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At least 883 records · Page 49

Global stellarator coil optimization with quadratic constraints and objectives

Most present stellarator designs are produced by costly two-stage optimization: the first for an optimized equilibrium, and the second for a coil design reproducing its magnetic configuration. Few proxies for coil complexity and forces exist at the equilibrium stage. Rapid initial state finding for both stages is a topic of active research. Most present convex coil optimization codes use the least square winding surface method by Merkel (NESCOIL), with recent improvements in conditioning, regularization, sparsity, and physics objectives. While elegant, the method is limited to modeling the norms of linear functions in coil current. We present QUADCOIL, a global coil optimization method that targets combinations of linear and quadratic functions of the current. It can directly constrain and/or minimize a wide range of physics objectives unavailable in NESCOIL and REGCOIL, including the Lorentz force, magnetic energy, curvature, field-current alignment, and the maximum density of a dipole array. QUADCOIL requires no initial guess and runs nearly $10$ 2 x faster than filament optimization. Integrating it in the equilibrium optimization stage can potentially exclude equilibria with difficult-to-design coils, without significantly increasing the computation time per iteration. QUADCOIL finds the exact, global minimum in a large parameter space when possible, and otherwise finds a well-performing approximate global minimum. It supports most regularization techniques developed for NESCOIL and REGCOIL. We demonstrate QUADCOIL’s effectiveness in coil topology control, minimizing non-convex penalties, and predicting filament coil complexity with three numerical examples.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Optimization of Medium Frequency Transformers with Practical Considerations

A method to design and optimize a Medium Frequency Transformer (MFT) based on commercially available components and semiconductor and converter constraints is presented. The optimization algorithm is used to redesign the transformer for a scaled-down electric vehicle (EV) fast charger using a three-level resonant circuit topology. The main consideration of this paper is the uncertainty caused by modeling assumptions in optimization algorithms. To reduce this uncertainty, space mapping is used to create an optimized design point. Lastly, a comparison of the designs found using the original optimization algorithm and the space mapping technique are compared and analyzed. For simplicity, only a single objective optimization routine is employed.

42 ENGINEERING↗

Optimal Energy Dispatch of Distributed PVs for the Next Generation of Distribution Management Systems

Advanced Distribution Management Systems (ADMS) are being widely adopted by electric utilities for managing and optimizing the operations of their distribution systems. Distributed photovoltaic (DPV) systems with smart inverters can be controlled to adjust active power and reactive power outputs, and they are envisioned to become a part of (centrally or distributed) controllable assets managed by the ADMS for optimizing grid operations. This paper proposes an optimal energy dispatch strategy controlling DPV systems for regulating distribution voltages and achieving conservation voltage reduction. A convex optimization model is proposed with the use of linearized power flow, and the gradient projection algorithm is used to solve the optimal active power and reactive power outputs of smart inverters. The proposed optimal energy dispatch is implemented using an open-source ADMS platform, and simulation results have demonstrated the effectiveness of the proposed approach on improving distribution grid operations.

42 ENGINEERING↗

Dual Representations and H ∞ -Optimal Control of Partial Differential Equations

We consider H ∞ -optimal state-feedback control of the class of linear Partial Differential Equations (PDEs) which admit a Partial Integral Equation (PIE) representation. While linear matrix inequalities are commonly used for optimal control of Ordinary Differential Equations (ODEs), the absence of a universal state-space representation and suitable dual form prevents such methods from being applied to optimal control of PDEs. Specifically, for ODEs, the controller synthesis problem is defined in state-space, and duality is used to resolve the bilinearity of that synthesis problem. Recently, the PIE representation was proposed as a universal state-space representation for linear PDE systems. In this paper, we show that any PDE system represented by a PIE admits a dual PIE with identical stability and I/O properties. This result allows us to reformulate the stabilizing and optimal state-feedback control problems as convex optimization over the cone of positive Partial Integral (PI) operators. Operator inversion formulae then allow us to construct feedback gains for the original PDE system. The results are verified through application to several canonical problems in optimal control of PDEs and indicate the resulting bounds on H ∞ norm are not conservative.

