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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Multidisciplinary concurrent optimization framework for multi-phase building design process

Modern day building design projects require multidisciplinary expertise from architects and engineers across various phases of the design (conceptual, preliminary, and detailed) and construction processes. The Architecture Engineering and Construction (AEC) community has recently shifted gears toward leveraging design optimization techniques to make well-informed decisions in the design of buildings. However, most of the building design optimization efforts are either multidisciplinary optimization confined to just a specific design phase (conceptual/preliminary/detailed) or single disciplinary optimization (structural/thermal/daylighting/energy) spanning across multiple phases. Complexity in changing the optimization setup as the design progresses through subsequent phases, interoperability issues between modeling and physics-based analysis tools used at later stages, and the lack of an appropriate level of design detail to get meaningful results from these sophisticated analysis tools are few challenges that limit multi-phase multidisciplinary design optimization (MDO) in the AEC field. Here this paper proposes a computational building design platform leveraging concurrent engineering techniques such as interactive problem structuring, simulation-based optimization using meta models for energy and daylighting (machine learning based) and tradespace visualization. The proposed multi-phase concurrent MDO framework is demonstrated by using it to design and optimize a sample office building for energy and daylighting objectives across multiple phases. Furthermore, limitations of the proposed framework and future avenues of research are listed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Optimizations of spin-exchange relaxation-free magnetometer based on potassium and rubidium hybrid optical pumping

The hybrid optical pumping atomic magnetometers have not realized its theoretical sensitivity, the optimization is critical for optimal performance. The optimizations proposed in this paper are suitable for hybrid optical pumping atomic magnetometer, which contains two alkali species. To optimize the parameters, the dynamic equations of spin evolution with two alkali species were solved, whose steady-state solution is used to optimize the parameters. The demand of the power of the pump beam is large for hybrid optical pumping. Moreover, the sensitivity of the hybrid optical pumping magnetometer increases with the increase of the power density of the pump beam. The density ratio between the two alkali species is especially important for hybrid optical pumping magnetometer. A simple expression for optimizing the density ratio is proposed in this paper, which can help to determine the mole faction of the alkali atoms in fabricating the hybrid cell before the cell is sealed. The spin-exchange rate between the two alkali species is proportional to the saturated density of the alkali vapor, which is highly dependent on the temperature of the cell. Consequently, the sensitivity of the hybrid optical pumping magnetometer is dependent on the temperature of the cell. We proposed the thermal optimization of the hybrid cell for a hybrid optical pumping magnetometer, which can improve the sensitivity especially when the power of the pump beam is low. With these optimizations, a sensitivity of approximately 5 fT/Hz{sup 1/2} is achieved with gradiometer arrangement.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Physics successfully implements Lagrange multiplier optimization

Optimization is a major part of human effort. While being mathematical, optimization is also built into physics. For example, physics has the Principle of Least Action; the Principle of Minimum Power Dissipation, also called Minimum Entropy Generation; and the Variational Principle. Physics also has Physical Annealing, which, of course, preceded computational Simulated Annealing. Physics has the Adiabatic Principle, which, in its quantum form, is called Quantum Annealing. Thus, physical machines can solve the mathematical problem of optimization, including constraints. Binary constraints can be built into the physical optimization. In that case, the machines are digital in the same sense that a flip–flop is digital. A wide variety of machines have had recent success at optimizing the Ising magnetic energy. We demonstrate in this paper that almost all those machines perform optimization according to the Principle of Minimum Power Dissipation as put forth by Onsager. Further, we show that this optimization is in fact equivalent to Lagrange multiplier optimization for constrained problems. We find that the physical gain coefficients that drive those systems actually play the role of the corresponding Lagrange multipliers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Optimized finite-build stellarator coils using automatic differentiation

A new stellarator coil design code is introduced that optimizes the position and winding pack orientation of finite-build coils. The new code, called flexible optimized curves in space using automatic differentiation (AD) and finite build (FOCUSADD), performs gradient-based optimization in a high-dimensional, non-convex space. The derivatives with respect to parameters of finite-build coils are easily and efficiently computed using AD. FOCUSADD parametrizes coil positions in free space using a Fourier series and uses a multi-filament approximation to the coil winding pack. The orientation of the winding pack is parametrized with a Fourier series and can be optimized as well. Optimized finite-build coils for a Wendelstein 7-X (W7-X)-like stellarator are found, and compared with filamentary coil results. The final positions of optimized finite-build W7-X-like coils are shifted, on average, by approximately 2.5 mm relative to optimized filamentary coils. These results suggest that finite-build effects should be accounted for in the optimization of stellarators with low coil tolerances.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Classical combinatorial optimization scaling for random Ising models on 2D heavy-hex graphs

