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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-E-T-628: Predicted Risk of Post-Irradiation Cerebral Necrosis in Pediatric Brain Cancer Patients: A Treatment Planning Comparison of Proton Vs. Photon Therapy

Purpose: Post-irradiation cerebral necrosis (PICN) is a severe late effect that can Result from brain cancers treatment using radiation therapy. The purpose of this study was to compare the treatment plans and predicted risk of PICN after volumetric modulated arc therapy (VMAT) to the risk after passively scattered proton therapy (PSPT) and intensity modulated proton therapy (IMPT) in a cohort of pediatric patients. Methods: Thirteen pediatric patients with varying age and sex were selected for this study. A clinical treatment volume (CTV) was constructed for 8 glioma patients and 5 ependymoma patients. Prescribed dose was 54 Gy over 30 fractions to the planning volume. Dosimetric endpoints were compared between VMAT and proton plans. The normal tissue complication probability (NTCP) following VMAT and proton therapy planning was also calculated using PICN as the biological endpoint. Sensitivity tests were performed to determine if predicted risk of PICN was sensitive to positional errors, proton range errors and selection of risk models. Results: Both PSPT and IMPT plans resulted in a significant increase in the maximum dose and reduction in the total brain volume irradiated to low doses compared with the VMAT plans. The average ratios of NTCP between PSPT and VMAT were 0.56 and 0.38 for glioma and ependymoma patients respectively and the average ratios of NTCP between IMPT and VMAT were 0.67 and 0.68 for glioma and ependymoma plans respectively. Sensitivity test revealed that predicted ratios of risk were insensitive to range and positional errors but varied with risk model selection. Conclusion: Both PSPT and IMPT plans resulted in a decrease in the predictive risk of necrosis for the pediatric plans studied in this work. Sensitivity analysis upheld the qualitative findings of the risk models used in this study, however more accurate models that take into account dose and volume are needed.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2014

SU-E-T-396: Dosimetric Accuracy of Proton Therapy for Patients with Metal Implants in CT Scans Using Metal Deletion Technique (MDT) Artifacts Reduction

Purpose: To evaluate the dosimetric accuracy for proton therapy patients with metal implants in CT using metal deletion technique (MDT) artifacts reduction. Methods: Proton dose accuracies under CT metal artifacts were first evaluated using a water phantom with cylindrical inserts of different materials (titanium and steel). Ranges and dose profiles along different beam angles were calculated using treatment planning system (Eclipse version 8.9) on uncorrected CT, MDT CT, and manually-corrected CT, where true Hounsfield units (water) were assigned to the streak artifacts. In patient studies, the treatment plans were developed on manually-corrected CTs, then recalculated on MDT and uncorrected CTs. DVH indices were compared between the dose distributions on all the CTs. Results: For water phantom study with 1/2 inch titanium insert, the proton range differences estimated by MDT CT were with 1% for all beam angles, while the range error can be up to 2.6% for uncorrected CT. For the study with 1 inch stainless steel insert, the maximum range error calculated by MDT CT was 1.09% among all the beam angles compared with maximum range error with 4.7% for uncorrected CT. The dose profiles calculated on MDT CTs for both titanium and steel inserts showed very good agreements with the ones calculated on manually-corrected CTs, while large dose discrepancies calculated using uncorrected CTs were observed in the distal end region of the proton beam. The patient study showed similar dose distribution and DVHs for organs near the metal artifacts recalculated on MDT CT compared with the ones calculated on manually-corrected CT, while the differences between uncorrected and corrected CTs were much pronounced. Conclusion: In proton therapy, large dose error could occur due to metal artifact. The MDT CT can be used for proton dose calculation to achieve similar dose accuracy as the current clinical practice using manual correction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-F-BRD-05: Robustness of Dose Painting by Numbers in Proton Therapy

