4D dose simulation in volumetric arc therapy: Accuracy and affecting parameters

Abstract

Radiotherapy of lung and liver lesions has changed from normofractioned 3D-CRT to stereotactic treatment in a single or few fractions, often employing volumetric arc therapy (VMAT)-based techniques. Potential unintended interference of respiratory target motion and dynamically changing beam parameters during VMAT dose delivery motivates establishing 4D quality assurance (4D QA) procedures to assess appropriateness of generated VMAT treatment plans when taking into account patient-specific motion characteristics. Current approaches are motion phantom-based 4D QA and image-based 4D VMAT dose simulation. Whereas phantom-based 4D QA is usually restricted to a small number of measurements, the computational approaches allow simulating many motion scenarios. However, 4D VMAT dose simulation depends on various input parameters, influencing estimated doses along with mitigating simulation reliability. Thus, aiming at routine use of simulation-based 4D VMAT QA, the impact of such parameters as well as the overall accuracy of the 4D VMAT dose simulation has to be studied in detail-which is the topic of the present work. In detail, we introduce the principles of 4D VMAT dose simulation, identify influencing parameters and assess their impact on 4D dose simulation accuracy by comparison of simulated motion-affected dose distributions to corresponding dosimetric motion phantom measurements. Exploiting an ITV-based treatment planning approach, VMAT treatment plans were generated for a motion phantom and different motion scenarios (sinusoidal motion of different period/direction; regular/irregular motion). 4D VMAT dose simulation results and dose measurements were compared by local 3% / 3 mm γ-evaluation, with the measured dose distributions serving as ground truth. Overall γ-passing rates of simulations and dynamic measurements ranged from 97% to 100% (mean across all motion scenarios: 98% ± 1%); corresponding values for comparison of different day repeat measurements were between 98% and 100%. Parameters of major influence on 4D VMAT dose simulation accuracy were the degree of temporal discretization of the dose delivery process (the higher, the better) and correct alignment of the assumed breathing phases at the beginning of the dose measurements and simulations. Given the high γ-passing rates between simulated motion-affected doses and dynamic measurements, we consider the simulations to provide a reliable basis for assessment of VMAT motion effects that-in the sense of 4D QA of VMAT treatment plans-allows to verify target coverage in hypofractioned VMAT-based radiotherapy of moving targets. Remaining differences between measurements and simulations motivate, however, further detailed studies.

Fig 1. Experimental setup.

Left: Measurement setup: 4D motion platform with detector array and lung phantom, consisting of bone, lung and tissue equivalent materials. Right: Average CT of setup, planned VMAT dose distribution, and target structures/organs at risk.

Fig 2. Patient motion scenarios.

SI motion amplitudes of applied regular and irregular tumor trajectories.

Fig 3. Study design and evaluation strategy.

Illustration of performed experiments for the SI-only sinusoidal motion with 4.5 s period (i. e. case 1d); for details see text. Left column: planned dose distribution (top), simulated motion-affected dose (middle; arc discretization of 2.3°), γ-map for comparison of the two (bottom). Middle column: measured static dose (top), measured dynamic dose (middle), γ-comparison (bottom). Right column: γ-comparison of planned and measured static dose (top), γ-comparison of simulated motion-affected and corresponding measured dose (middle), γ-comparison of repeat dynamic measurements (bottom).

Fig 4. Influence of arc discretization.

Illustration of the influence of arc discretization on simulated motion effects. 3^rd row: γ-comparison to planned dose for finest possible arc discretization; 4^th row: no discretization. Results have to be compared to γ-maps between static and motion-affected measurements in 1^st and 2^nd row. Differences between the simulation γ-maps and the measurement γ-maps should be as small as possible.

Fig 5. Starting phase influence.

Influence of breathing phase at dose delivery beginning. Left, top: In accordance with the measurements, all previous results were computed with the simulations starting at the breathing phase at t = 0 s of the curve (here: case 1b). Now, this starting phase was systematically varied by adding offsets $\Delta t\in [0\text{s};10\text{s}]$. Left, bottom: The ITV γ-passing rates for comparison of planned static and motion-affected simulated dose distributions are shown as red lines (solid lines: Δα = 2.3°; dashed: Δα = 150°); the black lines visualize the dependence of the difference between dynamic measurement and simulated motion-affected dose on the starting phase. Right: similar information but for the regular real tumor trajectory (case 2a).