Motivated by near term quantum computing hardware limitations, combinatorial optimization problems that can be addressed by current quantum algorithms and noisy hardware with little or no overhead are used to probe capabilities of quantum algorithms such as the quantum approximate optimization algorithm. In this study, a specific class of near term quantum computing hardware defined combinatorial optimization problems, Ising models on heavy-hex graphs both with and without geometrically local cubic terms, are examined for their classical computational hardness via empirical computation time scaling quantification. Specifically the time-to-solution (TTS) metric using the classical heuristic simulated annealing is measured for finding optimal variable assignments (ground states), as well as the time required for the optimization software Gurobi to find an optimal variable assignment. Because of the sparsity of these Ising models, the classical algorithms are able to find optimal solutions efficiently even for large instances (i.e. 100 000 spin variables). The Ising models both with and without geometrically local cubic terms exhibit average-case linear-time or weakly quadratic scaling when solved exactly using Gurobi, and the Ising models with no cubic terms show evidence of exponential-time TTS scaling when sampled using simulated annealing. These findings point to the necessity of developing and testing more complex, namely more densely connected, optimization problems in order for quantum computing to ever have a practical advantage over classical computing. Our results are another illustration that different classical algorithms can indeed have exponentially different running times, thus making the identification of the best practical classical technique important in any quantum computing vs. classical computing comparison.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

An agent-based blackboard system for multi-objective optimization

In the field of multi-objective optimization, there are a multitude of algorithms from which to choose. Each algorithm has strengths and weaknesses associated with the mechanics for finding the Pareto front. Recently, researchers have begun to examine how multi-agent environments can be used to help solve multi-objective optimization problems. In this work, we propose a multi-objective optimization algorithm based on a multi-agent blackboard system (MABS). The MABS framework allows for multiple agents to read and write pertinent optimization problem data to a central blackboard agent. Agents can stochastically search the design space, use previously discovered solutions to explore local optima, or update and prune the Pareto front. A centralized blackboard framework allows the optimization problem to be solved in a cohesive manner and permits stopping, restarting, or updating the optimization problem. The MABS framework is tested against three alternative optimization algorithms across a suite of engineering design problems and typically outperforms the other algorithms in discovering the Pareto front. A parallelizability study is performed where we find that the MABS is able to evaluate a set number of designs, which require an evaluation time ranging from 0 to 300 seconds, quicker than a traditional optimization algorithm: this fact becomes more apparent the longer it takes to evaluate a design. To provide context for the benefits provided by MABS, a real-world nuclear engineering design problem is examined. MABS is used to examine the placement of experiments in a nuclear reactor, where we are able to evaluate hundreds of configurations for experimental placement while maintaining a strict set of safety constraints.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Multi-solution nature of topology optimization and its application in design for additive manufacturing

The purpose of this paper is to introduce the multi-solution nature of topology optimization (TO) as a design tool for additive manufacturing (AM). The sensitivity of topologically optimized parts and manufacturing constraints to the initial starting point of the optimization process leading to structures with equivalent performance is explored. A modified bi-directional evolutionary structural optimization (BESO) code was used as the numerical approach to optimize a cantilever beam problem and reduce the mass by 50 per cent. Several optimized structures with relatively equivalent mechanical performance were generated by changing the initial starting point of the TO algorithm. These optimized structures were manufactured using fused deposition modeling (FDM). The equivalence of strain distribution in FDM parts was tested with the digital image correlation (DIC) technique and compared with that from the modified BESO code.The results confirm that TO could lead to a wide variety of non-unique solutions based on loading and manufacturability constraints. The modified BESO code was able to reduce the support structure needed to build the simple two-dimensional cantilever beam by 15 per cent while keeping the mechanical performance at the same level. Finally, the originality of this paper lies in introduction and application of the multi-solution nature of TO for AM as a design tool for optimizing structures with minimized features in the overhang condition and the need for support structures.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Optimization of the x-ray monitoring angle for creating a correlation model between internal and external respiratory signals