Purpose: Proton range uncertainties may cause important dose perturbations within the target volume, especially when steep dose gradients are present as in dose painting. The aim of this study is to assess the robustness against setup and range errors for high heterogeneous dose prescriptions (i.e., dose painting by numbers), delivered by proton pencil beam scanning. Methods: An automatic workflow, based on MATLAB functions, was implemented through scripting in RayStation (RaySearch Laboratories). It performs a gradient-based segmentation of the dose painting volume from 18FDG-PET images (GTVPET), and calculates the dose prescription as a linear function of the FDG-uptake value on each voxel. The workflow was applied to two patients with head and neck cancer. Robustness against setup and range errors of the conventional PTV margin strategy (prescription dilated by 2.5 mm) versus CTV-based (minimax) robust optimization (2.5 mm setup, 3% range error) was assessed by comparing the prescription with the planned dose for a set of error scenarios. Results: In order to ensure dose coverage above 95% of the prescribed dose in more than 95% of the GTVPET voxels while compensating for the uncertainties, the plans with a PTV generated a high overdose. For the nominal case, up to 35% of the GTVPET received doses 5% beyond prescription. For the worst of the evaluated error scenarios, the volume with 5% overdose increased to 50%. In contrast, for CTV-based plans this 5% overdose was present only in a small fraction of the GTVPET, which ranged from 7% in the nominal case to 15% in the worst of the evaluated scenarios. Conclusion: The use of a PTV leads to non-robust dose distributions with excessive overdose in the painted volume. In contrast, robust optimization yields robust dose distributions with limited overdose. RaySearch Laboratories is sincerely acknowledged for providing us with RayStation treatment planning system and for the support provided.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations

We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempirical OM2 method using a set of 6095 constitutional isomers C7H10O2, for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules.

Dral, Pavlo O.↗
DOE OSTI2016

MO-FG-CAMPUS-JeP1-02: Proton Range Verification of Scanned Pencil Beams Using Prompt Gamma Imaging

Purpose: Prompt gammas are emitted along the proton beam path and have an emission profile correlated with the depth dose profile. In this study, the accuracy of in-vivo proton range verification using a 1-D prompt gamma camera is assessed. Methods: The 1-D camera is comprised of a tungsten slit collimator positioned in front of a linear array of LYSO scintillating crystals coupled to silicon photomultipliers. The imaged gamma profiles of individual pencil beam spots and energy layers were analyzed by determining the relative shifts from the expected gamma profiles based on analytic prediction or reference measurements. The range retrieval precision was evaluated by reproducibility measurements and by irradiation through a heterogeneous phantom composed of materials with known stopping power ratios. The camera was evaluated at clinical doses in pencil beam scanning mode on a head-and-neck phantom (HN). Two scenarios were studied: 5 mm systematic range error; and setup error of 10 mm transverse to the proton beam. Results: The camera range retrieval precision was 2 mm at clinical doses. For the heterogeneous phantom and HN phantom studies, the discrepancies between the analytic model and measurements were less than 2 mm for both spot and iso-energy layer analysis. For the simulated 5 mm range error, the retrieved shifts were 4.3±2.0 mm. For the 10 mm setup error, large shifts (> 4 mm) were observed for some spots due to differences in the irradiated and expected beam path from the measurements without setup error. Conclusion: Our studies demonstrated that in-vivo proton range verification is feasible using a 1D prompt gamma camera with a 2 mm range retrieval precision. Pencil beam spot under or over ranging can be detected via comparison between measured and expected profiles.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

MO-AB-BRA-07: Prompt Gamma-Ray Spectroscopy for Range Verification of Clinical Proton Beams

