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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

A Supervised Machine Learning Approach to Control Energy Storage Devices

This paper introduces a supervised machine learning approach to predict and schedule the real-time operation mode of the next operation interval for residential PV/Battery systems controlled by mode-based controllers. The performance of the mode-based economic model-predictive control (EMPC) approach is used as the benchmark. The residential load and PV data used in the paper are 1-minute data downloaded from the the Pecan Street Project website. The optimal operation mode for each control interval is first derived from the historical data used as the training set. Then, four machine learning algorithms (i.e. neural network, support vector machine, logistic regression, and random forest algorithms) are applied. We compared the performance of the four algorithms when using different number of features and length of the training sets extracted from different months of the year. Simulation results show that using the machine learning approach can effectively improve the performance of the mode-based control system and reduce the computation effort of local controllers because the training can be completed on a cloud-based Machine Learning engine. Furthermore, the work presented in this paper paves the way for using a shared-learning platform to design controllers of residential PV/storage systems. This may significantly reduce the cost for implementing such systems.

25 ENERGY STORAGE↗
DOE OSTI2017

Nest survival modelling using a multi‐species approach in forests managed for timber and biofuel feedstock

Abstract Switchgrass ( Panicum virgatum ) intercropping is a novel forest management practice for biomass production intended to generate cellulosic feedstocks within intensively managed loblolly pine‐dominated landscapes. These pine plantations are important for early‐successional bird species, as short rotation times continually maintain early‐successional habitat. We tested the efficacy of using community models compared to individual surrogate species models in understanding influences on nest survival. We analysed nest data to test for differences in habitat use for 14 bird species in plots managed for switchgrass intercropping and controls within loblolly pine ( Pinus taeda ) plantations in Mississippi, USA. We adapted hierarchical models using hyper‐parameters to incorporate information from both common and rare species to understand community‐level nest survival. This approach incorporates rare species that are often discarded due to low sample sizes, but can inform community‐level demographic parameter estimates. We illustrate use of this approach in generating both species‐level and community‐wide estimates of daily survival rates for songbird nests. We were able to include rare species with low sample size (minimum n = 5) to inform a hyper‐prior, allowing us to estimate effects of covariates on daily survival at the community level, then compare this with a single‐species approach using surrogate species. Using single‐species models, we were unable to generate estimates below a sample size of 21 nests per species. Community model species‐level survival and parameter estimates were similar to those generated by five single‐species models, with improved precision in community model parameters. Covariates of nest placement indicated that switchgrass at the nest site (<4 m) reduced daily nest survival, although intercropping at the forest stand level increased daily nest survival. Synthesis and applications . Community models represent a viable method for estimating community nest survival rates and effects of covariates while incorporating limited data for rarely detected species. Intercropping switchgrass in loblolly pine plantations slightly increased daily nest survival at the research plot scale (0.1 km 2 ), although at a local scale (50 m 2 ) switchgrass negatively influenced nest survival. A likely explanation is intercropping shifted community composition, favouring species with greater disturbance tolerance.

Loman, Zachary G.↗
DOE OSTI2018

Tiered Approach to Resilience Assessment

Regulatory agencies have long adopted a three-tier structure for risk assessment and risk management. We build on this structure to propose a tiered approach for resilience assessment that can be integrated in the existing regulatory processes. Comprehensive approaches to assessing resilience at appropriate and operational scales, reconciling analytical complexity as needed with stakeholder needs and resources available, and ultimately creating actionable recommendations to enhance resilience is still lacking. Our proposed structure consists of tiers by which analysts can scale a resilience assessment and associated management actions relative to the scope and urgency of the risk and the capacity of resource managers to improve system resilience. We use an application of water allocation decisions in the Platte River Basin, Nebraska, USA to illustrate this approach.