Purpose: To perform dynamic tumor tracking irradiation with the Vero4DRT (MHI-TM2000), a correlation model [four dimensional (4D) model] between the displacement of infrared markers on the abdominal wall and the three-dimensional position of a tumor indicated by a minimum of three implanted gold markers is required. However, the gold markers cannot be detected successfully on fluoroscopic images under the following situations: (1) overlapping of the gold markers; and (2) a low intensity ratio of the gold marker to its surroundings. In the present study, the authors proposed a method to readily determine the optimal x-ray monitoring angle for creating a 4D model utilizing computed tomography (CT) images. Methods: The Vero4DRT mounting two orthogonal kV x-ray imaging subsystems can separately rotate the gantry along an O-shaped guide-lane and the O-ring along its vertical axis. The optimal x-ray monitoring angle was determined on CT images by minimizing the root-sum-square of water equivalent path lengths (WEPLs) on the orthogonal lines passing all of the gold markers while rotating the O-ring and the gantry. The x-ray monitoring angles at which the distances between the gold markers were within 5 mm at the isocenter level were excluded to prevent false detection of the gold markers in consideration of respiratory motions. First, the relationship between the WEPLs (unit: mm) and the intensity ratios of the gold markers was examined to assess the validity of our proposed method. Second, our proposed method was applied to the 4D-CT images at the end-expiration phase for 11 lung cancer patients who had four to five gold markers. To prove the necessity of the x-ray monitoring angle optimization, the intensity ratios of the least visible markers (minimum intensity ratios) that were estimated from the WEPLs were compared under the following conditions: the optimal x-ray monitoring angle and the angles used for setup verification. Additionally, the intra- and interfractional variations in the intensity ratio were examined from the optimal x-ray monitoring angle. Results: A negative strong correlation was observed between the WEPL (x) and the intensity ratio (y) (y= 6.57 exp[-0.0125x]+ 1, R=-0.88 [95% confidence interval: -0.85 to -0.90], p < 0.01). Our proposed method effectively avoided having the x-ray beam pass through high-density structures, although there were large interpatient variations in the optimal x-ray monitoring angle because of the geometric arrangement between the gold markers and the anatomical structures. The minimum intensity ratios that were estimated from the WEPLs at the optimal x-ray monitoring angle ranged from 1.43 to 2.48, which was an average of 1.27 times (range, 1.02-1.66) higher than the angles used for setup verification. The maximum intra- and interfractional decreases in the intensity ratio were 0.23 and 0.17, respectively. Conclusions: The authors demonstrated that the optimal x-ray monitoring angle for creating a 4D model can improve the visibility of gold markers.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Expected treatment dose construction and adaptive inverse planning optimization: Implementation for offline head and neck cancer adaptive radiotherapy

Purpose: To construct expected treatment dose for adaptive inverse planning optimization, and evaluate it on head and neck (h and n) cancer adaptive treatment modification. Methods: Adaptive inverse planning engine was developed and integrated in our in-house adaptive treatment control system. The adaptive inverse planning engine includes an expected treatment dose constructed using the daily cone beam (CB) CT images in its objective and constrains. Feasibility of the adaptive inverse planning optimization was evaluated retrospectively using daily CBCT images obtained from the image guided IMRT treatment of 19 h and n cancer patients. Adaptive treatment modification strategies with respect to the time and the number of adaptive inverse planning optimization during the treatment course were evaluated using the cumulative treatment dose in organs of interest constructed using all daily CBCT images. Results: Expected treatment dose was constructed to include both the delivered dose, to date, and the estimated dose for the remaining treatment during the adaptive treatment course. It was used in treatment evaluation, as well as in constructing the objective and constraints for adaptive inverse planning optimization. The optimization engine is feasible to perform planning optimization based on preassigned treatment modification schedule. Compared to the conventional IMRT, the adaptive treatment for h and n cancer illustrated clear dose-volume improvement for all critical normal organs. The dose-volume reductions of right and left parotid glands, spine cord, brain stem and mandible were (17 {+-} 6)%, (14 {+-} 6)%, (11 {+-} 6)%, (12 {+-} 8)%, and (5 {+-} 3)% respectively with the single adaptive modification performed after the second treatment week; (24 {+-} 6)%, (22 {+-} 8)%, (21 {+-} 5)%, (19 {+-} 8)%, and (10 {+-} 6)% with three weekly modifications; and (28 {+-} 5)%, (25 {+-} 9)%, (26 {+-} 5)%, (24 {+-} 8)%, and (15 {+-} 9)% with five weekly modifications. Conclusions: Adaptive treatment modification can be implemented including the expected treatment dose in the adaptive inverse planning optimization. The retrospective evaluation results demonstrate that utilizing the weekly adaptive inverse planning optimization, the dose distribution of h and n cancer treatment can be largely improved.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-T-21: A Novel Sampling Algorithm to Reduce Intensity-Modulated Radiation Therapy (IMRT) Optimization Time