Purpose: We developed a pre-clinical prototype system for range verification of proton pencil-beam scanning fields. The system was evaluated using phantom treatment plans delivered with a clinical dose rate. Methods: The absolute range of proton pencil-beams was verified through an optimization procedure, which matches energy- and time-resolved prompt gamma-ray measurements with models, based on cross sections for discrete prompt gamma-ray line excitations. Phantom experiments were performed with a pre-clinical prototype detector, using treatment plans delivered with a clinical pencil-beam scanning system. The detector consisted of an actively shielded lanthanum(III) bromide scintillator. Tungsten was used to collimate the gamma-rays. To support high event rates, the detector readout featured custom amplifiers and an active voltage divider for the photomultiplier. The detector signals were acquired by fast analog-to-digital converters and processed using digital algorithms. The data acquisition was also synchronized with the pencil-beam scanning and dosimetry systems. Results: We successfully acquired prompt gamma-ray spectra during the delivery of proton pencil-beams with a clinical beam current of 2 nA at the exit of the treatment head. The number of events in the primary detector ranged from 1 x 10{sup 6} to 2 x 10{sup 6} per second. In phantom experiments, non-uniform range errors were introduced by placing strips of plastic in the beam path. The magnitudes and positions of these range errors were correctly detected in two-dimensional range maps that were generated from the measurements. With our small scale prototype, a 1.0 mm standard deviation on the absolute range required about 5 x 10{sup 8} protons per delivered pencil-beam. Conclusions: Prompt gamma-ray spectroscopy to verify the absolute range of proton beams was demonstrated under clinical pencil-beam delivery conditions. A 1 mm to 2 mm range verification accuracy for a field delivering 1 Gy, appears feasible with a full scale system. This work was supported by the Federal Share of program income earned on C06-CA059267, Proton Therapy Research and Treatment Center.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Dose Uncertainties in IMPT for Oropharyngeal Cancer in the Presence of Anatomical, Range, and Setup Errors

Purpose: Setup, range, and anatomical uncertainties influence the dose delivered with intensity modulated proton therapy (IMPT), but clinical quantification of these errors for oropharyngeal cancer is lacking. We quantified these factors and investigated treatment fidelity, that is, robustness, as influenced by adaptive planning and by applying more beam directions. Methods and Materials: We used an in-house treatment planning system with multicriteria optimization of pencil beam energies, directions, and weights to create treatment plans for 3-, 5-, and 7-beam directions for 10 oropharyngeal cancer patients. The dose prescription was a simultaneously integrated boost scheme, prescribing 66 Gy to primary tumor and positive neck levels (clinical target volume-66 Gy; CTV-66 Gy) and 54 Gy to elective neck levels (CTV-54 Gy). Doses were recalculated in 3700 simulations of setup, range, and anatomical uncertainties. Repeat computed tomography (CT) scans were used to evaluate an adaptive planning strategy using nonrigid registration for dose accumulation. Results: For the recalculated 3-beam plans including all treatment uncertainty sources, only 69% (CTV-66 Gy) and 88% (CTV-54 Gy) of the simulations had a dose received by 98% of the target volume (D98%) >95% of the prescription dose. Doses to organs at risk (OARs) showed considerable spread around planned values. Causes for major deviations were mixed. Adaptive planning based on repeat imaging positively affected dose delivery accuracy: in the presence of the other errors, percentages of treatments with D98% >95% increased to 96% (CTV-66 Gy) and 100% (CTV-54 Gy). Plans with more beam directions were not more robust. Conclusions: For oropharyngeal cancer patients, treatment uncertainties can result in significant differences between planned and delivered IMPT doses. Given the mixed causes for major deviations, we advise repeat diagnostic CT scans during treatment, recalculation of the dose, and if required, adaptive planning to improve adequate IMPT dose delivery.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

MRI-Based Computed Tomography Metal Artifact Correction Method for Improving Proton Range Calculation Accuracy