42 ENGINEERING↗

Online Optimization as a Feedback Controller: Stability and Tracking

This paper develops and analyzes feedback-based online optimization methods to regulate the output of a linear time-invariant (LTI) dynamical system to the optimal solution of a time-varying convex optimization problem. The design of the algorithm is based on continuous-time primal-dual dynamics, properly modified to incorporate feedback from the LTI dynamical system, applied to a proximal augmented Lagrangian function. The resultant closed-loop algorithm tracks the solution of the time-varying optimization problem without requiring knowledge of (time-varying) disturbances in the dynamical system. The analysis leverages integral quadratic constraints to provide linear matrix inequality (LMI) conditions that guarantee global exponential stability and bounded tracking error. Analytical results show that, under a sufficient time-scale separation between the dynamics of the LTI dynamical system and the algorithm, the LMI conditions can be always satisfied. The paper further proposes a modified algorithm that can track an approximate solution trajectory of the constrained optimization problem under less restrictive assumptions. As an illustrative example, the proposed algorithms are showcased for power transmission systems, to compress the time scales between secondary and tertiary control, and allow to simultaneously power re-balancing and tracking of DC optimal power flow points.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Cooperative Game Approach to Optimal Merging Sequence and On-Ramp Merging Control of Connected and Automated Vehicles

Vehicle merging is one of the main causes of reduced traffic efficiency, increased risk of collision, and fuel consumption. Connected and automated vehicles (CAVs) can improve traffic efficiency, increase safety, and reduce the negative environmental impacts through effective communication and control. Therefore, to improve the traffic efficiency and reduce the fuel consumption in on-ramp scenarios, this paper addresses the global and optimal coordination of the CAVs in a merging zone. In this work, a cooperative multi-player game-based optimization framework and an algorithm are presented to coordinate vehicles and achieve minimum values for the global pay-off conditions. Fuel consumption, passenger comfort, and travel time within the merging control zone were used as the pay-off conditions. After analyzing the characteristics of the merging control zone and selecting the appropriate control decision duration, multi-player games were decomposed into multiple two-player games. An optimal merging strategy was, thereby, derived from a pay-off matrix, and minimum payoffs were predicted for a number of different potential strategies. The optimal trajectory corresponding to the predicted minimum payoffs was then utilized as the control law to coordinate the vehicles merging. The proposed control scheme derives an optimal merging sequence and an optimal trajectory for each vehicle. The effectiveness of the proposed model is validated through simulation. Finally, the proposed controller is compared with two alternative methods to demonstrate its potential to reduce fuel consumption and travel time and to improve passenger comfort and traffic efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Optimal Control Strategy With Efficiency and Reliability Improvement for Offshore DC Microgrids

Offshore microgrids, due to their remote location and lack of external energy support, face significant challenges in wide-range load operation and maintenance. Consequently, efficiency and reliability are critical concerns for converters in offshore dc microgrids. This article presents an optimal control strategy aimed at enhancing both efficiency and reliability. A normalized nonlinear relationship between power loss and thermal stress of a paralleled converter is first established. Based on this, a dual-objective optimization function with an active weight function as well as a system overall performance index is established. The active weight function dynamically adjusts the control priority based on converter efficiency and switching device thermal stress. Then, the optimal power-sharing strategy is derived by the Lagrange multiplier method with the proposed optimal function. Additionally, to accommodate a wide load range, an optimal selection strategy for operating converter combinations is proposed, requiring only low-bandwidth communication. Experiment verification is given to validate the effectiveness of the proposed control strategy. The experiment results demonstrate that the proposed control strategy can improve the overall performance of offshore microgrids by optimizing efficiency and reliability.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Uncertainty propagation and sensitivity analysis for constrained optimization of nuclear waste vitrification

