Continuous aperture dose calculation and optimization for volumetric modulated arc therapy

Christiansen, E; Heath, Emily; Xu, Tingfa

doi:10.1088/1361-6560/aae65e

Cited by 15 publications

(60 citation statements)

References 14 publications

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“…So long as the control points are spaced by no more that around 2°, this approach is accurate . However, when the apertures are small and the MLC is able to move with a considerable speed, the fluence calculated by this method is inadequate to model the actually delivered fluence . One means of overcoming this inadequacy is to use more closely spaced control points.…”

Section: Discussionmentioning

confidence: 99%

“…An alternative method of calculating effective fluence between control points is described by Bedford and can be used with more coarsely spaced control points to provide an accurate dose during optimization. A mathematical means of performing the same calculation is also described by Christiansen et al These methods use the beam orientations of the discrete control points but use the fluence of the moving aperture as it moves between control points. The present study takes the most accurate representation of what is actually delivered to be the optimized plan with segments at 5° intervals recalculated with interpolated segments at 1° intervals and with the method of Christiansen et al or Bedford used between these interpolated segments.…”

Section: Discussionmentioning

confidence: 99%

“…A mathematical means of performing the same calculation is also described by Christiansen et al These methods use the beam orientations of the discrete control points but use the fluence of the moving aperture as it moves between control points. The present study takes the most accurate representation of what is actually delivered to be the optimized plan with segments at 5° intervals recalculated with interpolated segments at 1° intervals and with the method of Christiansen et al or Bedford used between these interpolated segments. When the optimization is based on discrete apertures at 5° node spacing, a significant difference between the optimized dose and the recalculated dose is seen, indicating that the dose during optimization is not accurate.…”

Section: Discussionmentioning

confidence: 99%

“…If the leaves underwent significant motion relative to the aperture width, it was possible for a bixel to lie in both regions simultaneously, that is the ramp down began before the ramp up finished. In this case, the fluence received by the bixel was:

ϕ = Φ (\frac{x - L_{italicinit}}{L_{italicfin} - L_{italicinit}} - \frac{x - R_{italicinit}}{R_{italicfin} - R_{italicinit}}) .

…”

Section: Methodsmentioning

confidence: 99%

“…No modeling of arc motion was carried out during fluence optimization. After sequencing, the allowed MLC motion was included in the optimization, but for dose calculation, one of two methods was used: (a) no motion modeling in the dose calculation, and (b) use of an effective fluence method to model the MLC leaf motion . The former simply assumed a uniform fluence over the aperture at each control point, with no account taken of the change in the aperture between control points.…”

Section: Methodsmentioning

confidence: 99%

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Treatment planning optimization with beam motion modeling for dynamic arc delivery of SBRT using Cyberknife with multileaf collimation

et al. 2019

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Purpose The use of dynamic arcs for delivery of stereotactic body radiation therapy (SBRT) on Cyberknife is investigated, with a view to improving treatment times. This study investigates the required modeling of robot and multileaf collimator (MLC) motion between control points in the trajectory and then uses this to develop an optimization method for treatment planning of a dynamic arc with Cyberknife. The resulting plans are compared in terms of dose‐volume histograms and estimated treatment times with those produced by a conventional beam arrangement. Methods Five SBRT patient cases (prostate A — conventional, prostate B — brachytherapy‐type, lung, liver, and partial left breast) were retrospectively studied. A suitable arc trajectory with control points spaced at 5° was proposed and treatment plans were produced for typical clinical protocols. The optimization consisted of a fluence optimization, segmentation, and direct aperture optimization using a gradient descent method. Dose delivered by the moving MLC was either taken to be the dose delivered discretely at the control points or modeled using effective fluence delivered between control points. The accuracy of calculated dose was assessed by recalculating after optimization using five interpolated beams and 100 interpolated apertures between each optimization control point. The resulting plans were compared using dose‐volume histograms and estimated treatment times with those for a conventional Cyberknife beam arrangement. Results If optimization is performed based on discrete doses delivered at the arc control points, large differences of up to 40% of the prescribed dose are seen when recalculating with interpolation. When the effective fluence between control points is taken into account during optimization, dosimetric differences are <2% for most structures when the plans are recalculated using intermediate nodes, but there are differences of up to 15% peripherally. Treatment plan quality is comparable between the arc trajectory and conventional body path. All plans meet the relevant clinical goals, with the exception of specific structures which overlap with the planning target volume. Median estimated treatment time is 355 s (range 235–672 s) for arc delivery and 675 s (range 554–1025 s) for conventional delivery. Conclusions The method of using effective fluence to model MLC motion between control points is sufficiently accurate to provide for accurate inverse planning of dynamic arcs with Cyberknife. The proposed arcing method produces treatment plans with comparable quality to the body path, with reduced estimated treatment delivery time.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