Purpose: Computed tomography (CT) artifacts can severely degrade dose calculation accuracy in proton therapy. Prompted by the recently increased popularity of magnetic resonance imaging (MRI) in the radiation therapy clinic, we developed an MRI-based CT artifact correction method for improving the accuracy of proton range calculations. Methods and Materials: The proposed method replaces corrupted CT data by mapping CT Hounsfield units (HU number) from a nearby artifact-free slice, using a coregistered MRI. MRI and CT volumetric images were registered with use of 3-dimensional (3D) deformable image registration (DIR). The registration was fine-tuned on a slice-by-slice basis by using 2D DIR. Based on the intensity of paired MRI pixel values and HU from an artifact-free slice, we performed a comprehensive analysis to predict the correct HU for the corrupted region. For a proof-of-concept validation, metal artifacts were simulated on a reference data set. Proton range was calculated using reference, artifactual, and corrected images to quantify the reduction in proton range error. The correction method was applied to 4 unique clinical cases. Results: The correction method resulted in substantial artifact reduction, both quantitatively and qualitatively. On respective simulated brain and head and neck CT images, the mean error was reduced from 495 and 370 HU to 108 and 92 HU after correction. Correspondingly, the absolute mean proton range errors of 2.4 cm and 1.7 cm were reduced to less than 2 mm in both cases. Conclusions: Our MRI-based CT artifact correction method can improve CT image quality and proton range calculation accuracy for patients with severe CT artifacts.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Two schemes for quantitative photoacoustic tomography based on Monte Carlo simulation

Purpose: The aim of this study was to develop novel methods for photoacoustically determining the optical absorption coefficient of biological tissues using Monte Carlo (MC) simulation. Methods: In this study, the authors propose two quantitative photoacoustic tomography (PAT) methods for mapping the optical absorption coefficient. The reconstruction methods combine conventional PAT with MC simulation in a novel way to determine the optical absorption coefficient of biological tissues or organs. Specifically, the authors’ two schemes were theoretically and experimentally examined using simulations, tissue-mimicking phantoms, ex vivo, and in vivo tests. In particular, the authors explored these methods using several objects with different absorption contrasts embedded in turbid media and by using high-absorption media when the diffusion approximation was not effective at describing the photon transport. Results: The simulations and experimental tests showed that the reconstructions were quantitatively accurate in terms of the locations, sizes, and optical properties of the targets. The positions of the recovered targets were accessed by the property profiles, where the authors discovered that the off center error was less than 0.1 mm for the circular target. Meanwhile, the sizes and quantitative optical properties of the targets were quantified by estimating the full width half maximum of the optical absorption property. Interestingly, for the reconstructed sizes, the authors discovered that the errors ranged from 0 for relatively small-size targets to 26% for relatively large-size targets whereas for the recovered optical properties, the errors ranged from 0% to 12.5% for different cases. Conclusions: The authors found that their methods can quantitatively reconstruct absorbing objects of different sizes and optical contrasts even when the diffusion approximation is unable to accurately describe the photon propagation in biological tissues. In particular, their methods are able to resolve the intrinsic difficulties that occur when quantitative PAT is conducted by combining conventional PAT with the diffusion approximation or with radiation transport modeling.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Analytical Stopping Power and Range Parameterization for Therapeutic Energy Intervals

The literature on stopping powers and ranges for proton and heavier ions is vast. It includes measurements, theory, evaluations and tabulations, and software programs and routines. Electronic stopping powers are typically calculated with either the Bethe-Bloch Equation or empirically derived functions, depending on the energy regime of the ion. Nuclear stopping powers are typically calculated using a theoretical approach. Ranges are commonly calculated numerically using path length integration, stopping power data and the continuous slowing down approximation. The Stopping and Ranges of Ions in Matter (SRIM) by Ziegler et al., which provides these and other capabilities, exemplifies the integration of highly-realistic theories, evaluated data, and radiation transport capabilities. In applications where speed and simplicity are paramount considerations, analytical calculations of range offer considerable advantages over numerical methods. Range has long been calculated analytically using a simple power law known as the range-energy relationship. This provides excellent accuracy over a surprisingly wide interval of ion species (e.g. protons, carbon), ion energies (e.g. 10-200 MeV/u), absorber compositions (e.g. plastic, water, metal), and thickness (e.g. 0.5-1 10 mm). The major limitation of formulas derived from a simple power law relation was their limited interval of applicability, 1.e., 10-200 MeV for protons in water. Thus, fora projectile in a stopping length target, such as a cancer patient, the simple power law model breaks down in the biologically important interval below 10 MeV. In current standard-of-care treatment planning, this is typically not a concern because the resulting range errors are less than 1 mm (and can be accounted for) and because biological effects are not calculated based on ion stopping power. However, there is considerable interest to perform multi-scale calculations of ion beams, e.g., at dimensions corresponding to cellular DNA, with ion energies below 10 MeV. Thus, there is an increasing need for new fast and simple formulae to calculate range and stopping power across a broader energy intervals of ion energy. It was not known if simple analytical equations would meet this need and, if so, the limits of their applicability. The aim of this work was to develop a simple, non-piecewise continuous model of total stopping power which could be analytically integrated to provide a simple formula for range. More specifically, we sought to develop a model to provide stopping powers with accuracy of better than 20% and range errors less than 1 mm for hydrogen, carbon, iron, and uranium projectile ions and target materials including water, carbon, aluminum, lead, copper, iron, gadolinium, and gold. (authors)

