Existing volumetric modulated arc therapy (VMAT) optimization using coplanar arcs is highly efficient but usually dosimetrically inferior to intensity modulated radiation therapy (IMRT) with optimized non-coplanar beams. To achieve both dosimetric quality and delivery efficiency, we proposed in this study, a novel integrated optimization method for non-coplanar VMAT (4πVMAT). 4πVMAT with direct aperture optimization (DAO) was achieved by utilizing a least square dose fidelity objective, along with an anisotropic total variation term for regularizing the fluence smoothness, a single segment term for imposing simple apertures, and a group sparsity term for selecting beam angles. Continuous gantry/couch angle trajectories were selected using the Dijkstra's algorithm, where the edge and node costs were determined based on the maximal gantry rotation speed and the estimated fluence map at the current iteration, respectively. The couch-gantry-patient collision space was calculated based on actual machine geometry and a human subject 3D surface. Beams leading to collision are excluded from the DAO and beam trajectory selection (BTS). An alternating optimization strategy was implemented to solve the integrated DAO and BTS problem. The feasibility of 4πVMAT using one full-arc or two full-arcs was tested on nine patients with brain, lung, or prostate cancer. The plan was compared against a coplanar VMAT (2πVMAT) plan using one additional arc and collimator rotation. Compared to 2πVMAT, 4πVMAT reduced the average maximum and mean organs-at-risk dose by 9.63% and 3.08% of the prescription dose with the same target coverage. R50 was reduced by 23.0%. Maximum doses to the dose limiting organs, such as the brainstem, the major vessels, and the proximal bronchus, were reduced by 8.1 Gy (64.8%), 16.3 Gy (41.5%), and 19.83 Gy (55.5%), respectively. The novel 4πVMAT approach affords efficient delivery of non-coplanar arc trajectories that lead to dosimetric improvements compared with coplanar VMAT using more arcs.
Purpose Dose conformality and robustness are equally important in intensity modulated proton therapy (IMPT). Despite the obvious implication of beam orientation on both dosimetry and robustness, an automated, robust beam orientation optimization algorithm has not been incorporated due to the problem complexity and paramount computational challenge. In this study, we developed a novel IMPT framework that integrates robust beam orientation optimization (BOO) and robust fluence map optimization (FMO) in a unified framework. Methods The unified framework is formulated to include a dose fidelity term, a heterogeneity‐weighted group sparsity term, and a sensitivity regularization term. The L2, 1/2‐norm group sparsity is used to reduce the number of active beams from the initial 1162 evenly distributed noncoplanar candidate beams, to between two and four. A heterogeneity index, which evaluates the lateral tissue heterogeneity of a beam, is used to weigh the group sparsity term. With this index, beams more resilient to setup uncertainties are encouraged. There is a symbiotic relationship between the heterogeneity index and the sensitivity regularization; the integrated optimization framework further improves beam robustness against both range and setup uncertainties. This Sensitivity regularization and Heterogeneity weighting based BOO and FMO framework (SHBOO‐FMO) was tested on two skull‐base tumor (SBT) patients and two bilateral head‐and‐neck (H&N) patients. The conventional CTV‐based optimized plans (Conv) with SHBOO‐FMO beams (SHBOO‐Conv) and manual beams (MAN‐Conv) were compared to investigate the beam robustness of the proposed method. The dosimetry and robustness of SHBOO‐FMO plan were compared against the manual beam plan with CTV‐based voxel‐wise worst‐case scenario approach (MAN‐WC). Results With SHBOO‐FMO method, the beams with superior range robustness over manual beams were selected while the setup robustness was maintained or improved. On average, the lowest [D95%, V95%, V100%] of CTV were increased from [93.85%, 91.06%, 70.64%] in MAN‐Conv plans, to [98.62%, 98.61%, 96.17%] in SHBOO‐Conv plans with range uncertainties. With setup uncertainties, the average lowest [D98%, D95%, V95%, V100%] of CTV were increased from [92.06%, 94.83%, 94.31%, 78.93%] in MAN‐Conv plans, to [93.54%, 96.61%, 97.01%, 91.98%] in SHBOO‐Conv plans. Compared with the MAN‐WC plans, the final SHBOO‐FMO plans achieved comparable plan robustness and better OAR sparing, with an average reduction of [Dmean, Dmax] of [6.31, 6.55] GyRBE for the SBT cases and [1.89, 5.08] GyRBE for the H&N cases from the MAN‐WC plans. Conclusion We developed a novel method to integrate robust BOO and robust FMO into IMPT optimization for a unified solution of both BOO and FMO, generating plans with superior dosimetry and good robustness.