Sustainability, natural resources management, disa↗
DOE OSTI2023

Quantitative approaches for including equity in risk and resilience infrastructure planning analyses

Risk and resilience assessments for critical infrastructure focus on myriad objectives, from natural hazard evaluations to optimizing investments. Although research has started to characterize externalities associated with current or possible future states, incorporation of equity priorities at project inception is increasingly being recognized as critical for planning related activities. However, there is no standard methodology that guides development of equity-informed quantitative approaches for infrastructure planning activities. Here, to address this gap, we introduce a logic model that can be tailored to capture nuances about specific geographies and community priorities, effectively incorporating them into different mathematical approaches for quantitative risk assessments. Specifically, the logic model uses a graded, iterative approach to clarify specific equity objectives as well as inform the development of equations being used to support analysis. We demonstrate the utility of this framework using case studies spanning aviation fuel, produced water, and microgrid electricity infrastructures. For each case study, the use of the logic model helps clarify the ways that local priorities and infrastructure needs are used to drive the types of data and quantitative methodologies used in the respective analyses. The explicit consideration of methodological limitations (e.g., data mismatches) and stakeholder engagements serves to increase the transparency of the associated findings as well as effectively integrate community nuances (e.g., ownership of assets) into infrastructure assessments. Such integration will become increasingly important to ensure that planning activities (which occur throughout the lifecycle of the infrastructure projects) lead to long-lasting solutions to meet both energy and sustainable development goals for communities.

community-based↗
DOE OSTI2017

A Cyclic-Plasticity-Based Mechanistic Approach for Fatigue Evaluation of 316 Stainless Steel Under Arbitrary Loading

In this paper, a cyclic-plasticity based fully mechanistic fatigue modeling approach is presented. This is based on time-dependent stress-strain evolution of the material over the entire fatigue life rather than just based on the end of live information typically used for empirical S~N curve based fatigue evaluation approaches. Previously we presented constant amplitude fatigue test based related material models for 316 SS base, 508 LAS base and 316 SS- 316 SS weld which are used in nuclear reactor components such as pressure vessels, nozzles, and surge line pipes. However, we found that constant amplitude fatigue data based models have limitation in capturing the stress-strain evolution under arbitrary fatigue loading. To address the above mentioned limitation, in this paper, we present a more advanced approach that can be used for modeling the cyclic stress-strain evolution and fatigue life not only under constant amplitude but also under any arbitrary (random/variable) fatigue loading. The related material model and analytical model results are presented for 316 SS base metal. Two methodologies (either based on time/cycle or based on accumulated plastic strain energy) to track the material parameters at a given time/cycle are discussed and associated analytical model results are presented. From the material model and analytical cyclic plasticity model results, it is found that the proposed cyclic plasticity model can predict all the important stages of material behavior during the entire fatigue life of the specimens with more than 90% accuracy

316 Stainless Steel↗
DOE OSTI2020

A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) that fits high-level information about the response surface and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected for evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. Additionally, this methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface and, subsequently, to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to conventional optimization techniques, such as particle swarm and genetic algorithm-based optimization techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

An Approach to Bayesian Optimization for Design Feasibility Check on Discontinuous Black-Box Functions

The paper presents a novel approach to applying Bayesian Optimization (BO) in predicting an unknown constraint boundary, also representing the discontinuity of an unknown function, for a feasibility check on the design space, thereby representing a classification tool to discern between a feasible and infeasible region. Bayesian optimization is a low-cost black-box global optimization tool in the Sequential Design Methods where one learns and updates knowledge from prior evaluated designs, and proceeds to the selection of new designs for future evaluation. However, BO is best suited to problems with the assumption of a continuous objective function and does not guarantee true convergence when having a discontinuous design space. This is because of the insufficient knowledge of the BO about the nature of the discontinuity of the unknown true function. In this paper, we have proposed to predict the location of the discontinuity using a BO algorithm on an artificially projected continuous design space from the original discontinuous design space. The proposed approach has been implemented in a thin tube design with the risk of creep-fatigue failure under constant loading of temperature and pressure. The stated risk depends on the location of the designs in terms of safe and unsafe regions, where the discontinuities lie at the transition between those regions; therefore, the discontinuity has also been treated as an unknown creep-fatigue failure constraint. The proposed BO algorithm has been trained to maximize sampling toward the unknown transition region, to act as a high accuracy classifier between safe and unsafe designs with minimal training cost. The converged solution has been validated for different design parameters with classification error rate and function evaluations at an average of <1% and ~150, respectively. Finally, the performance of our proposed approach in terms of training cost and classification accuracy of thin tube design is shown to be better than the existing machine learning (ML) algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Boosting.