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

CrowdPhase: crowdsourcing the phase problem

The idea of attacking the phase problem by crowdsourcing is introduced. Using an interactive, multi-player, web-based system, participants work simultaneously to select phase sets that correspond to better electron-density maps in order to solve low-resolution phasing problems. The human mind innately excels at some complex tasks that are difficult to solve using computers alone. For complex problems amenable to parallelization, strategies can be developed to exploit human intelligence in a collective form: such approaches are sometimes referred to as ‘crowdsourcing’. Here, a first attempt at a crowdsourced approach for low-resolution ab initio phasing in macromolecular crystallography is proposed. A collaborative online game named CrowdPhase was designed, which relies on a human-powered genetic algorithm, where players control the selection mechanism during the evolutionary process. The algorithm starts from a population of ‘individuals’, each with a random genetic makeup, in this case a map prepared from a random set of phases, and tries to cause the population to evolve towards individuals with better phases based on Darwinian survival of the fittest. Players apply their pattern-recognition capabilities to evaluate the electron-density maps generated from these sets of phases and to select the fittest individuals. A user-friendly interface, a training stage and a competitive scoring system foster a network of well trained players who can guide the genetic algorithm towards better solutions from generation to generation via gameplay. CrowdPhase was applied to two synthetic low-resolution phasing puzzles and it was shown that players could successfully obtain phase sets in the 30° phase error range and corresponding molecular envelopes showing agreement with the low-resolution models. The successful preliminary studies suggest that with further development the crowdsourcing approach could fill a gap in current crystallographic methods by making it possible to extract meaningful information in cases where limited resolution might otherwise prevent initial phasing.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

TU-C-BRE-08: IMRT QA: Selecting Meaningful Gamma Criteria Based On Error Detection Sensitivity

Purpose: To develop a strategy for defining meaningful tolerance limits and studying the sensitivity of IMRT QA gamma criteria by inducing known errors in QA plans. Methods: IMRT QA measurements (ArcCHECK, Sun Nuclear) were compared to QA plan calculations with induced errors. Many (>24) gamma comparisons between data and calculations were performed for each of several kinds of cases and classes of induced error types with varying magnitudes (e.g. MU errors ranging from -10% to +10%), resulting in over 3,000 comparisons. Gamma passing rates for each error class and case were graphed against error magnitude to create error curves in order to represent the range of missed errors in routine IMRT QA using various gamma criteria. Results: This study demonstrates that random, case-specific, and systematic errors can be detected by the error curve analysis. Depending on location of the peak of the error curve (e.g., not centered about zero), 3%/3mm threshold=10% criteria may miss MU errors of up to 10% and random MLC errors of up to 5 mm. Additionally, using larger dose thresholds for specific devices may increase error sensitivity (for the same X%/Ymm criteria) by up to a factor of two. This analysis will allow clinics to select more meaningful gamma criteria based on QA device, treatment techniques, and acceptable error tolerances. Conclusion: We propose a strategy for selecting gamma parameters based on the sensitivity of gamma criteria and individual QA devices to induced calculation errors in QA plans. Our data suggest large errors may be missed using conventional gamma criteria and that using stricter criteria with an increased dose threshold may reduce the range of missed errors. This approach allows quantification of gamma criteria sensitivity and is straightforward to apply to other combinations of devices and treatment techniques.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Shortening Delivery Times of Intensity Modulated Proton Therapy by Reducing Proton Energy Layers During Treatment Plan Optimization