Abstract The vitrification of high‐level waste (HLW) by heating a mixture of glass‐forming chemicals (GFCs) with the waste can be improved using a constrained optimization problem. This study explores how different uncertainty propagation (UP) methods implemented with the optimization process can affect the glass formulation of nuclear waste glasses. UP is the effort of propagating uncertain inputs through a system to understand and quantify output distributions. Uncertainty intervals are crafted from output distributions to inform the optimization algorithm. UP is often implemented with Monte Carlo (MC) sampling for large nonlinear systems, which can be difficult to implement within a constrained optimization algorithm that requires derivative information. Other UP methods often used for optimization under uncertainty (OUU) can be designed to work within an established constrained optimization framework. Methods of UP are evaluated in this study including iterative sampling approaches, first‐order approximations, and surrogate modeling with machine learning (ML). A method of dimensional reduction based on global sensitivity analysis is introduced to support the UP methods for the large dimensionality of the problem. Analytical UP methods able to achieve similar optimums 10 times faster than the baseline MC approach, and produce 93.9% similar output distributions are reported.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Optimization of a Cyclone Using Multiphase Flow Computational Fluid Dynamics

The U.S. Department of Energy National Energy Technology Laboratory's (NETL) 50 kW th chemical looping reactor (CLR) has an underperforming cyclone, which was designed using empirical correlations. To improve the performance of this cyclone using computational fluid dynamics (CFD)-based modeling simulations, four critical design parameters including the vortex tube radius and length, barrel radius, and the inlet width and height were optimized. Here, NETL's open source multiphase flow with interphase exchange (MFiX) CFD code has been used to model a series of cyclones by systematically varying the geometric design parameters. To perform the optimization process, the surrogate modeling and sensitivity analysis followed by the optimization capability in nodeworks was used. The basic methodology for the process is to employ a statistical design of experiments (DOE) method to generate sampling simulations that fill the design space. Corresponding CFD models are then created, executed, and postprocessed. A response surface is created to characterize the relationship between input parameters and the quantities of interest (QoI). Finally, the CFD-surrogate is used by an optimization method to find the optimal design condition based on the objective and constraints prescribed. The resulting optimal cyclone has a larger diameter and longer vortex tube, a larger diameter barrel, and a taller and narrower solids inlet. The improved design has a predicted pressure drop 11 times lower than the original design while reducing the mass loss by a factor of 2.3.

42 ENGINEERING↗

Poster — Thur Eve — 61: A new framework for MPERT plan optimization using MC-DAO

This work combines the inverse planning technique known as Direct Aperture Optimization (DAO) with Intensity Modulated Radiation Therapy (IMRT) and combined electron and photon therapy plans. In particular, determining conditions under which Modulated Photon/Electron Radiation Therapy (MPERT) produces better dose conformality and sparing of organs at risk than traditional IMRT plans is central to the project. Presented here are the materials and methods used to generate and manipulate the DAO procedure. Included is the introduction of a powerful Java-based toolkit, the Aperture-based Monte Carlo (MC) MPERT Optimizer (AMMO), that serves as a framework for optimization and provides streamlined access to underlying particle transport packages. Comparison of the toolkit's dose calculations to those produced by the Eclipse TPS and the demonstration of a preliminary optimization are presented as first benchmarks. Excellent agreement is illustrated between the Eclipse TPS and AMMO for a 6MV photon field. The results of a simple optimization shows the functioning of the optimization framework, while significant research remains to characterize appropriate constraints.