Engineering↗
DOE OSTI2024

Uncertainty-Aware, Structure-Preserving Machine Learning Approach for Domain Shift Detection From Nonlinear Dynamic Responses of Structural Systems

Complex structural systems deployed for aerospace, civil, or mechanical applications must operate reliably under varying operational conditions. Structural health monitoring (SHM) systems help ensure the reliability of these systems by providing continuous monitoring of the state of the structure. SHM relies on synthesizing measured data with a predictive model to make informed decisions about structural states. However, these models—which may be thought of as a form of a digital twin—need to be updated continuously as structural changes (e.g., due to damage) arise. We propose an uncertainty-aware machine learning model that enforces distance preservation of the original input state space and then encodes a distance-aware mechanism via a Gaussian process (GP) kernel. The proposed approach leverages the spectral-normalized neural GP algorithm to combine the flexibility of neural networks with the advantages of GP, subjected to structure-preserving constraints, to produce an uncertainty-aware model. This model is used to detect domain shift due to structural changes that cannot be observed directly because they may be spatially isolated (e.g., inside a joint or localized damage). This work leverages detection theory to detect domain shift systematically given statistical features of the prediction variance produced by the model. The proposed approach is demonstrated on a nonlinear structure being subjected to damage conditions. In conclusion, it is shown that the proposed approach is able to rely on distances of the transformed input state space to predict increased variance in shifted domains while being robust to normative changes.

Algorithms↗
DOE OSTI2025

Chemical Reactor Network Modeling of Ammonia Rich-Quench-Lean Combustion Using a Partially Stirred Reactor Approach

Ammonia is a promising alternative to hydrogen with high energy density and favorable storage and transport characteristics. However, low flammability and a propensity for high nitrogen oxide (NO x ) emissions make direct utilization challenging. Recently, two-stage rich-quench-lean (RQL) combustion strategies have shown promise in achieving low NO x emissions with ammonia. In this approach, the rich stage serves to oxidize a portion of the fuel while thermally decomposing as much of the remaining ammonia as possible, generating hydrogen. In the second (lean) stage, air is rapidly introduced, burning out the hydrogen and residual ammonia. Two-stage RQL combustion of ammonia has been investigated in the open literature both experimentally and numerically. In general, idealized chemical reactor network (CRN) models predict NO x concentrations below those of 2D/3D computational fluid dynamics models and experiments. The primary drivers of these discrepancies may be largely attributed to finite rate mixing nonadiabatic operation. The typical CRN model is comprised of a perfectly-stirred-reactor (PSR), followed by a plug-flow-reactor (PFR), meant to represent the flame, and postflame zones, respectively. In the two-stage RQL approach two PSR-PFR networks are arranged sequentially, corresponding to the rich and lean stages, with secondary air injection in between. In the authors' past work, this arrangement has demonstrated the significant sensitivity of exit NO x to the rich stage equivalence ratio, while the amount of secondary air injection was shown to be less critical. In this paper, the CRN model is extended to (1) include the impacts of heat loss and (2) utilize a partially-stirred-reactor (PaSR) approach to study the impacts of mixing on emissions performance. Varying amounts of heat loss are applied to the rich relaxation zone to understand emissions performance and changes to optimization of equivalence ratio and residence time. Premixed and nonpremixed configurations are considered in the rich stage PaSR, with varying degrees of mixing intensity to study the interaction between mixing, transport, and kinetic timescales. Critically, the impact of mixing between hot products and secondary air injection is studied to understand practical injector needs. Results show unburnt ammonia leaving the rich stage as a primary contributor to NO x emissions – driven both by increased heat loss and reduced mixing rates. Furthermore, heat losses have been shown to create conditions that are conducive to increased N 2 O formation in the lean stage. In conclusion, the results of this study will be considered in the context of developing optimized two-stage RQL combustors for ammonia.