Purpose: To shorten delivery times of intensity modulated proton therapy by reducing the number of energy layers in the treatment plan. Methods and Materials: We have developed an energy layer reduction method, which was implemented into our in-house-developed multicriteria treatment planning system “Erasmus-iCycle.” The method consisted of 2 components: (1) minimizing the logarithm of the total spot weight per energy layer; and (2) iteratively excluding low-weighted energy layers. The method was benchmarked by comparing a robust “time-efficient plan” (with energy layer reduction) with a robust “standard clinical plan” (without energy layer reduction) for 5 oropharyngeal cases and 5 prostate cases. Both plans of each patient had equal robust plan quality, because the worst-case dose parameters of the standard clinical plan were used as dose constraints for the time-efficient plan. Worst-case robust optimization was performed, accounting for setup errors of 3 mm and range errors of 3% + 1 mm. We evaluated the number of energy layers and the expected delivery time per fraction, assuming 30 seconds per beam direction, 10 ms per spot, and 400 Giga-protons per minute. The energy switching time was varied from 0.1 to 5 seconds. Results: The number of energy layers was on average reduced by 45% (range, 30%-56%) for the oropharyngeal cases and by 28% (range, 25%-32%) for the prostate cases. When assuming 1, 2, or 5 seconds energy switching time, the average delivery time was shortened from 3.9 to 3.0 minutes (25%), 6.0 to 4.2 minutes (32%), or 12.3 to 7.7 minutes (38%) for the oropharyngeal cases, and from 3.4 to 2.9 minutes (16%), 5.2 to 4.2 minutes (20%), or 10.6 to 8.0 minutes (24%) for the prostate cases. Conclusions: Delivery times of intensity modulated proton therapy can be reduced substantially without compromising robust plan quality. Shorter delivery times are likely to reduce treatment uncertainties and costs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Clinical analysis of the approximate, 3-dimensional, biological effective dose equation in multiphase treatment plans

A multiphase, approximate biological effective dose (BED{sub A}) equation was introduced because most treatment planning systems (TPS) are incapable of calculating the true BED (BED{sub T}). This work investigates the accuracy and precision of the multiphase BED{sub A} relative to the BED{sub T} in clinical cases. Ten patients with head and neck cancer and 10 patients with prostate cancer were studied using their treatment plans from Pinnacle{sup 3} 9.2 (Philips Medical, Fitchburg, WI). The organs at risk (OARs) that were studied are the normal brain, left and right optic nerves, optic chiasm, spinal cord, brainstem, bladder, and rectum. BED{sub A} and BED{sub T} distributions were calculated using MATLAB 2010b (MathWorks, Natick, MA) and analyzed on a voxel basis for percent error, percent error volume histograms (PEVHs), Pearson correlation coefficient, and Bland-Altman analysis. The maximum BED values that were calculated using the BED{sub A} and BED{sub T} methods were also analyzed. BED{sub A} was found to always underestimate BED{sub T}. The accuracy and precision of BED{sub A} distributions varied between the organs: for optic chiasm and brainstem, 50% of the patients had an overall BED{sub A} percent error of A percent error of A percent error of A distributions had maximum errors ranging from 2% to 11%, with the 11% error occurring for bladder. BED{sub A} produced much more accurate maximum BED values with adjacent organs such as normal brain, bladder, and rectum. This study has shown that BED{sub A} can calculate BED distributions with acceptable accuracy under certain circumstances. However, its consistency and accuracy strongly depend on the dose distributions of the different treatment phases. One should be cautious when using BED{sub A}.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2014

SU-E-J-30: Benchmark Image-Based TCP Calculation for Evaluation of PTV Margins for Lung SBRT Patients