60 APPLIED LIFE SCIENCES↗

Asymmetric dose–volume optimization with smoothness control for rotating-shield brachytherapy

Purpose: It is important to reduce fluence map complexity in rotating-shield brachytherapy (RSBT) inverse planning to improve delivery efficiency while maintaining plan quality. This study proposes an efficient and effective RSBT dose optimization method which enables to produce smooth fluence maps. Methods: Five cervical cancer patients each with a high-risk clinical-target-volume (HR-CTV) larger than 40 cm{sup 3} were considered as the test cases. The RSBT source was a partially shielded electronic brachytherapy source (Xoft Axxent™). The anchor RSBT plans generated by the asymmetric dose–volume optimization with smoothness control (ADOS) method were compared against those produced by the dose–surface optimization (DSO) method and inverse-planning with simulated annealing (IPSA). Either L{sub 1}-norm or L{sub 2}-norm was used to measure the smoothness of a fluence map in the proposed ADOS method as one weighted term of the objective function. Uniform dwell-time scaling was applied to all plans such that HR-CTV D{sub 90} was maximized without violating the D{sub 2cc} tolerances of the rectum, bladder, and sigmoid colon. The quality of the anchor plans was measured with HR-CTV D{sub 90} of the anchor plans. Single-shielded RSBT [(S-RSBT), RSBT with single, fix sized delivery window] and dynamic-sheilded RSBT [(D-RSBT), RSBT with dynamically varying sized delivery window] delivery plans generated based on the anchor plans were also measured, with delivery time constraints of 10, 20, and 30 min/fraction (fx). Results: The average HR-CTV D{sub 90} values of the anchor plans achieved by the ADOS, DSO, and IPSA methods were 111.5, 94.2, and 107.4 Gy, respectively, where the weighting parameter β used in ADOS with L{sub 2}-norm was set to be 100. By using S-RSBT sequencing and 20 min/fx delivery time, the corresponding D{sub 90} values were 88.8, 81.9, and 83.4 Gy; while using D-RSBT sequencing with 20 min/fx delivery time, the corresponding D{sub 90} values were 91.4, 88.3, and 78.9 Gy, respectively. The average optimization times for ADOS, DSO, and IPSA were, respectively, 77, 4, and 1800 s. By using L{sub 1}-norm instead of L{sub 2}-norm in the ADOS method, the optimization time was increased by 20 s, while the D{sub 90} was reduced by 6.8 Gy on average. ADOS-L1 was found to be more sensitive to the weighting parameter than ADOS-L2. If β was increased to 10 000, the D{sub 90} drop with ADOS-L1 was 38 Gy, while the drop with ADOS-L2 is 13 Gy. Conclusions: The ADOS method had a reasonable optimization time cost, while achieving comparable RSBT dose plans as the IPSA method, which is of much higher time complexity. Compared to the DSO and IPSA methods, ADOS is able to generate anchor plans which are more suitable for RSBT delivery while preserving the high quality of the original plans. Compared to ADOS-L1, ADOS-L2 is able to achieve better quality of anchor plans more efficiently.

60 APPLIED LIFE SCIENCES↗

SU-E-T-07: 4DCT Robust Optimization for Esophageal Cancer Using Intensity Modulated Proton Therapy

Purpose: To develop a 4DCT robust optimization method to reduce the dosimetric impact from respiratory motion in intensity modulated proton therapy (IMPT) for esophageal cancer. Methods: Four esophageal cancer patients were selected for this study. The different phases of CT from a set of 4DCT were incorporated into the worst-case dose distribution robust optimization algorithm. 4DCT robust treatment plans were designed and compared with the conventional non-robust plans. Result doses were calculated on the average and maximum inhale/exhale phases of 4DCT. Dose volume histogram (DVH) band graphic and ΔD95%, ΔD98%, ΔD5%, ΔD2% of CTV between different phases were used to evaluate the robustness of the plans. Results: Compare to the IMPT plans optimized using conventional methods, the 4DCT robust IMPT plans can achieve the same quality in nominal cases, while yield a better robustness to breathing motion. The mean ΔD95%, ΔD98%, ΔD5% and ΔD2% of CTV are 6%, 3.2%, 0.9% and 1% for the robustly optimized plans vs. 16.2%, 11.8%, 1.6% and 3.3% from the conventional non-robust plans. Conclusion: A 4DCT robust optimization method was proposed for esophageal cancer using IMPT. We demonstrate that the 4DCT robust optimization can mitigate the dose deviation caused by the diaphragm motion.