Combustion↗
DOE OSTI2013

An integrated approach to geological disposal of UK wastes containing carbon-14

Carbon-14 is a key radionuclide in the assessment of the safety of a geological disposal facility for radioactive waste because of the calculated assessment of the radiological consequences of gaseous carbon-14 bearing species [i]. It may be that such calculations are based on overly conservative assumptions and that better understanding could lead to considerably reduced assessment of the radiological consequences from these wastes. Alternatively, it may be possible to mitigate the impact of these wastes through alternative treatment, packaging or design options. The Radioactive Waste Management Directorate of the UK's Nuclear Decommissioning Authority (NDA RWMD) has established an integrated project team in which the partners are working together to develop a holistic approach to carbon-14 management in the disposal system [ii]. For a waste stream containing carbon-14 to be an issue: There must be a significant inventory of carbon-14 in the waste stream; and That waste stream has to generate carbon-14 bearing gas; and a bulk gas phase has to entrain the carbon-14 bearing gas: and these gases must migrate through the engineered barriers in significant quantities; and these gases must migrate through the overlying geological environment (either as a distinct gas phase or as dissolved gas); and these gases must interact with materials in the biosphere (i.e. plants) in a manner that leads to significant doses and risks to exposed groups or potentially exposed groups. The project team has developed and used this 'and' approach to structure and prioritise the technical work and break the problem down in a manageable way. We have also used it to develop our approach to considering alternative treatment, packaging and design options. For example, it may be possible to pre-treat some wastes to remove some of the inventory or to segregate other wastes so that they are removed from any bulk gas phase which might facilitate migration through the geosphere. Initially, the project team has undertaken a six month programme of work to examine the current understanding of these aspects and has captured this in the Phase 1 report [ii], in a modelling basis spreadsheet and in scoping assessments, which help us better understand the potential significance of carbon-14. Using the current modelling basis, but ignoring any potential benefits from the geosphere in retarding or preventing gas from reaching the surface, the calculated release of carbon- 14 is dominated by: corrosion of irradiated reactive metals (in the operational and early post-closure time frame); corrosion of irradiated stainless steel and leaching of irradiated graphite (in the longer term). The Phase 1 work has shown that there is considerable scope for reducing the calculated radiological consequence for these wastes and a roadmap has been developed for a second Phase of work. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Modeling the Ductile Brittle Fracture Transition in Reactor Pressure Vessel Steels using a Cohesive Zone Model based approach

Fracture properties of Reactor Pressure Vessel (RPV) steels show large variations with changes in temperature and irradiation levels. Brittle behavior is observed at lower temperatures and/or higher irradiation levels whereas ductile mode of failure is predominant at higher temperatures and/or lower irradiation levels. In addition to such temperature and radiation dependent fracture behavior, significant scatter in fracture toughness has also been observed. As a consequence of such variability in fracture behavior, accurate estimates of fracture properties of RPV steels are of utmost importance for safe and reliable operation of reactor pressure vessels. A cohesive zone based approach is being pursued in the present study where an attempt is made to obtain a unified law capturing both stable crack growth (ductile fracture) and unstable failure (cleavage fracture). The parameters of the constitutive model are dependent on both temperature and failure probability. The effect of irradiation has not been considered in the present study. The use of such a cohesive zone based approach would allow the modeling of explicit crack growth at both stable and unstable regimes of fracture. Also it would provide the possibility to incorporate more physical lower length scale models to predict DBT. Such a multi-scale approach would significantly improve the predictive capabilities of the model, which is still largely empirical.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A data-driven approach to sampling matrix selection for compressive sensing