ϕ = Φ (\frac{x - L_{italicinit}}{L_{italicfin} - L_{italicinit}} - \frac{x - R_{italicinit}}{R_{italicfin} - R_{italicinit}}) .

…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Treatment planning optimization with beam motion modeling for dynamic arc delivery of SBRT using Cyberknife with multileaf collimation

et al. 2019

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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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Dosimetric accuracy of delivering SBRT using dynamic arcs on Cyberknife

2020

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Purpose: Several studies have demonstrated potential improvements in treatment time through the use of dynamic arcs for delivery of stereotactic body radiation therapy (SBRT) on Cyberknife. However, the delivery system has a finite accuracy, so that potential exists for dosimetric uncertainties. This study estimates the expected dosimetric accuracy of dynamic delivery of SBRT, based on realistic estimates of the uncertainties in delivery parameters. Methods: Five SBRT patient cases (prostate Aconventional, prostate Bbrachytherapy-type, lung, liver, partial left breast) were retrospectively studied. Treatment plans were produced for a fixed arc trajectory using fluence optimization, segmentation, and direct aperture optimization. Dose rate uncertainty was modeled as a smoothly varying random fluctuation of AE 1.0%, AE2.0% or AE 5.0% over a time period of 10, 30 or 60 s. Multileaf collimator uncertainty was modeled as a lag in position of each leaf up to 0.25 or 0.5 mm. Robot pointing error was modeled as a shift of the target location, with the direction of the shift chosen as a random angle with respect to the multileaf collimator and with a random magnitude in the range 0.0-1.0 mm at the delivery nodes and with an additional random magnitude of 0.5-1.0 mm in between the delivery nodes. The impact of the errors was investigated using dose-volume histograms. Results: Uncertainty in dose rate has the effect of varying the total monitor units delivered, which in turn produces a variation in mean dose to the planning target volume. The random sampling of dose rate error produces a distribution of mean doses with a standard deviation proportional to the magnitude of the dose rate uncertainty. A lag in multileaf collimator position of 0.25 or 0.5 mm produces a small impact on the delivered dose. In general, an increase in the PTV mean dose of around 1% is observed. An error in robot pointing of the order of 1 mm produces a small increase in dose inhomogeneity to the planning target volume, sometimes accompanied by an increase in mean dose by around 1%. Conclusions: Based upon the limited data available on the dose rate stability and geometric accuracy of the Cyberknife system, this study estimates that dynamic arc delivery can be accomplished with sufficient accuracy for clinical application. Dose rate variation produces a change in dose to the planning target volume according to the perturbation of total monitor units delivered, while multileaf collimator lag and robot pointing error typically increase the mean dose to the planning target volume by up to 1%.

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Continuous aperture dose calculation and optimization for volumetric modulated arc therapy

Cited by 15 publications

References 14 publications

Treatment planning optimization with beam motion modeling for dynamic arc delivery of SBRT using Cyberknife with multileaf collimation

Treatment planning optimization with beam motion modeling for dynamic arc delivery of SBRT using Cyberknife with multileaf collimation

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

Dosimetric accuracy of delivering SBRT using dynamic arcs on Cyberknife

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