60 APPLIED LIFE SCIENCES↗

SU-E-T-574: Novel Chance-Constrained Optimization in Intensity-Modulated Proton Therapy Planning to Account for Range and Patient Setup Uncertainties

Purpose: We propose to apply a probabilistic framework, namely chanceconstrained optimization, in the intensity-modulated proton therapy (IMPT) planning subject to range and patient setup uncertainties. The purpose is to hedge against the influence of uncertainties and improve robustness of treatment plans. Methods: IMPT plans were generated for a typical prostate patient. Nine dose distributions are computed — the nominal one and one each for ±5mm setup uncertainties along three cardinal axes and for ±3.5% range uncertainty. These nine dose distributions are supplied to the solver CPLEX as chance constraints to explicitly control plan robustness under these representative uncertainty scenarios with certain probability. This probability is determined by the tolerance level. We make the chance-constrained model tractable by converting it to a mixed integer optimization problem. The quality of plans derived from this method is evaluated using dose-volume histogram (DVH) indices such as tumor dose homogeneity (D5% – D95%) and coverage (D95%) and normal tissue sparing like V70 of rectum, V65, and V40 of bladder. We also compare the results from this novel method with the conventional PTV-based method to further demonstrate its effectiveness Results: Our model can yield clinically acceptable plans within 50 seconds. The chance-constrained optimization produces IMPT plans with comparable target coverage, better target dose homogeneity, and better normal tissue sparing compared to the PTV-based optimization [D95% CTV: 67.9 vs 68.7 (Gy), D5% – D95% CTV: 11.9 vs 18 (Gy), V70 rectum: 0.0 % vs 0.33%, V65 bladder: 2.17% vs 9.33%, V40 bladder: 8.83% vs 21.83%]. It also simultaneously makes the plan more robust [Width of DVH band at D50%: 2.0 vs 10.0 (Gy)]. The tolerance level may be varied to control the tradeoff between plan robustness and quality. Conclusion: The chance-constrained optimization generates superior IMPT plan compared to the PTV-based optimization with explicit control of plan robustness. NIH/NCI K25CA168984, Eagles Cancer Research Career Development, The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, Mayo ASU Seed Grant, and The Kemper Marley Foundation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

SU-E-T-590: Optimizing Magnetic Field Strengths with Matlab for An Ion-Optic System in Particle Therapy Consisting of Two Quadrupole Magnets for Subsequent Simulations with the Monte-Carlo Code FLUKA

Purpose: Aim of this study was to optimize the magnetic field strengths of two quadrupole magnets in a particle therapy facility in order to obtain a beam quality suitable for spot beam scanning. Methods: The particle transport through an ion-optic system of a particle therapy facility consisting of the beam tube, two quadrupole magnets and a beam monitor system was calculated with the help of Matlab by using matrices that solve the equation of motion of a charged particle in a magnetic field and field-free region, respectively. The magnetic field strengths were optimized in order to obtain a circular and thin beam spot at the iso-center of the therapy facility. These optimized field strengths were subsequently transferred to the Monte-Carlo code FLUKA and the transport of 80 MeV/u C12-ions through this ion-optic system was calculated by using a user-routine to implement magnetic fields. The fluence along the beam-axis and at the iso-center was evaluated. Results: The magnetic field strengths could be optimized by using Matlab and transferred to the Monte-Carlo code FLUKA. The implementation via a user-routine was successful. Analyzing the fluence-pattern along the beam-axis the characteristic focusing and de-focusing effects of the quadrupole magnets could be reproduced. Furthermore the beam spot at the iso-center was circular and significantly thinner compared to an unfocused beam. Conclusion: In this study a Matlab tool was developed to optimize magnetic field strengths for an ion-optic system consisting of two quadrupole magnets as part of a particle therapy facility. These magnetic field strengths could subsequently be transferred to and implemented in the Monte-Carlo code FLUKA to simulate the particle transport through this optimized ion-optic system.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