Sampling is a fundamental aspect of any implementation of compressive sensing. Typically, the choice of sampling method is guided by the reconstruction basis. However, this approach can be problematic with respect to certain hardware constraints and is not responsive to domain-specific context. We propose a method for defining an order for a sampling basis that is optimal with respect to capturing variance in data, thus allowing for meaningful sensing at any desired level of compression. We focus on the Walsh-Hadamard sampling basis for its relevance to hardware constraints, but our approach applies to any sampling basis of interest. We illustrate the effectiveness of our method on the Physical Sciences Inc. Fabry-Perot interferometer sensor multispectral dataset, the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset, and a Colorado State University Swiss Ranger depth image dataset. The spectral datasets consist of simulant experiments, including releases of chemicals such as GAA and SF6. We combine our sampling and reconstruction with the adaptive coherence estimator (ACE) and bulk coherence for chemical detection and we incorporate an algorithmic threshold for ACE values to determine the presence or absence of a chemical. We compare results across sampling methods in this context. We have successful chemical detection at a compression rate of 90%. For all three datasets, we compare our sampling approach to standard orderings of sampling basis such as random, sequency, and an analog of sequency that we term `frequency.' In one instance, the peak signal to noise ratio was improved by over 30% across a test set of depth images.

compressive sensing, hyperspectral imaging, Walsh-↗
DOE OSTI2013

Objected constrained registration and manifold learning: A new patient setup approach in image guided radiation therapy of thoracic cancer

Purpose: The management of thoracic malignancies with radiation therapy is complicated by continuous target motion. In this study, a real time motion analysis approach is proposed to improve the accuracy of patient setup. Methods: For 11 lung cancer patients a long training fluoroscopy was acquired before the first treatment, and multiple short testing fluoroscopies were acquired weekly at the pretreatment patient setup of image guided radiotherapy (IGRT). The data analysis consisted of three steps: first a 4D target motion model was constructed from 4DCT and projected to the training fluoroscopy through deformable registration. Then the manifold learning method was used to construct a 2D subspace based on the target motion (kinetic) and location (static) information in the training fluoroscopy. Thereafter the respiratory phase in the testing fluoroscopy was determined by finding its location in the subspace. Finally, the phase determined testing fluoroscopy was registered to the corresponding 4DCT to derive the pretreatment patient position adjustment for the IGRT. The method was tested on clinical image sets and numerical phantoms. Results: The registration successfully reconstructed the 4D motion model with over 98% volume similarity in 4DCT, and over 95% area similarity in the training fluoroscopy. The machine learning method derived the phase values in over 98% and 93% test images of the phantom and patient images, respectively, with less than 3% phase error. The setup approach achieved an average accumulated setup error less than 1.7 mm in the cranial-caudal direction and less than 1 mm in the transverse plane. All results were validated against the ground truth of manual delineations by an experienced radiation oncologist. The expected total time for the pretreatment setup analysis was less than 10 s. Conclusions: By combining the registration and machine learning, the proposed approach has the potential to improve the accuracy of pretreatment setup for patients with thoracic malignancy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TH-A-BRF-01: A Probabilistic Bayesian Approach to Derive Electron Density From MRI for Radiation Therapy Treatment Planning

