Rudi Labarbe

The Reconstruction Toolkit (RTK), an open-source cone-beam CT reconstruction toolkit based on the Insight Toolkit (ITK)

Purpose: To develop an open-source toolkit for fast cone-beam CT reconstruction based on the Insight Toolkit. Methods: We have started the Reconstruction Toolkit (RTK, http://www.openrtk.org), an open-source toolkit for cone-beam CT reconstruction, based on the Insight Toolkit (ITK, http://www.itk.org/) and using GPU code extracted from Plastimatch (http://www.plastimatch.org/). RTK is developed by an open consortium (see affiliations) under the non- contaminating Apache 2.0 license. The quality of the platform is daily checked with regression tests in partnership with Kitware which already supports ITK. Results: Several features are already available: Elekta, Varian and IBA inputs, multi-threaded Feldkamp-David-Kress reconstruction on CPU and GPU, Parker short scan weighting, multi-threaded CPU and GPU forward projectors, etc. Each feature is either accessible through command line tools or C++ classes that can be included in independent software. A MIDAS community (http://midas3.kitware.com) has been opened to provide CatPhan datasets of several vendors (Elekta, Varian and IBA). RTK will be used in the upcoming cone-beam CT scanner developed by IBA for proton therapy rooms. Many features are under development: new input format support, iterative reconstruction, hybrid Monte Carlo / deterministic CBCT simulation, etc. Conclusions: RTK has been built to freely share tomographic reconstruction development between researchers and is open for new contributions.

Reassessment of stopping power ratio uncertainties caused by mean excitation energies using a water‐based formalism

Purpose In proton therapy planning, the accuracy of the Stopping Power Ratios (SPR) calculated in the stoichiometric CT calibration is affected by, among others, uncertainties on the mean excitation energies (I‐values) of human tissues and water. Traditionally, the contribution of these uncertainties on the SPR has been conservatively estimated of the order of 1% or more for a reference tissue of known composition. This study provides a methodology that enables a finer estimation of this uncertainty, eventually showing that the traditional estimates of the uncertainty are too conservative. Methods Since human tissues contain water, a correlation exists between the I‐values of tissues and water. As the SPR is the ratio of the tissue stopping power to that of water, this correlation decreases the uncertainty of the SPR. Our formalism considers this by expressing the I‐value of the tissue as a function of the water weight fraction and the I‐value of water, while applying Braggs additivity rule only to the nonaqueous mixture of the tissue. For 22 reference tissue compositions, the SPR uncertainty was estimated by randomly sampling Gaussian distributions, based on ICRU data, for the I‐values of water and the nonaqueous mixture, as well as for the water weight fraction. Results The relative standard deviation of the SPR, estimated at 150 MeV, is in the range of 0.1%–0.3% for soft tissues with an average water weight percentage of at least 60%. For tissues with a low water content (e.g., adipose and bones), this uncertainty is in the range of 0.5%–0.7%. Conclusion Uncertainties on the I‐values of human tissues and water appear to have a significantly lower impact on the SPR uncertainty than traditionally expected. In the future, this may provide a rationale for using smaller distal and proximal margins on the target volume, provided that all other range uncertainty components are correctly estimated too.

Estimating patient specific uncertainty parameters for adaptive treatment re-planning in proton therapy using in vivo range measurements and Bayesian inference: application to setup and stopping power errors.

In proton therapy, quantification of the proton range uncertainty is important to achieve dose distribution compliance. The promising accuracy of prompt gamma imaging (PGI) suggests the development of a mathematical framework using the range measurements to convert population based estimates of uncertainties into patient specific estimates with the purpose of plan adaptation. We present here such framework using Bayesian inference. The sources of uncertainty were modeled by three parameters: setup bias m, random setup precision r and water equivalent path length bias u. The evolution of the expectation values E(m), E(r) and E(u) during the treatment was simulated. The expectation values converged towards the true simulation parameters after 5 and 10 fractions, for E(m) and E(u), respectively. E(r) settle on a constant value slightly lower than the true value after 10 fractions. In conclusion, the simulation showed that there is enough information in the frequency distribution of the range errors measured by PGI to estimate the expectation values and the confidence interval of the model parameters by Bayesian inference. The updated model parameters were used to compute patient specific lateral and local distal margins for adaptive re-planning.

SU-F-J-65: Prediction of Patient Setup Errors and Errors in the Calibration Curve from Prompt Gamma Proton Range Measurements

PURPOSE To understand the extent to which the prompt gamma camera measurements can be used to predict the residual proton range due to setup errors and errors in the calibration curve. METHODS We generated ten variations on a default calibration curve (CC) and ten corresponding range maps (RM). Starting with the default RM, we chose a square array of N beamlets, which were then rotated by a random angle θ and shifted by a random vector s. We added a 5% distal Gaussian noise to each beamlet in order to introduce discrepancies that exist between the ranges predicted from the prompt gamma measurements and those simulated with Monte Carlo algorithms. For each RM, s, θ, along with an offset u in the CC, were optimized using a simple Euclidian distance between the default ranges and the ranges produced by the given RM. RESULTS The application of our method lead to the maximal overrange of 2.0mm and underrange of 0.6mm on average. Compared to the situations where s, θ, and u were ignored, these values were larger: 2.1mm and 4.3mm. In order to quantify the need for setup error corrections, we also performed computations in which u was corrected for, but s and θ were not. This yielded: 3.2mm and 3.2mm. The average computation time for 170 beamlets was 65 seconds. CONCLUSION These results emphasize the necessity to correct for setup errors and the errors in the calibration curve. The simplicity and speed of our method makes it a good candidate for being implemented as a tool for in-room adaptive therapy. This work also demonstrates that the Prompt gamma range measurements can indeed be useful in the effort to reduce range errors. Given these results, and barring further refinements, this approach is a promising step towards an adaptive proton radiotherapy.

Feasibility and preliminary validation of 2D/3D image registration using fixed 2-D X-ray devices in image-guided radiotherapy

In radiotherapy, fixed 2-D X-ray imaging devices have several advantages compared to gantry-mounted systems, such as less geometrical deformations and the possibility to monitor 3-D markers motion in real-time. However, there is a lack of studies concerning the geometry of these systems. For example, in the case of a non-orthogonal geometry, the effect of the angle between the X-ray axes has not been investigated yet. In the first part of this study, the optimal angle was analyzed theoretically. Results showed that 60° between the axes still enables displacements of the order of 0.35 mm to be detected. In a second step, the performance of the registration method for such oblique configuration was evaluated on phantom data sets. It was found that using images separated by 60° rather than 90° required more than twice as much the number of iterations to obtain sufficient accuracy (i.e. 0.7 mm and 0.5°).

PO-0783: Design of cone-beam CT for proton therapy gantry

Purpose/Objective: Cone-beam CT is widely used in radiation therapy proving tremendous interest for patient positioning and daily monitoring of anatomical modifications. Moreover, the use of volumetric imaging can contribute to a more accurate location of the target volume which is important in proton therapy (PT) where sharp dose gradients are used. Despite this potential advantage, integrating a CBCT on a PT gantry has not already been done and poses additional challenges such as potentially larger geometrical deformations compared to a LINAC (Table 1). In this paper, we describe how the first CBCT has been implemented in a PT gantry.The general design, choice of hardware, and software implementation are outlined and results on a phantom demonstrate the possibilities of the system.