Purpose: The IMRT optimization problem requires substantial computer time to find optimal dose distributions because of the large number of variables and constraints. Voxel sampling reduces the number of constraints and accelerates the optimization process, but usually deteriorates the quality of the dose distributions to the organs. We propose a novel sampling algorithm that accelerates the IMRT optimization process without significantly deteriorating the quality of the dose distribution. Methods: We included all boundary voxels, as well as a sampled fraction of interior voxels of organs in the optimization. We selected a fraction of interior voxels using a clustering algorithm, that creates clusters of voxels that have similar influence matrix signatures. A few voxels are selected from each cluster based on the pre-set sampling rate. Results: We ran sampling and no-sampling IMRT plans for de-identified head and neck treatment plans. Testing with the different sampling rates, we found that including 10% of inner voxels produced the good dose distributions. For this optimal sampling rate, the algorithm accelerated IMRT optimization by a factor of 2–3 times with a negligible loss of accuracy that was, on average, 0.3% for common dosimetric planning criteria. Conclusion: We demonstrated that a sampling could be developed that reduces optimization time by more than a factor of 2, without significantly degrading the dose quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-T-395: Multi-GPU-Based VMAT Treatment Plan Optimization Using a Column-Generation Approach

Purpose: GPU has been employed to speed up VMAT optimizations from hours to minutes. However, its limited memory capacity makes it difficult to handle cases with a huge dose-deposition-coefficient (DDC) matrix, e.g. those with a large target size, multiple arcs, small beam angle intervals and/or small beamlet size. We propose multi-GPU-based VMAT optimization to solve this memory issue to make GPU-based VMAT more practical for clinical use. Methods: Our column-generation-based method generates apertures sequentially by iteratively searching for an optimal feasible aperture (referred as pricing problem, PP) and optimizing aperture intensities (referred as master problem, MP). The PP requires access to the large DDC matrix, which is implemented on a multi-GPU system. Each GPU stores a DDC sub-matrix corresponding to one fraction of beam angles and is only responsible for calculation related to those angles. Broadcast and parallel reduction schemes are adopted for inter-GPU data transfer. MP is a relatively small-scale problem and is implemented on one GPU. One headand- neck cancer case was used for test. Three different strategies for VMAT optimization on single GPU were also implemented for comparison: (S1) truncating DDC matrix to ignore its small value entries for optimization; (S2) transferring DDC matrix part by part to GPU during optimizations whenever needed; (S3) moving DDC matrix related calculation onto CPU. Results: Our multi-GPU-based implementation reaches a good plan within 1 minute. Although S1 was 10 seconds faster than our method, the obtained plan quality is worse. Both S2 and S3 handle the full DDC matrix and hence yield the same plan as in our method. However, the computation time is longer, namely 4 minutes and 30 minutes, respectively. Conclusion: Our multi-GPU-based VMAT optimization can effectively solve the limited memory issue with good plan quality and high efficiency, making GPUbased ultra-fast VMAT planning practical for real clinical use.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-F-BRD-08: Guaranteed Epsilon-Optimal Treatment Plans with Minimum Number of Beams for SBRT Using RayStation

