectives and dose distributions. Due to the stochastic nature of the solution search space, extensive tests were also conducted to determine the optimal smoothing technique, ensuring balance between plan deliverability and the resulting plan quality. QTA convergence rates were investigated in relation to the chosen barrier width function, and QTA and SA performances were compared regarding sensitivity to the choice of solution initializations, annealing schedules, and complexity of the dose-volume constraints. Finally, we investigated the extension from beamlet intensity optimization to direct aperture optimization (DAO). Influence matrices were calculated using the Eclipse scripting application program interface (API), and the optimizations were run on the University of Michigan's high-performance computing cluster, Flux. Results: Our results indicate that QTA's barrier-width function can be tuned to achieve faster convergence rates. The QTA algorithm reached convergence up to 46.6% faster than SA for beamlet intensity optimization and up to 26.8% faster for DAO. QTA and SA were ultimately found to be equally insensitive to the initialization process, but the convergence rate of QTA was found to be more sensitive to the complexity of the dose-volume constraints. The optimal smoothing technique was found to be a combination of a Laplace-of-Gaussian (LOG) edge-finding filter implemented as a penalty within the objective function and a two-dimensional Savitzky-Golay filter applied to the final iteration; this achieved total monitor units more than 20% smaller than plans optimized by commercial treatment planning software. Conclusions: We have characterized the performance of a stochastic, quantum-inspired optimization algorithm, QTA, for radiotherapy treatment planning. This proof of concept study suggests that QTA can be tuned to achieve faster convergence than SA; therefore, ...

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Feasibility and analysis of CNN‐based candidate beam generation for robotic radiosurgery

Gerlach

Fürweger

Hofmann³

et al. 2020

Medical Physics

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Purpose: Robotic radiosurgery offers the flexibility of a robotic arm to enable high conformity to the target and a steep dose gradient. However, treatment planning becomes a computationally challenging task as the search space for potential beam directions for dose delivery is arbitrarily large. We propose an approach based on deep learning to improve the search for treatment beams. Methods: In clinical practice, a set of candidate beams generated by a randomized heuristic forms the basis for treatment planning. We use a convolutional neural network to identify promising candidate beams. Using radiological features of the patient, we predict the influence of a candidate beam on the delivered dose individually and let this prediction guide the selection of candidate beams. Features are represented as projections of the organ structures which are relevant during planning. Solutions to the inverse planning problem are generated for random and CNN-predicted candidate beams. Results: The coverage increases from 95.35% to 97.67% for 6000 heuristically and CNN-generated candidate beams, respectively. Conversely, a similar coverage can be achieved for treatment plans with half the number of candidate beams. This results in a patient-dependent reduced averaged computation time of 20.28%-45.69%. The number of active treatment beams can be reduced by 11.35% on average, which reduces treatment time. Constraining the maximum number of candidate beams per beam node can further improve the average coverage by 0.75 percentage points for 6000 candidate beams. Conclusions: We show that deep learning based on radiological features can substantially improve treatment plan quality, reduce computation runtime, and treatment time compared to the heuristic approach used in clinics.

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A fast inverse direct aperture optimization algorithm for intensity‐modulated radiation therapy

Cited by 7 publications

References 20 publications

Technical Note: A fast inverse direct aperture optimization algorithm for volumetric‐modulated arc therapy

Technical Note: A fast inverse direct aperture optimization algorithm for volumetric‐modulated arc therapy

Quantum‐inspired algorithm for radiotherapy planning optimization

Feasibility and analysis of CNN‐based candidate beam generation for robotic radiosurgery

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