Purpose: Tumor control probability (TCP) calculated with accumulated radiation doses may help design appropriate treatment margins. Image registration errors, however, may compromise the calculated TCP. The purpose of this study is to develop benchmark CT images to quantify registration-induced errors in the accumulated doses and their corresponding TCP. Methods: 4DCT images were registered from end-inhale (EI) to end-exhale (EE) using a “demons” algorithm. The demons DVFs were corrected by an FEM model to get realistic deformation fields. The FEM DVFs were used to warp the EI images to create the FEM-simulated images. The two images combined with the FEM DVF formed a benchmark model. Maximum intensity projection (MIP) images, created from the EI and simulated images, were used to develop IMRT plans. Two plans with 3 and 5 mm margins were developed for each patient. With these plans, radiation doses were recalculated on the simulated images and warped back to the EI images using the FEM DVFs to get the accumulated doses. The Elastix software was used to register the FEM-simulated images to the EI images. TCPs calculated with the Elastix-accumulated doses were compared with those generated by the FEM to get the TCP error of the Elastix registrations. Results: For six lung patients, the mean Elastix registration error ranged from 0.93 to 1.98 mm. Their relative dose errors in PTV were between 0.28% and 6.8% for 3mm margin plans, and between 0.29% and 6.3% for 5mm-margin plans. As the PTV margin reduced from 5 to 3 mm, the mean TCP error of the Elastix-reconstructed doses increased from 2.0% to 2.9%, and the mean NTCP errors decreased from 1.2% to 1.1%. Conclusion: Patient-specific benchmark images can be used to evaluate the impact of registration errors on the computed TCPs, and may help select appropriate PTV margins for lung SBRT patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Validation of Multiple Tools for Flat Plate Photovoltaic Modeling Against Measured Data

This report expands upon a previous work by the same authors, published in the 40th IEEE Photovoltaic Specialists conference. In this validation study, comprehensive analysis is performed on nine photovoltaic systems for which NREL could obtain detailed performance data and specifications, including three utility-scale systems and six commercial scale systems. Multiple photovoltaic performance modeling tools were used to model these nine systems, and the error of each tool was analyzed compared to quality-controlled measured performance data. This study shows that, excluding identified outliers, all tools achieve annual errors within +/-8% and hourly root mean squared errors less than 7% for all systems. It is further shown using SAM that module model and irradiance input choices can change the annual error with respect to measured data by as much as 6.6% for these nine systems, although all combinations examined still fall within an annual error range of +/-8.5%. Additionally, a seasonal variation in monthly error is shown for all tools. Finally, the effects of irradiance data uncertainty and the use of default loss assumptions on annual error are explored, and two approaches to reduce the error inherent in photovoltaic modeling are proposed.

14 SOLAR ENERGY↗
DOE OSTI2022

Conversion of METL Diagnostic Flowmeters to Permanent Magnet Based Electromagnetic Flowmeters