Purpose: To efficiently find quality-guaranteed treatment plans with the minimum number of beams for stereotactic body radiation therapy using RayStation. Methods: For a pre-specified pool of candidate beams we use RayStation (a treatment planning software for clinical use) to identify the deliverable plan which uses all the beams with the minimum dose to organs at risk (OARs) and dose to the tumor and other structures in specified ranges. Then use the dose matrix information for the generated apertures from RayStation to solve a linear program to find the ideal plan with the same objective and constraints allowing use of all beams. Finally we solve a mixed integer programming formulation of the beam angle optimization problem (BAO) with the objective of minimizing the number of beams while remaining in a predetermined epsilon-optimality of the ideal plan with respect to the dose to OARs. Since the treatment plan optimization is a multicriteria optimization problem, the planner can exploit the multicriteria optimization capability of RayStation to navigate the ideal dose distribution Pareto surface and select a plan of desired target coverage versus OARs sparing, and then use the proposed technique to reduce the number of beams while guaranteeing quality. For the numerical experiments two liver cases and one lung case with 33 non-coplanar beams are considered. Results: The ideal plan uses an impractically large number of beams. The proposed technique reduces the number of beams to the range of practical application (5 to 9 beams) while remaining in the epsilon-optimal range of 1% to 5% optimality gap. Conclusion: The proposed method can be integrated into a general algorithm for fast navigation of the ideal dose distribution Pareto surface and finding the treatment plan with the minimum number of beams, which corresponds to the delivery time, in epsilon-optimality range of the desired ideal plan. The project was supported by the Federal Share of program income earned by Massachusetts General Hospital on C06 CA059267, Proton Therapy Research and Treatment Center and partially by RaySearch Laboratories.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-C-204-02: Improved Patient-Specific Optimization of the Stopping Power Calibration for Proton Therapy Planning Using a Single Proton Radiography

Purpose: We present an improved method to calculate patient-specific calibration curves to convert X-ray computed tomography (CT) Hounsfield Unit (HU) to relative stopping powers (RSP) for proton therapy treatment planning. Methods: By optimizing the HU-RSP calibration curve, the difference between a proton radiographic image and a digitally reconstructed X-ray radiography (DRR) is minimized. The feasibility of this approach has previously been demonstrated. This scenario assumes that all discrepancies between proton radiography and DRR originate from uncertainties in the HU-RSP curve. In reality, external factors cause imperfections in the proton radiography, such as misalignment compared to the DRR and unfaithful representation of geometric structures (“blurring”). We analyze these effects based on synthetic datasets of anthropomorphic phantoms and suggest an extended optimization scheme which explicitly accounts for these effects. Performance of the method is been tested for various simulated irradiation parameters. The ultimate purpose of the optimization is to minimize uncertainties in the HU-RSP calibration curve. We therefore suggest and perform a thorough statistical treatment to quantify the accuracy of the optimized HU-RSP curve. Results: We demonstrate that without extending the optimization scheme, spatial blurring (equivalent to FWHM=3mm convolution) in the proton radiographies can cause up to 10% deviation between the optimized and the ground truth HU-RSP calibration curve. Instead, results obtained with our extended method reach 1% or better correspondence. We have further calculated gamma index maps for different acceptance levels. With DTA=0.5mm and RD=0.5%, a passing ratio of 100% is obtained with the extended method, while an optimization neglecting effects of spatial blurring only reach ∼90%. Conclusion: Our contribution underlines the potential of a single proton radiography to generate a patient-specific calibration curve and to improve dose delivery by optimizing the HU-RSP calibration curve as long as all sources of systematic incongruence are properly modeled.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-J-130: Automating Liver Segmentation Via Combined Global and Local Optimization

Purpose: The aim of this work is to develop a robust algorithm for accurate segmentation of liver with special attention paid to the problems with fuzzy edges and tumor. Methods: 200 CT images were collected from radiotherapy treatment planning system. 150 datasets are selected as the panel data for shape dictionary and parameters estimation. The remaining 50 datasets were used as test images. In our study liver segmentation was formulated as optimization process of implicit function. The liver region was optimized via local and global optimization during iterations. Our method consists five steps: 1)The livers from the panel data were segmented manually by physicians, and then We estimated the parameters of GMM (Gaussian mixture model) and MRF (Markov random field). Shape dictionary was built by utilizing the 3D liver shapes. 2)The outlines of chest and abdomen were located according to rib structure in the input images, and the liver region was initialized based on GMM. 3)The liver shape for each 2D slice was adjusted using MRF within the neighborhood of liver edge for local optimization. 4)The 3D liver shape was corrected by employing SSR (sparse shape representation) based on liver shape dictionary for global optimization. Furthermore, H-PSO(Hybrid Particle Swarm Optimization) was employed to solve the SSR equation. 5)The corrected 3D liver was divided into 2D slices as input data of the third step. The iteration was repeated within the local optimization and global optimization until it satisfied the suspension conditions (maximum iterations and changing rate). Results: The experiments indicated that our method performed well even for the CT images with fuzzy edge and tumors. Comparing with physician delineated results, the segmentation accuracy with the 50 test datasets (VOE, volume overlap percentage) was on average 91%–95%. Conclusion: The proposed automatic segmentation method provides a sensible technique for segmentation of CT images. This work is supported by NIH/NIBIB (1R01-EB016777), National Natural Science Foundation of China (No.61471226 and No.61201441), Research funding from Shandong Province (No.BS2012DX038 and No.J12LN23), and Research funding from Jinan City (No.201401221 and No.20120109)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-175: Clinical Evaluations of Monte Carlo-Based Inverse Treatment Plan Optimization for Intensity Modulated Radiotherapy