SU-E-T-780: Use Robustness Optimization (RO) Method to Improve the Planning Efficiency for Pencil Beam Scanning Cranial Spinal Irradiation

Purpose: We evaluate the feasibility of using robustness optimization (RO) function to improve the planning efficiency of pencil beam scanning (PBS) craniospinal irradiation (CSI) with gradient matching technique. Methods: A CSI patient was planned with 2 lateral brain fields and 4 posterior fields to cover the entire spine to maximal field of 24 cm × 20 cm on a compact PBS gantry, ProteusONE. CSI plans were generated using traditional volumetric gradient dose optimization (VGDO) and robustness optimization (RO) method respectively. In traditional VGDO, besides the sectioned spine target volumes, gradient volume (GV) were generated as 4 equally spaced structures e.g. 80%, 60%, 40%, and 20% of prescription dose. In RO method, only sectioned spine target volumes with an overlap of 4cm were created. In the robustness optimization settings, 5mm uncertainty in superior and inferior direction was defined for auto gradient optimization. Dosimetric metrics of conformity number (CN), homogeneity index (HI), and maximal cord doses were compared in Raystation version 4.6.100.6. Results: In VGDO method, total 16 GV structures and five 100% dose level target structures were contoured compared to total 5 target structures in RO method which saves 30 min in contour. With the same PTV coverage (95% volume receive 30.6Gy prescription dose), maximum cord dose is 32.64Gy in VGDO and 31.94Gy in RO. HI is 1.03 and 1.04 for VGDO and RO respectively. CN is 0.93 and 0.94 for VGDO and RO respectively. Conclusions: The dosimetric comparison demonstrated both methods are equivalent in terms of plan quality. With robust optimization for CSI gradient matching, it efficiently reduces the amount of planning target contour structure by factor of 4 and thus improves the planning efficiency especially for 4 or more gradient junction area.

60 APPLIED LIFE SCIENCES↗

MO-FG-204-08: Optimization-Based Image Reconstruction From Unevenly Distributed Sparse Projection Views

Purpose: Optimization-based reconstruction has been proposed and investigated for reconstructing CT images from sparse views, as such the radiation dose can be substantially reduced while maintaining acceptable image quality. The investigation has so far focused on reconstruction from evenly distributed sparse views. Recognizing the clinical situations wherein only unevenly sparse views are available, e.g., image guided radiation therapy, CT perfusion and multi-cycle cardiovascular imaging, we investigate the performance of optimization-based image reconstruction from unevenly sparse projection views in this work. Methods: The investigation is carried out using the FORBILD and an anthropomorphic head phantoms. In the study, 82 views, which are evenly sorted out from a full (360°) axial CT scan consisting of 984 views, form sub-scan I. Another 82 views are sorted out in a similar manner to form sub-scan II. As such, a CT scan with sparse (164) views at 1:6 ratio are formed. By shifting the two sub-scans relatively in view angulation, a CT scan with unevenly distributed sparse (164) views at 1:6 ratio are formed. An optimization-based method is implemented to reconstruct images from the unevenly distributed views. By taking the FBP reconstruction from the full scan (984 views) as the reference, the root mean square (RMS) between the reference and the optimization-based reconstruction is used to evaluate the performance quantitatively. Results: In visual inspection, the optimization-based method outperforms the FBP substantially in the reconstruction from unevenly distributed, which are quantitatively verified by the RMS gauged globally and in ROIs in both the FORBILD and anthropomorphic head phantoms. The RMS increases with increasing severity in the uneven angular distribution, especially in the case of anthropomorphic head phantom. Conclusion: The optimization-based image reconstruction can save radiation dose up to 12-fold while providing acceptable image quality for advanced clinical applications wherein only unevenly distributed sparse views are available. Research Grants: W81XWH-12-1-0138 (DoD), Sinovision Technologies.

