Investigation of geometrical and scoring grid resolution for Monte Carlo dose calculations for IMRT

Efficiency enhancements of a Monte Carlo beamlet based treatment planning process: implementation and parameter study

Objective.The computational effort to perform beamlet calculation, plan optimization and final dose calculation of a treatment planning process (TPP) generating intensity modulated treatment plans is enormous, especially if Monte Carlo (MC) simulations are used for dose calculation. The goal of this work is to improve the computational efficiency of a fully MC based TPP for static and dynamic photon, electron and mixed photon-electron treatment techniques by implementing multiple methods and studying the influence of their parameters.Approach.A framework is implemented calculating MC beamlets efficiently in parallel on each available CPU core. The user can specify the desired statistical uncertainty of the beamlets, a fractional sparse dose threshold to save beamlets in a sparse format and minimal distances to the PTV surface from which 2 × 2 × 2 = 8 (medium) or even 4 × 4 × 4 = 64 (large) voxels are merged. The compromise between final plan quality and computational efficiency of beamlet calculation and optimization is studied for several parameter values to find a reasonable trade-off. For this purpose, four clinical and one academic case are considered with different treatment techniques.Main results.Setting the statistical uncertainty to 5% (photon beamlets) and 15% (electron beamlets), the fractional sparse dose threshold relative to the maximal beamlet dose to 0.1% and minimal distances for medium and large voxels to the PTV to 1 cm and 2 cm, respectively, does not lead to substantial degradation in final plan quality compared to using 2.5% (photon beamlets) and 5% (electron beamlets) statistical uncertainty and no sparse format nor voxel merging. Only OAR sparing is slightly degraded. Furthermore, computation times are reduced by about 58% (photon beamlets), 88% (electron beamlets) and 96% (optimization).Significance.Several methods are implemented improving computational efficiency of beamlet calculation and plan optimization of a fully MC based TPP without substantial degradation in final plan quality.

Beam modeling and beam model commissioning for Monte Carlo dose calculation-based radiation therapy treatment planning: Report of AAPM Task Group 157

Dose calculation plays an important role in the accuracy of radiotherapy treatment planning and beam delivery. The Monte Carlo (MC) method is capable of achieving the highest accuracy in radiotherapy dose calculation and has been implemented in many commercial systems for radiotherapy treatment planning. The objective of this task group was to assist clinical physicists with the potentially complex task of acceptance testing and commissioning MC-based treatment planning systems (TPS) for photon and electron beam dose calculations. This report provides an overview on the general approach of clinical implementation and testing of MC-based TPS with a specific focus on models of clinical photon and electron beams. Different types of beam models are described including those that utilize MC simulation of the treatment head and those that rely on analytical methods and measurements. The trade-off between accuracy and efficiency in the various source-modeling approaches is discussed together with guidelines for acceptance testing of MC-based TPS from the clinical standpoint. Specific recommendations are given on methods and practical procedures to commission clinical beam models for MC-based TPS.

Improved normal tissue sparing in head and neck radiotherapy using biological cost function based-IMRT

Intensity-modulated radiotherapy (IMRT) has reduced the impact of acute and late toxicities associated with head and neck radiotherapy. Treatment planning system (TPS) advances in biological cost function based optimization (BBO) and improved segmentation techniques have increased organ at risk (OAR) sparing compared to conventional dose-based optimization (DBO). A planning study was undertaken to compare OAR avoidance in DBO and BBO treatment planning. Simultaneous integrated boost treatment plans were produced for 10 head and neck patients using both planning systems. Plans were compared for tar get coverage and OAR avoidance. Comparisons were made using the BBO TPS Monte Carlo dose engine to eliminate differences due to inherent algorithms. Target coverage (V95%) was maintained for both solutions. BBO produced lower OAR doses, with statistically significant improvement to left (12.3%, p = 0.005) and right parotid mean dose (16.9%, p = 0.004), larynx V50 Gy (71.0%, p = 0.005), spinal cord (21.9%, p < 0.001) and brain stem dose maximums (31.5%, p = 0.002). This study observed improved OAR avoidance with BBO planning. Further investigations will be undertaken to review any clinical benefit of this improved planned dosimetry.

An anatomically realistic lung model for Monte Carlo-based dose calculations

Treatment planning for disease sites with large variations of electron density in neighboring tissues requires an accurate description of the geometry. This self-evident statement is especially true for the lung, a highly complex organ having structures with a wide range of sizes that range from about 10(-4) to 1 cm. In treatment planning, the lung is commonly modeled by a voxelized geometry obtained using computed tomography (CT) data at various resolutions. The simplest such model, which is often used for QA and validation work, is the atomic mix or mean density model, in which the entire lung is homogenized and given a mean (volume-averaged) density. The purpose of this paper is (i) to describe a new heterogeneous random lung model, which is based on morphological data of the human lung, and (ii) use this model to assess the differences in dose calculations between an actual lung (as represented by our model) and a mean density (homogenized) lung. Eventually, we plan to use the random lung model to assess the accuracy of CT-based treatment plans of the lung. For this paper, we have used Monte Carlo methods to make accurate comparisons between dose calculations for the random lung model and the mean density model. For four realizations of the random lung model, we used a single photon beam, with two different energies (6 and 18 MV) and four field sizes (1 x 1, 5 x 5, 10 x 10, and 20 x 20 cm2). We found a maximum difference of 34% of D(max) with the 1 x 1, 18 MV beam along the central axis (CAX). A "shadow" region distal to the lung, with dose reduction up to 7% of D(max), exists for the same realization. The dose perturbations decrease for larger field sizes, but the magnitude of the differences in the shadow region is nearly independent of the field size. We also observe that, compared to the mean density model, the random structures inside the heterogeneous lung can alter the shape of the isodose lines, leading to a broadening or shrinking of the penumbra region. For small field sizes, the mean lung doses significantly depend on the structures' relative locations to the beam. In addition to these comparisons between the random lung and mean density models, we also provide a preliminary comparison between dose calculations for the random lung model and a voxelized version of this model at 0.4 x 0.4 x 0.4 cm3 resolution. Overall, this study is relevant to treatment planning for lung tumors, especially in situations where small field sizes are used. Our results show that for such situations, the mean density model of the lung is inadequate, and a more accurate CT model of the lung is required. Future work with our model will involve patient motion, setup errors, and recommendations for the resolution of CT models.