Purpose: Pencil-beam or superposition-convolution type dose calculation algorithms are routinely used in inverse plan optimization for intensity modulated radiation therapy (IMRT). However, due to their limited accuracy in some challenging cases, e.g. lung, the resulting dose may lose its optimality after being recomputed using an accurate algorithm, e.g. Monte Carlo (MC). It is the objective of this study to evaluate the feasibility and advantages of a new method to include MC in the treatment planning process. Methods: We developed a scheme to iteratively perform MC-based beamlet dose calculations and plan optimization. In the MC stage, a GPU-based dose engine was used and the particle number sampled from a beamlet was proportional to its optimized fluence from the previous step. We tested this scheme in four lung cancer IMRT cases. For each case, the original plan dose, plan dose re-computed by MC, and dose optimized by our scheme were obtained. Clinically relevant dosimetric quantities in these three plans were compared. Results: Although the original plan achieved a satisfactory PDV dose coverage, after re-computing doses using MC method, it was found that the PTV D95% were reduced by 4.60%–6.67%. After re-optimizing these cases with our scheme, the PTV coverage was improved to the same level as in the original plan, while the critical OAR coverages were maintained to clinically acceptable levels. Regarding the computation time, it took on average 144 sec per case using only one GPU card, including both MC-based beamlet dose calculation and treatment plan optimization. Conclusion: The achieved dosimetric gains and high computational efficiency indicate the feasibility and advantages of the proposed MC-based IMRT optimization method. Comprehensive validations in more patient cases are in progress.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-368: Evaluating Dosimetric Outcome of Modulated Photon Radiotherapy (XMRT) Optimization for Head and Neck Patients

Purpose: The dosimetric outcome of optimized treatment plans obtained by modulating the photon beamlet energy and fluence on a small cohort of four Head and Neck (H and N) patients was investigated. This novel optimization technique is denoted XMRT for modulated photon radiotherapy. The dosimetric plans from XMRT for H and N treatment were compared to conventional, 6 MV intensity modulated radiotherapy (IMRT) optimization plans. Methods: An arrangement of two non-coplanar and five coplanar beams was used for all four H and N patients. Both XMRT and IMRT were subject to the same optimization algorithm, with XMRT optimization allowing both 6 and 18 MV beamlets while IMRT was restricted to 6 MV only. The optimization algorithm was based on a linear programming approach with partial-volume constraints implemented via the conditional value-at-risk method. H and N constraints were based off of those mentioned in the Radiation Therapy Oncology Group 1016 protocol. XMRT and IMRT solutions were assessed using metrics suggested by International Commission on Radiation Units and Measurements report 83. The Gurobi solver was used in conjunction with the CVX package to solve each optimization problem. Dose calculations and analysis were done in CERR using Monte Carlo dose calculation with VMC{sub ++}. Results: Both XMRT and IMRT solutions met all clinical criteria. Trade-offs were observed between improved dose uniformity to the primary target volume (PTV1) and increased dose to some of the surrounding healthy organs for XMRT compared to IMRT. On average, IMRT improved dose to the contralateral parotid gland and spinal cord while XMRT improved dose to the brainstem and mandible. Conclusion: Bi-energy XMRT optimization for H and N patients provides benefits in terms of improved dose uniformity to the primary target and reduced dose to some healthy structures, at the expense of increased dose to other healthy structures when compared with IMRT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-478: IMRT Delivery Parameter Dependence of Dose-Mass Optimization

