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- W2016274577 abstract "Purpose: To derive optimal correction strategies for setup errors and to analyze their impact upon treatment margins. Materials and Methods: New concepts like image-guided radiotherapy aim to provide an increasing amount of targeting information during treatment. Future treatment devices incorporating imaging capabilities will facilitate frequent correction of treatment setup errors. It is therefore possible to design new correction protocols which not only reduce systematic but also random setup errors. A novel, very general approach to develop optimal correction strategies is derived from linear systems theory. In the simplest approach, the state variable of the system is the spatial displacement of the center-of-mass of the clinical target volume (CTV) with respect to the planning CT. Assuming no organ motion, the input to the system represents the systematic setup error, and the (internal) process error is the random setup error. Uncertainties in the displacement measurement arising from organ deformation and image processing (delineation of the CTV) is naturally incorporated in the linear systems model. At this stage, the imperfectness of the correction is taken into account by defining an action level, only above which a correction is performed. Simulations of the model were performed for a treatment with n = 30 fractions on a patient population with standard deviation (SD) Σ for the systematic error and σ for the average random error. A two-phase correction protocol was implemented. For the first phase, reduction of systematic setup errors, an online strategy of daily corrections with a zero action level during the first m fractions was chosen. Assuming no measurement error, the running average estimate after m fractions leads to a residual systematic error uncertainty of σ/sqrt(m). For m = 5, the residual total setup error has SD σ′ = 1.1 σ. During the remaining n - m fractions, a Kalman filter was applied to determine an optimal estimate of the displacement from measurements with uncertainty σm. Results: The figure shows the residual standard deviation of the error in one dimension as a function of the normalized action level after performing different correction strategies in the second phase of the protocol. Daily corrections reduce the error most efficiently, whereas weekly corrections affect the error only marginally. For no measurement uncertainty and low action level, the error can theoretically be eliminated completely. The presence of measurement uncertainty, σm > 0, inhibits the efficiency of such correction strategies. However, using the optimal estimates from the Kalman filter can still lead to a significant error reduction. The plot indicates that, for low action levels, the margin can be reduced by 40% even for relatively large σm = 0.5 σ′. Conclusion: Frequent corrections are an effective means of reducing the impact of systematic and random setup errors and therefore also the treatment margins. The model described above is very general and allows the study of a large variety of adaptive correction protocols." @default.
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- W2016274577 modified "2023-09-27" @default.
- W2016274577 title "Optimal correction strategies for reducing systematic and random setup errors and their impact on treatment margins" @default.
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- W2016274577 doi "https://doi.org/10.1016/s0360-3016(01)01993-9" @default.
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