Purpose:The accuracy of dose prediction is essential for knowledge-based planning and automated planning techniques. We compare the dose prediction accuracy of 3 prediction methods including statistical voxel dose learning, spectral regression, and support vector regression based on limited patient training data.Methods:Statistical voxel dose learning, spectral regression, and support vector regression were used to predict the dose of noncoplanar intensity-modulated radiation therapy (4π) and volumetric-modulated arc therapy head and neck, 4π lung, and volumetric-modulated arc therapy prostate plans. Twenty cases of each site were used for k-fold cross-validation, with k = 4. Statistical voxel dose learning bins voxels according to their Euclidean distance to the planning target volume and uses the median to predict the dose of new voxels. Distance to the planning target volume, polynomial combinations of the distance components, planning target volume, and organ at risk volume were used as features for spectral regression and support vector regression. A total of 28 features were included. Principal component analysis was performed on the input features to test the effect of dimension reduction. For the coplanar volumetric-modulated arc therapy plans, separate models were trained for voxels within the same axial slice as planning target volume voxels and voxels outside the primary beam. The effect of training separate models for each organ at risk compared to all voxels collectively was also tested. The mean squared error was calculated to evaluate the voxel dose prediction accuracy.Results:Statistical voxel dose learning using separate models for each organ at risk had the lowest root mean squared error for all sites and modalities: 3.91 Gy (head and neck 4π), 3.21 Gy (head and neck volumetric-modulated arc therapy), 2.49 Gy (lung 4π), and 2.35 Gy (prostate volumetric-modulated arc therapy). Compared to using the original features, principal component analysis reduced the 4π prediction error for head and neck spectral regression (−43.9%) and support vector regression (−42.8%) and lung support vector regression (−24.4%) predictions. Principal component analysis was more effective in using all/most of the possible principal components. Separate organ at risk models were more accurate than training on all organ at risk voxels in all cases.Conclusion:Compared with more sophisticated parametric machine learning methods with dimension reduction, statistical voxel dose learning is more robust to patient variability and provides the most accurate dose prediction method.
Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation quality and speed, creating an unmet need for the acceleration of Monte Carlo (MC) dose calculation. Existing deep learning approaches to denoise the final plan MC dose fail to achieve the accuracy and speed requirements of large-scale beamlet dose calculation in the presence of a strong magnetic field for online adaptive radiotherapy planning. Our deep learning dose calculation method, DeepMC, addresses these needs by predicting low-noise dose from extremely noisy (but fast) MC-simulated dose and anatomical inputs, thus enabling significant acceleration. DeepMC simultaneously reduces MC sampling noise and predicts corrupted dose buildup at tissue-air material interfaces resulting from MR-field induced electron return effects. Here we demonstrate our model’s ability to accelerate dose calculation for daily treatment planning by a factor of 38 over traditional low-noise MC simulation with clinically meaningful accuracy in deliverable dose and treatment delivery parameters. As a post-processing approach, DeepMC provides compounded acceleration of large-scale dose calculation when used alongside established MC acceleration techniques in variance reduction and graphics processing unit-based MC simulation.
Purpose A dose‐modulation device for small animal radiotherapy is required to use clinically analogous treatment techniques, which will likely increase the translatability of preclinical research results. Because the clinically used multileaf collimator (MLC) is impractical for miniaturization, we have developed a simpler, better‐suited sparse orthogonal collimator (SOC) for delivering small animal intensity‐modulated radiation therapy (IMRT) using a rectangular aperture optimization (RAO) treatment planning system. Methods The SOC system was modeled in computer‐aided design software and fabricated with machined tungsten leaves and three‐dimensional (3D) printed leaf housing. A graphical user interface was developed for controlling and calibrating the SOC leaves, which are driven by Arduino‐controlled stepper motors. A Winston‐Lutz test was performed to assess mechanical alignment, and abutting field and grid dose patterns were created to analyze intra‐ and intercalibration leaf positioning error. Leaf transmission and penumbra were measured over the full range of gantry angles and leaf positions, respectively. Three SOC test plans were delivered, and film measurements were compared to the intended dose distributions. The differences in maximum, mean, and minimum, as well as pixelwise absolute dose differences, were compared for each structure, and a gamma analysis was performed for the target structures using criteria of 4% dose difference and 0.3 mm distance to agreement. Results The Winston‐Lutz test revealed maximum directional offsets between the SOC and primary collimator axes of 0.53 mm at 0° and 0.68 mm over the full 360°. Upper and lower abutting field patterns had maximum dose deviations of 18.8 ± 3.1% and 15.5 ± 2.9%, respectively, and grid patterns showed intra‐ and intercalibration repeatability of 93% and 91%, respectively. Extremely low midleaf (0.15 ± 0.05%) and interleaf (0.27 ± 0.22%) transmission was measured, with no significant rotational variation. The average penumbra was ~0.8 mm for all leaves at field center, with a range of 0.17 mm for all leaf positions. A highly concave test plan was delivered with a ~ 95% gamma analysis pass rate, and a realistic mouse phantom liver irradiation plan achieved a pass rate of ~98%. A highly complex dose distribution was also created with 551 SOC apertures averaging 2.4 mm in size. Conclusions A sparse orthogonal collimator was developed and commissioned, with promising preliminary dosimetry results. The SOC design, with its limited moving components and high dose‐modulation resolution, is ideal for delivering high‐quality small animal IMRT with our RAO‐based treatment planning system.
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