Comparison of dose-volume histograms of IMRT treatment plans for ethmoid sinus cancer computed by advanced treatment planning systems including Monte Carlo

BACKGROUND AND PURPOSE

To recompute clinical intensity-modulated treatment plans for ethmoid sinus cancer and to compare quantitatively the dose-volume histograms (DVHs) of the planning target volume (PTV) and the optic organs at risk.

MATERIAL AND METHODS

Ten step-and-shoot intensity-modulated treatment plans were enrolled in this study. Large natural and surgical air cavities challenged the calculation systems. Each optimized treatment plan was recalculated by two superposition convolution (TMS and Pinnacle) and a Monte Carlo system (MCDE). To compare the resulting DVHs, a one-way ANOVA for repeated measurements was performed and multiple pairwise comparisons were made.

RESULTS

The tails of the PTV-DVHs were significantly higher for the Monte Carlo system. The DVHs of the critical organs displayed some statistically but not always clinically significant differences. For the individual patients, the three planning systems sometimes reproduced clinically discrepant DVHs that were not significantly different when averaged over all patients.

CONCLUSIONS

Dose to air cavities contains computational uncertainty. As this dose is clinically irrelevant and optimizing it is meaningless, we recommended extracting the air from the PTV when constructing the PTV-DVH. The planning systems considered reproduce DVHs that are significantly different, especially in the tail region of PTV-DVHs.

Accuracy of patient dose calculation for lung IMRT: A comparison of Monte Carlo, convolution/superposition, and pencil beam computations

The accuracy of dose computation within the lungs depends strongly on the performance of the calculation algorithm in regions of electronic disequilibrium that arise near tissue inhomogeneities with large density variations. There is a lack of data evaluating the performance of highly developed analytical dose calculation algorithms compared to Monte Carlo computations in a clinical setting. We compared full Monte Carlo calculations (performed by our Monte Carlo dose engine MCDE) with two different commercial convolution/superposition (CS) implementations (Pinnacle-CS and Helax-TMS's collapsed cone model Helax-CC) and one pencil beam algorithm (Helax-TMS's pencil beam model Helax-PB) for 10 intensity modulated radiation therapy (IMRT) lung cancer patients. Treatment plans were created for two photon beam qualities (6 and 18 MV). For each dose calculation algorithm, patient, and beam quality, the following set of clinically relevant dose-volume values was reported: (i) minimal, median, and maximal dose (Dmin, D50, and Dmax) for the gross tumor and planning target volumes (GTV and PTV); (ii) the volume of the lungs (excluding the GTV) receiving at least 20 and 30 Gy (V20 and V30) and the mean lung dose; (iii) the 33rd percentile dose (D33) and Dmax delivered to the heart and the expanded esophagus; and (iv) Dmax for the expanded spinal cord. Statistical analysis was performed by means of one-way analysis of variance for repeated measurements and Tukey pairwise comparison of means. Pinnacle-CS showed an excellent agreement with MCDE within the target structures, whereas the best correspondence for the organs at risk (OARs) was found between Helax-CC and MCDE. Results from Helax-PB were unsatisfying for both targets and OARs. Additionally, individual patient results were analyzed. Within the target structures, deviations above 5% were found in one patient for the comparison of MCDE and Helax-CC, while all differences between MCDE and Pinnacle-CS were below 5%. For both Pinnacle-CS and Helax-CC, deviations from MCDE above 5% were found within the OARs: within the lungs for two (6 MV) and six (18 MV) patients for Pinnacle-CS, and within other OARs for two patients for Helax-CC (for Dmax of the heart and D33 of the expanded esophagus) but only for 6 MV. For one patient, all four algorithms were used to recompute the dose after replacing all computed tomography voxels within the patient's skin contour by water. This made all differences above 5% between MCDE and the other dose calculation algorithms disappear. Thus, the observed deviations mainly arose from differences in particle transport modeling within the lungs, and the commissioning of the algorithms was adequately performed (or the commissioning was less important for this type of treatment). In conclusion, not one pair of the dose calculation algorithms we investigated could provide results that were consistent within 5% for all 10 patients for the set of clinically relevant dose-volume indices studied. As the results from both CS algorithms differed significantly, care should be taken when evaluating treatment plans as the choice of dose calculation algorithm may influence clinical results. Full Monte Carlo provides a great benchmarking tool for evaluating the performance of other algorithms for patient dose computations.