No abstract

Determining the 'best' optimization parameters in IMRT planning is typically a time-consuming trial-and-error process with no unambiguous termination point. Recently we and others proposed a goal-programming approach which better captures the desired prioritization of dosimetric goals. Here, individual prescription goals are addressed stepwise in their order of priority. In the first step, only the highest order goals are considered (target coverage and dose-limiting normal structures). In subsequent steps, the achievements of the previous steps are turned into hard constraints and lower priority goals are optimized, in turn, subject to higher priority constraints. So-called 'slip' factors were introduced to allow for slight, clinically acceptable violations of the constraints. Focusing on head and neck cases, we present several examples for this planning technique. The main advantages of the new optimization method are (i) its ability to generate plans that meet the clinical goals, as well as possible, without tuning any weighting factors or dose-volume constraints, and (ii) the ability to conveniently include more terms such as fluence map smoothness. Lower level goals can be optimized to the achievable limit without compromising higher order goals. The prioritized prescription-goal planning method allows for a more intuitive and human-time-efficient way of dealing with conflicting goals compared to the conventional trial-and-error method of varying weighting factors and dose-volume constraints.

Recent studies have demonstrated that Monte Carlo (MC) denoising techniques can reduce MC radiotherapy dose computation time significantly by preferentially eliminating statistical fluctuations ('noise') through smoothing. In this study, we compare new and previously published approaches to MC denoising, including 3D wavelet threshold denoising with sub-band adaptive thresholding, content adaptive mean-median-hybrid (CAMH) filtering, locally adaptive Savitzky-Golay curve-fitting (LASG), anisotropic diffusion (AD) and an iterative reduction of noise (IRON) method formulated as an optimization problem. Several challenging phantom and computed-tomography-based MC dose distributions with varying levels of noise formed the test set. Denoising effectiveness was measured in three ways: by improvements in the mean-square-error (MSE) with respect to a reference (low noise) dose distribution; by the maximum difference from the reference distribution and by the 'Van Dyk' pass/fail criteria of either adequate agreement with the reference image in low-gradient regions (within 2% in our case) or, in high-gradient regions, a distance-to-agreement-within-2% of less than 2 mm. Results varied significantly based on the dose test case: greater reductions in MSE were observed for the relatively smoother phantom-based dose distribution (up to a factor of 16 for the LASG algorithm); smaller reductions were seen for an intensity modulated radiation therapy (IMRT) head and neck case (typically, factors of 2-4). Although several algorithms reduced statistical noise for all test geometries, the LASG method had the best MSE reduction for three of the four test geometries, and performed the best for the Van Dyk criteria. However, the wavelet thresholding method performed better for the head and neck IMRT geometry and also decreased the maximum error more effectively than LASG. In almost all cases, the evaluated methods provided acceleration of MC results towards statistically more accurate results.

Intensity modulated radiation therapy treatment planning (IMRTP) is a challenging application of optimization technology. We present software tools to facilitate IMRTP research by computational scientists who may not have convenient access to radiotherapy treatment planning systems. The tools, developed within Matlab and CERR (computational environment for radiotherapy research), allow convenient access, visualization, programmable manipulation, and sharing of patient treatment planning data, as well as convenient generation of dosimetric data needed as input for treatment plan optimization research. CERR/Matlab

Purpose: IMRT QA currently is currently a time consuming activity. We hypothesize that IMRT QA labor would significantly decrease if a software tool were available which conveniently re‐checked IMRT treatment plans by independently re‐computing the expected dose distribution based on the leaf‐instructions generated by the treatment planning system. Such a system should have the capability to make detailed comparisons with the original treatment plan. Method and Materials: Our research treatment planning system CERR (Computational Environment for Radiotherapy Research) was modified and extended to include the capability of recomputing dose based on leaf‐sequences received via DICOM. Three dose computation algorithms have been implemented: (1) a simple pencil beam model which corrects for changes in central axis attenuation, but includes realistic scatter tails, (2) the Monte Carlo code VMC++, and (3) the open source Monte Carlo code DPM (dose planning method). GUI tools were developed to allow for side‐by‐side dose comparisons and comparative profile dose plots, in addition to DVH comparisons. Initial tests were performed using data generated via the Varian Helios planning system. Comparisons were made beam‐by‐beam in a simplified QA geometry as well as for total dose. Results: Initial results indicate reasonable agreement between the Helios planning system dose distributions and the pencil beam method. Differences with Monte Carlo results were greater, but energy spectral effects have not yet been added to the model. Conclusion: CERR provides a powerful and convenient environment to develop an IMRT plan re‐calculator. Initial results indicate the basic correctness of data being used for the dose recalculation. We expect to fully develop this system as a helpful tool for IMRT QA. Conflict of Interest: This research was supported by a grant from Sun Nuclear, Inc.