A dosimetric comparison of real-time adaptive and non-adaptive radiotherapy: A multi-institutional study encompassing robotic, gimbaled, multileaf collimator and couch tracking

AbstractPurposeA study of real-time adaptive radiotherapy systems was performed to test the hypothesis that, across delivery systems and institutions, the dosimetric accuracy is improved with adaptive treatments over non-adaptive radiotherapy in the presence of patient-measured tumor motion.Methods and materialsTen institutions with robotic(2), gimbaled(2), MLC(4) or couch tracking(2) used common materials including CT and structure sets, motion traces and planning protocols to create a lung and a prostate plan. For each motion trace, the plan was delivered twice to a moving dosimeter; with and without real-time adaptation. Each measurement was compared to a static measurement and the percentage of failed points for γ-tests recorded.ResultsFor all lung traces all measurement sets show improved dose accuracy with a mean 2%/2mm γ-fail rate of 1.6% with adaptation and 15.2% without adaptation (p<0.001). For all prostate the mean 2%/2mm γ-fail rate was 1.4% with adaptation and 17.3% without adaptation (p<0.001). The difference between the four systems was small with an average 2%/2mm γ-fail rate of <3% for all systems with adaptation for lung and prostate.ConclusionsThe investigated systems all accounted for realistic tumor motion accurately and performed to a similar high standard, with real-time adaptation significantly outperforming non-adaptive delivery methods

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

<div><p>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 <i>γ</i>-evaluation, with the measured dose distributions serving as ground truth. Overall <i>γ</i>-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 <i>γ</i>-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.</p></div

ITV <i>γ</i>-passing rates for comparison of static dose distributions and dynamic dose measurements/simulations.

<p>ITV <i>γ</i>-passing rates for comparison of static dose distributions and dynamic dose measurements/simulations.</p

Study design and evaluation strategy.

<p>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°), <i>γ</i>-map for comparison of the two (bottom). Middle column: measured static dose (top), measured dynamic dose (middle), <i>γ</i>-comparison (bottom). Right column: <i>γ</i>-comparison of planned and measured static dose (top), <i>γ</i>-comparison of simulated motion-affected and corresponding measured dose (middle), <i>γ</i>-comparison of repeat dynamic measurements (bottom).</p

Motion characteristics: maximum and mean peak-to-peak amplitudes, mean breathing cycle lengths.

<p>Motion characteristics: maximum and mean peak-to-peak amplitudes, mean breathing cycle lengths.</p

Starting phase influence.

<p>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 <i>t</i> = 0 s of the curve (here: case 1b). Now, this starting phase was systematically varied by adding offsets . Left, bottom: The ITV <i>γ</i>-passing rates for comparison of planned static and motion-affected simulated dose distributions are shown as red lines (solid lines: Δ<i>α</i> = 2.3°; dashed: Δ<i>α</i> = 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).</p

Patient motion scenarios.

<p>SI motion amplitudes of applied regular and irregular tumor trajectories.</p

Total <i>γ</i>-passing rates for comparison of static dose measurements to dynamic measurements (lines ‘Day 1’ and ‘Day 2’) and <i>γ</i>-passing rates for comparison of the statically planned dose and the dose distributions containing simulated motion effects.

<p>Total <i>γ</i>-passing rates for comparison of static dose measurements to dynamic measurements (lines ‘Day 1’ and ‘Day 2’) and <i>γ</i>-passing rates for comparison of the statically planned dose and the dose distributions containing simulated motion effects.</p

Analysis of the influence of imaging-related uncertainties on cerebral aneurysm deformation quantification using a no-deformation physical flow phantom

Cardiac-cycle related pulsatile aneurysm motion and deformation is assumed to provide valuable information for assessing cerebral aneurysm rupture risk. Accordingly, numerous studies addressed quantification of cerebral aneurysm wall motion and deformation. Most of them utilized in vivo imaging data, but image-based aneurysm deformation quantification is subject to pronounced uncertainties: unknown ground-truth deformation; image resolution in the order of the expected deformation; direct interplay between contrast agent inflow and image intensity. To analyze the impact of the uncertainties on deformation quantification, a multi-imaging modality ground-truth phantom study is performed. A physical flow phantom was designed that allowed simulating pulsatile flow through a variety of modeled cerebral vascular structures. The phantom was imaged using different modalities [MRI, CT, 3D-RA] and mimicking physiologically realistic flow conditions. Resulting image data was analyzed by an established registration-based approach for automated wall motion quantification. The data reveals severe dependency between contrast media inflow-related image intensity changes and the extent of estimated wall deformation. The study illustrates that imaging-related uncertainties affect the accuracy of cerebral aneurysm deformation quantification, suggesting that in vivo imaging studies have to be accompanied by ground-truth phantom experiments to foster data interpretation and to prove plausibility of the applied image analysis algorithms