Purpose: MRI significantly improves the accuracy and reliability of target delineation for patient simulation and treatment planning in radiation therapy, due to its superior soft tissue contrast as compared to CT. An MRI based simulation will reduce cost and simplify clinical workflow with zero ionizing radiation. However, MRI lacks the key electron density information. The purpose of this work is to develop a reliable method to derive electron density from MRI. Methods: We adopt a probabilistic Bayesian approach for electron density mapping based on T1-weighted head MRI. For each voxel, we compute conditional probability of electron densities given its: (1) T1 intensity and (2) geometry in a reference anatomy, obtained by deformable image registration between the MRI of test patient and atlas. Intensity and geometry information are combined into a unifying posterior probability density function whose mean gives the electron density. Mean absolute HU error between the estimated and true CT, as well as ROC's for bone detection (HU>200) were calculated for 8 patients. The performance was compared with a global intensity approach based on T1 and no density correction (set whole head to water). Results: The proposed technique significantly reduced the errors in electron density estimation, with a mean absolute HU error of 132, compared with 139 for deformable registration (p=10{sup −3}), 371 for the intensity approach (p=10{sup −5}) and 282 without density correction (p=2×10{sup −4}). For 90% sensitivity in bone detection, the proposed method had a specificity of 85% and that for deformable registration, intensity and without density correction are 80%, 24% and 10% respectively. Conclusion: The proposed unifying method provides accurate electron density estimation and bone detection from MRI of the head with highly heterogeneous regions. This paves the way for accurate dose calculation and generating reference images for patient setup in MRI-based treatment planning.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Radiation dose calculations for CT scans with tube current modulation using the approach to equilibrium function

Purpose: The approach to equilibrium function has been used previously to calculate the radiation dose to a shift-invariant medium undergoing CT scans with constant tube current [Li, Zhang, and Liu, Med. Phys. 39, 5347–5352 (2012)]. The authors have adapted this method to CT scans with tube current modulation (TCM). Methods: For a scan with variable tube current, the scan range was divided into multiple subscan ranges, each with a nearly constant tube current. Then the dose calculation algorithm presented previously was applied. For a clinical CT scan series that presented tube current per slice, the authors adopted an efficient approach that computed the longitudinal dose distribution for one scan length equal to the slice thickness, which center was at z = 0. The cumulative dose at a specific point was a summation of the contributions from all slices and the overscan. Results: The dose calculations performed for a total of four constant and variable tube current distributions agreed with the published results of Dixon and Boone [Med. Phys. 40, 111920 (14pp.) (2013)]. For an abdomen/pelvis scan of an anthropomorphic phantom (model ATOM 701-B, CIRS, Inc., VA) on a GE Lightspeed Pro 16 scanner with 120 kV, N × T = 20 mm, pitch = 1.375, z axis current modulation (auto mA), and angular current modulation (smart mA), dose measurements were performed using two lines of optically stimulated luminescence dosimeters, one of which was placed near the phantom center and the other on the surface. Dose calculations were performed on the central and peripheral axes of a cylinder containing water, whose cross-sectional mass was about equal to that of the ATOM phantom in its abdominal region, and the results agreed with the measurements within 28.4%. Conclusions: The described method provides an effective approach that takes into account subject size, scan length, and constant or variable tube current to evaluate CT dose to a shift-invariant medium. For a clinical CT scan, dose calculations may be performed with a water-containing cylinder whose cross-sectional mass is equal to that of the subject. This method has the potential to substantially improve evaluations of patient dose from clinical CT scans, compared to CTDI{sub vol}, size-specific dose estimate (SSDE), or the dose evaluated for a TCM scan with a constant tube current equal to the average tube current of the TCM scan.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Dosimetric evaluation of new approaches in GRID therapy using nonconventional radiation sources

Purpose: Spatial fractionation of the dose has proven to be a promising approach to increase the tolerance of healthy tissue, which is the main limitation of radiotherapy. A good example of that is GRID therapy, which has been successfully used in the management of large tumors with low toxicity. The aim of this work is to explore new avenues using nonconventional sources: GRID therapy by using kilovoltage (synchrotron) x-rays, the use of very high-energy electrons, and proton GRID therapy. They share in common the use of the smallest possible grid sizes in order to exploit the dose–volume effects. Methods: Monte Carlo simulations (PENELOPE/PENEASY and GEANT4/GATE codes) were used as a method to study dose distributions resulting from irradiations in different configurations of the three proposed techniques. As figure of merit, percentage (peak and valley) depth dose curves, penumbras, and central peak-to-valley dose ratios (PVDR) were evaluated. As shown in previous biological experiments, high PVDR values are requested for healthy tissue sparing. A superior tumor control may benefit from a lower PVDR. Results: High PVDR values were obtained in the healthy tissue for the three cases studied. When low energy photons are used, the treatment of deep-seated tumors can still be performed with submillimetric grid sizes. Superior PVDR values were reached with the other two approaches in the first centimeters along the beam path. The use of protons has the advantage of delivering a uniform dose distribution in the tumor, while healthy tissue benefits from the spatial fractionation of the dose. In the three evaluated techniques, there is a net reduction in penumbra with respect to radiosurgery. Conclusions: The high PVDR values in the healthy tissue and the use of small grid sizes in the three presented approaches might constitute a promising alternative to treat tumors with such spatially fractionated radiotherapy techniques. The dosimetric results presented here support the interest of performing radiobiology experiments in order to evaluate these new avenues.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