60 APPLIED LIFE SCIENCES↗

TU-CD-304-10: Development and Optimization of “Compton Lens” Collimator Design for Increased Dose Rate in SRS

Purpose: To provide a proof of concept for a new collimator design to increase the dose rate at isocenter for stereotactic radiosurgery (SRS) treatments by taking advantage of off axis Compton scattered photons which are attenuated in current collimators. Methods: A fundamentally new collimator design was developed and optimized by introducing a series of slits to a standard block collimator. The introduction of slits allowed for initially off axis radiation that was scattered in the direction of the target to contribute to the target dose. For optimization, the design was broken into two parts: an upper interaction plate where primary scattering occurs, and a lower “Compton slit” region which allows for scattered photons traveling toward the target to reach isocenter. To optimize the design, a series of simulations were performed using MCNP6 in which several key parameters were adjusted and the output was compared to a standard collimator. Key parameters modified included the collimator material, cone size, and interaction plate thickness. The effects of using energies different than the traditional 6 MV beam were also explored. Results: An optimized collimator design utilizing a solid interaction plate with a Cesium-137 beam and a 4 mm cone size resulted in a dose rate increase on the order of 5% relative to standard collimators in use. Currently, designs incorporating a Cesium-137 source are the most feasible due to necessary size and weight concerns for 6 MV beams. Conclusion: Preliminary designs provide a proof of concept and indicate a potential to improve upon the dose rate of current collimators while not largely compromising the sharp dose falloff inherent to SRS. Further optimization into the geometry and positioning of the interaction plate, as well as slit optimization, will likely lead to further dose rate increases than were observed in this study. Funding for this work was provided by the Wisconsin Alumni Research Foundation (WARF). Authors have a conflict of interest as the technology discussed is patent pending, and thus there is a financial interest in the work.

60 APPLIED LIFE SCIENCES↗

WE-EF-BRA-07: High Performance Preclinical Irradiation Through Optimized Dual Focal Spot Dose Painting and Online Virtual Isocenter Radiation Field Targeting

Purpose: Advances in radiotherapy practice facilitated by collimation systems to shape radiation fields and image guidance to target these conformal beams have motivated proposals for more complex dose patterns to improve the therapeutic ratio. Recent progress in small animal radiotherapy platforms has provided the foundation to validate the efficacy of such interventions, but robustly delivering heterogeneous dose distributions at the scale and accuracy demanded by preclinical studies remains challenging. This work proposes a dual focal spot optimization method to paint spatially heterogeneous dose regions and an online virtual isocenter targeting method to accurately target the dose distributions. Methods: Two-dimensional dose kernels were empirically measured for the 1 mm diameter circular collimator with radiochromic film in a solid water phantom for the small and large x-ray focal spots on the X-RAD 225Cx microirradiator. These kernels were used in an optimization framework which determined a set of animal stage positions, beam-on times, and focal spot settings to optimally deliver a given desired dose distribution. An online method was developed which defined a virtual treatment isocenter based on a single image projection of the collimated radiation field. The method was demonstrated by optimization of a 6 mm circular 2 Gy target adjoining a 4 mm semicircular avoidance region. Results: The dual focal spot technique improved the optimized dose distribution with the proportion of avoidance region receiving more than 0.5 Gy reduced by 40% compared to the large focal spot technique. Targeting tests performed by irradiating ball bearing targets on radiochromic film pieced revealed the online targeting method improved the three-dimensional accuracy from 0.48 mm to 0.15 mm. Conclusion: The dual focal spot optimization and online virtual isocenter targeting framework is a robust option for delivering dose at the preclinical level and provides a new experimental option for unique radiobiological investigations This work is supported, in part, by the Natural Sciences and Engineering Research Council of Canada and a Mitacs-Accelerate fellowship. P.E. Lindsay, and D.A. Jaffray are listed as inventors of the system described herein. This system has been licensed to Precision X-Ray Inc. for commercial development.

60 APPLIED LIFE SCIENCES↗