M Birkner

Adapting inverse planning to patient and organ geometrical variation: algorithm and implementation.

Image guided radiotherapy has the potential to improve both tumour control and normal tissue sparing by including temporal patient specific geometry information into the adaptive planning process. In this study we present a practical method of image guided adaptive inverse planning based on computed tomography (CT) and portal image feedback during the treatment course. The method is based on a general description of the radiotherapy optimization problem subject to dynamic geometrical variations of the patient/organs. We will demonstrate the feasibility of off-line image feedback into the inverse planning process with the example of three prostate cancer patients. CT and portal images acquired during the early course of the treatment are used to predict the geometrical variation distribution of a patient and to re-optimize the treatment plan accordingly. We will study the convergence of the optimization problem with respect to the number of image measurements and adaptive feedback loops.

Monte Carlo dose computation for IMRT optimization

A method which combines the accuracy of Monte Carlo dose calculation with a finite size pencil-beam based intensity modulation optimization is presented. The pencil-beam algorithm is employed to compute the fluence element updates for a converging sequence of Monte Carlo dose distributions. The combination is shown to improve results over the pencil-beam based optimization in a lung tumour case and a head and neck case. Inhomogeneity effects like a broader penumbra and dose build-up regions can be compensated for by intensity modulation.

Treatment simulation approaches for the estimation of the distributions of treatment quality parameters generated by geometrical uncertainties

Geometric uncertainties arise during treatment planning and treatment and mean that dose-dependent parameters such as EUD are random variables with a patient specific probability distribution. Treatment planning with highly conformal treatment techniques such as intensity modulated radiation therapy requires new evaluation tools which allow us to estimate this influence of geometrical uncertainties on the probable treatment dose for a planned dose distribution. Monte Carlo simulations of treatment courses with recalculation of the dose according to the daily geometric errors are a gold standard for such an evaluation. Distribution histograms which show the relative frequency of a treatment quality parameter in the treatment simulations can be used to evaluate the potential risks and chances of a planned dose distribution. As treatment simulations with dose recalculation are very time consuming for sufficient statistical accuracy, it is proposed to do treatment simulations in the dose parameter space where the result is mainly determined by the systematic and random component of the geometrical uncertainties. Comparison of the parameter space simulation method with the gold standard for prostate cases and a head and neck case shows good agreement as long as the number of fractions is high enough and the influence of tissue inhomogeneities and surface curvature on the dose is small.

Tools for the analysis of dose optimization: II. Sensitivity analysis

Dose optimization requires that the treatment goals be specified in a meaningful manner, but also that alterations to the specification lead to predictable changes in the resulting dose distribution. Within the framework of constrained optimization, it is possible to devise a tool that quantifies the impact on the objective of target volume coverage of any change to a dosimetric constraint of normal tissue or target dose homogeneity. This sensitivity analysis relies on properties of the Lagrange function that is associated with the constrained optimization problem, but does not depend on the method used to solve this problem. It is useful particularly in cases with multiple target volumes and critical normal structures, where constraints and objectives can interact in a non-intuitive manner.

IMRT with Monte Carlo dose computation: what is the benefit?

Monte Carlo simulations arguably offer the most readily explorable route to computing radiation transport in complex geometries and inhomogeneous media. With IMRT, not only the dose computation in the patient poses problems, but also the predominantly small field sizes and the modulation devices in the accelerator head. However, IMRT also offers abundant degrees of freedom to compensate for the dose loss due to lateral electron transport at low density surfaces by adjustments to the primary fluence.

WE‐E‐J‐6C‐03: A Method for Modeling Individual Patient Geometric Variation: Implementation and Evaluation

Purpose: We present and evaluate the method of Principal Component Analysis for modeling individual organ motion/deformation. This method provides the most important factors to characterize deformable organ motion, therefore assists adaptive radiotherapy planning. Method and Materials: Input are N organ shape samples, described by the positions of a set of corresponding surface points. The covariance matrix of displacement vectors is determined and diagonalized. Each eigenvector defines a 3D‐vectorfield of correlated displacements for the surface points, a so‐called eigenmode of deformation. Each eigenvalue gives the variance of the shape samples in direction of the corresponding eigenmode, thereby providing an importance ranking for the eigenmodes with respect to the displacement direction and magnitude. Weighted sums of eigenmodes can be used to represent organ displacements/deformations. We evaluated the ability of eigenmodes to represent the measured samples by calculating the residual errors for the organ surface points, using a varying number of eigenmodes. The method was applied to four datasets of prostate/bladder/rectum with N=15–18 CTs to assess interfractional geometric variation. Typically a few thousand surface points were used in the analysis. Results: The spectrum of eigenvalues is clearly dominated by only few values. This indicates that the geometric variability of the input samples of prostate/bladder/rectum shapes is governed by only a few patient specific ‘deformation modes’, quantitatively given by the corresponding eigenvectors. The distribution of residual errors shows convergence with the number of eigenmodes used to represent the organ shapes. Using 4 dominating modes, the range of average residual errors is 1.3–2.0mm (prostate), 1.4–1.9mm (rectum) and 1.5–1.9mm (bladder) for the four patients. Conclusion: Individual geometric variation information taken from multiple imaging data can be described accuratuly by few dominating eigenmodes. This approach provides an efficient statistical model to characterize individual organ deformation, which quantitatively takes into account correlated motion of adjacent organ structures.

Automated Analysis of Film Checks for QA of Stereotactic Treatment Setup

Stereotactic radiotherapy with high doses per treatment strongly depends among other factors on the mechanical accuracy of the setup of the patient at the linear accelerator. A commonly used quality assurance tool for radiosurgery with circular collimators is a film check introduced by Winston and Lutz [1] to evaluate the positional accuracy of the gantry and couch rotation around a radiopaque target ball that is positioned in the isocenter by means of the stereotactic setup. There are different manual methods to determine the shift between the center of the exposures of the circular collimator and the sometimes hardly recognizable target ball on the film that naturally depend on the individual skills of the analyzing person to estimate the appropriate centers. Since stereotactic radiotherapy as well as radiosurgery steadily gain importance in cancer treatment, an automated tool to speed up and standardize the analysis of Winston-Lutz film QA checks would be very helpful.