SU-E-J-141: Activity-Equivalent Path Length Approach for the 3D PET-Based Dose Reconstruction in Proton Therapy

Purpose: Ion beam therapy is sensitive to uncertainties from treatment planning and dose delivery. PET imaging of induced positron emitter distributions is a practical approach for in vivo, in situ verification of ion beam treatments. Treatment verification is usually done by comparing measured activity distributions with reference distributions, evaluated in nominal conditions. Although such comparisons give valuable information on treatment quality, a proper clinical evaluation of the treatment ultimately relies on the knowledge of the actual delivered dose. Analytical deconvolution methods relating activity and dose have been studied in this context, but were not clinically applied. In this work we present a feasibility study of an alternative approach for dose reconstruction from activity data, which is based on relating variations in accumulated activity to tissue density variations. Methods: First, reference distributions of dose and activity were calculated from the treatment plan and CT data. Then, the actual measured activity data were cumulatively matched with the reference activity distributions to obtain a set of activity-equivalent path lengths (AEPLs) along the rays of the pencil beams. Finally, these AEPLs were used to deform the original dose distribution, yielding the actual delivered dose. The method was tested by simulating a proton therapy treatment plan delivering 2 Gy on a homogeneous water phantom (the reference), which was compared with the same plan delivered on a phantom containing inhomogeneities. Activity and dose distributions were were calculated by means of the FLUKA Monte Carlo toolkit. Results: The main features of the observed dose distribution in the inhomogeneous situation were reproduced using the AEPL approach. Variations in particle range were reproduced and the positions, where these deviations originated, were properly identified. Conclusions: For a simple inhomogeneous phantom the 3D dose reconstruction from PET-activity induced by proton beams was shown to be feasible.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-100: An Approach to Improving the Dynamic Delivery Accuracy for Breast IMRT

Purpose: A method is established to improve the accuracy of the IMRT dose delivery for the treatment of breast and chest wall tumors when the higher dose gradient is delivered at the end of the leaf sequence. Methods: Dynamic MLC deliveries on Varian Linacs are achieved through the motion of the leaves from X1 to X2 direction. If the higher dose gradient is at the end of the leaf motion sequence, this can Result in an increased error in the overall dose delivery. Such errors have been observed in Lateral beams for Left-sided and Medial beams for Right-sided treatments. To evaluate and resolve this issue we adopted an approach where the fluence for such beams was geometrically flipped (mirrored) to treat the higher end of the dose gradient first. Results: Using this method, it was possible to deliver the optimized dose map to the area of interest still using only the X1–X2 direction of the leaf motion. The accuracy of this method was tested on different beam as part of our pre-treatment QA program on both Varian delivery systems. With this approach we found that there was significant improvement in delivery accuracy on both 21EX and TrueBeam systems. Beams of initial Gamma index (3% and 3mm) 89–93% were increased to 98–99%. We also observed superior delivery accuracy with TB compared to the 21EX Conclusion: This work demonstrate the need for a delivery sequence option from X2–X1 in situations where the MLC sequence indicates higher dose gradient component is being delivered at the end of the sequence. Results from this work can be considered in the IMRT beam optimization in the treatment planning systems. Further work will be required to establish the application of this approach in clinical setting.

60 APPLIED LIFE SCIENCES↗