Purpose: To compare DMH and DVH optimization sensitivity to changes in IMRT delivery parameters. Methods: Two lung and two head and neck (HN) cases were retrospectively optimized using DVH and DMH optimization. For both optimization approaches, changes to two parameters were studied: number of IMRT segments (5 and 10 per beam) and the minimum segment area (2 and 6 cm2). The number of beams, beam angles, and minimum MUs per segment were the same for both optimizations approaches for each patient. During optimization, doses to the organs at risk (OARs) were iteratively lowered until the standard deviation across the PTV was above ∼3.0%. For each patient DVH and DMH plans were normalized such that 95% of the PTV received the same dose. Plan quality was evaluated by dose indices (DIs), which represent the dose delivered to a certain anatomical structure volume. For the lung cases, DIs assessed included: 1% cord, 33% heart, both lungs 20% and 30%, and 50% esophagus. In the HN cases: 1% cord, 1% brainstem, left/right parotids 50%, 50% larynx, and 50% esophagus. Results: When increasing the number of segments, while keeping a small segment area (2cm2), the average percent change of all DIs for DVH/DMH optimizations for each patient were: −4.66/4.71, 3.21/3.46, −9.62/21.69 and −3.28/−7.62. For a large segment area (6cm2): −0.26/−1.46, −5.04/−1.92, −5.23/−2.19 and 4.12/19.63. Results from increasing segment area while keeping a small number of segments (5segments/beam) were: 1.41/7.90, 8.17/11.66, 0.09/33.58 and −4.83/−11.60 for each case. For large number of segments (10 segments/beam): 8.35/1.30, −0.91/5.77, 6.29/7.08 and 2.62/5.16. Conclusion: This preliminary study showed case dependent results. Changes in IMRT parameters did not show consistent DI changes for either optimization approach. A larger population of patients is warranted for such comparison.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-F-BRD-01: A Novel 4D Robust Optimization Mitigates Interplay Effect in Intensity-Modulated Proton Therapy for Lung Cancer

Purpose: To compare the impact of interplay effect on 3D and 4D robustly optimized intensity-modulated proton therapy (IMPT) plans to treat lung cancer. Methods: Two IMPT plans were created for 11 non-small-cell-lung-cancer cases with 6–14 mm spots. 3D robust optimization generated plans on average CTs with the internal gross tumor volume density overridden to deliver 66 CGyE in 33 fractions to the internal target volume (ITV). 4D robust optimization generated plans on 4D CTs with the delivery of prescribed dose to the clinical target volume (CTV). In 4D optimization, the CTV of individual 4D CT phases received non-uniform doses to achieve a uniform cumulative dose. Dose evaluation software was developed to model time-dependent spot delivery to incorporate interplay effect with randomized starting phases of each field per fraction. Patient anatomy voxels were mapped from phase to phase via deformable image registration to score doses. Indices from dose-volume histograms were used to compare target coverage, dose homogeneity, and normal-tissue sparing. DVH indices were compared using Wilcoxon test. Results: Given the presence of interplay effect, 4D robust optimization produced IMPT plans with better target coverage and homogeneity, but slightly worse normal tissue sparing compared to 3D robust optimization (unit: Gy) [D95% ITV: 63.5 vs 62.0 (p=0.014), D5% - D95% ITV: 6.2 vs 7.3 (p=0.37), D1% spinal cord: 29.0 vs 29.5 (p=0.52), Dmean total lung: 14.8 vs 14.5 (p=0.12), D33% esophagus: 33.6 vs 33.1 (p=0.28)]. The improvement of target coverage (D95%,4D – D95%,3D) was related to the ratio RMA3/(TVx10−4), with RMA and TV being respiratory motion amplitude (RMA) and tumor volume (TV), respectively. Peak benefit was observed at ratios between 2 and 10. This corresponds to 125 – 625 cm3 TV with 0.5-cm RMA. Conclusion: 4D optimization produced more interplay-effect-resistant plans compared to 3D optimization. It is most effective when respiratory motion is modest compared to TV. NIH/NCI K25CA168984; Eagles Cancer Research Career Development; The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research; Mayo ASU Seed Grant; The Kemper Marley Foundation.

60 APPLIED LIFE SCIENCES↗