The Mechanisms Engineering Test Loop (METL) uses three flowmeters to monitor sodium circulation through the main loop, the Cold Trap leg, and the Plugging Meter leg. When constructed, the Cold Trap flowmeter (CTFM) and Plugging Meter flowmeter (PMFM) were equipped with permanent magnets and Hall Sensors. By October of 2019, during normal operations, both the CTFM and PMFM failed, producing either erroneous or zero signals. Repairs to the flowmeters were attempted in January of 2020 but failed to resolve the issues. Therefore, starting in March of 2021, a plan was developed to convert the installed flowmeters to more robust permanent magnet based electromagnetic flowmeters (EMFMs). The Cold Trap EMFM (CT EMFM) and Plugging Meter EMFM (PM EMFM) were fully installed by August of 2021. Initial testing and commissioning were completed in November of 2021. Finally, the first calibration runs were finished in March of 2022. This report discusses the design, conversion, and calibration of the as-built CTFM and PMFM to robust permanent magnet based EMFMs. During the design phase, theoretical models were used to estimate the signal output and required magnet sizes. Then, the CTFM and PMFM were disassembled and converted to EMFMs. After commissioning, the CT EMFM and PM EMFM were calibrated using a ‘Time Transfer Procedure’ carried out with METL Expansion Vessels and Test Vessel Two. The accuracy of the calibration was estimated through an uncertainty analysis. Finally, the report describes some lessons learned for future flowmeter conversions and follow-up work. In total, both METL EMFM’s met the desired performance targets. Testing data showed that the METL EMFMs provide a output signal of roughly 2 mV at 1 GPM and 250 °C. During calibration, flowrates between 0.7 GPM and 2.4 GPM were achieved. In this flow range, calibration coefficients of 0.485 and 0.935 were measured as with errors ranging from 15-5% and 16-5% for the CT EMFM and PM EMFM, respectively. Above 1.5 GPM, the error was assumed to be about 5% for both METL EMFMs. Several points will be considered for future follow-up work on the METL EMFM’s. First, the signal noise issues in the PM EMFM will be addressed. It is likely the source of the noise is due to some stray EMF from a heater. Secondly, the NdFeB magnets will be replaced with a grade that has a Curie temperature above 80 °C. For example, a few grades of NeFeB magnets have Curie temperatures which exceed 150 °C and some SmCo magnets have Curie temperatures as high as 500 °C. Third, more calibration runs will be completed in the range of 0.5-2.5 GPM. This will further improve the accuracy of the calibration coefficient. Fourth, calibration runs will be completed up to 5 GPM. This requires a larger pressure relief valve to be installed on the Expansion Vessel and Test Vessel 2, as well as a revised calibration procedure. Lastly, calibration runs will be completed to account for cold spots in the system, which reduced the inlet temperature of the METL EMFMs. Overall, these points will improve the calibration coefficients derived in this work and also improve the performance of the METL EMFMs.

42 ENGINEERING↗
DOE OSTI2015

Feasibility and robustness of dose painting by numbers in proton therapy with contour-driven plan optimization

Purpose: To prove the ability of protons to reproduce a dose gradient that matches a dose painting by numbers (DPBN) prescription in the presence of setup and range errors, by using contours and structure-based optimization in a commercial treatment planning system. Methods: For two patients with head and neck cancer, voxel-by-voxel prescription to the target volume (GTV{sub PET}) was calculated from {sup 18}FDG-PET images and approximated with several discrete prescription subcontours. Treatments were planned with proton pencil beam scanning. In order to determine the optimal plan parameters to approach the DPBN prescription, the effects of the scanning pattern, number of fields, number of subcontours, and use of range shifter were separately tested on each patient. Different constant scanning grids (i.e., spot spacing = Δx = Δy = 3.5, 4, and 5 mm) and uniform energy layer separation [4 and 5 mm WED (water equivalent distance)] were analyzed versus a dynamic and automatic selection of the spots grid. The number of subcontours was increased from 3 to 11 while the number of beams was set to 3, 5, or 7. Conventional PTV-based and robust clinical target volumes (CTV)-based optimization strategies were considered and their robustness against range and setup errors assessed. Because of the nonuniform prescription, ensuring robustness for coverage of GTV{sub PET} inevitably leads to overdosing, which was compared for both optimization schemes. Results: The optimal number of subcontours ranged from 5 to 7 for both patients. All considered scanning grids achieved accurate dose painting (1% average difference between the prescribed and planned doses). PTV-based plans led to nonrobust target coverage while robust-optimized plans improved it considerably (differences between worst-case CTV dose and the clinical constraint was up to 3 Gy for PTV-based plans and did not exceed 1 Gy for robust CTV-based plans). Also, only 15% of the points in the GTV{sub PET} (worst case) were above 5% of DPBN prescription for robust-optimized plans, while they were more than 50% for PTV plans. Low dose to organs at risk (OARs) could be achieved for both PTV and robust-optimized plans. Conclusions: DPBN in proton therapy is feasible with the use of a sufficient number subcontours, automatically generated scanning patterns, and no more than three beams are needed. Robust optimization ensured the required target coverage and minimal overdosing, while PTV-approach led to nonrobust plans with excessive overdose. Low dose to OARs can be achieved even in the presence of a high-dose escalation as in DPBN.

60 APPLIED LIFE SCIENCES↗