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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-E-T-288: Dose Volume Population Histogram (DVPH): A New Method to Evaluate Intensity Modulated Proton Therapy Plans With Geometrical Uncertainties

Purpose: In Proton therapy, especially intensity modulated proton therapy(IMPT), the dose distribution shape is very sensitive to errors due to sharp dose gradients at the Bragg peaks. The concept of the conventional margin is based on the assumption that dose distribution is shifted rather than deformed due to geometrical uncertainties. The goal of this study is to access the validity of the margin concept as well as propose a new approach using Dose Volume Population Histogram (DVPH) in evaluating IMPT plans. Methods: For a prostate case, an intensity modulated proton therapy is optimized based on the conventional PTV based objective function. The plan is evaluated based on the PTV DVH and CTV DVPH (dose volume population histogram) which explicitly taking into account geometric uncertainties. The DVPH is calculated based on 2197 dose distributions at different CTV possible positions for both random and systematic errors. The DVPH with a 90% confidence level is used for the comparison. Results: The minimum dose of the CTV DVPH with a 90% confidence level is only about 18% of the prescribed dose, while the minimum dose of the PTV is 95%. For bladder DVHs, the D50 and D35 is 26% and 30%, compared to 65% and 70% of the prescribed dose from the bladder DVPH with 90% confidence level. Conclusion: The results showed that the PTV concept for ensuring the prescribed dose actually delivered to the CTV is invalid in proton therapy. The good PTV DVH might Result in an underdose to the target and should not be used for IMPT optimization. For OARs, the conventional evaluation approach underestimates dose volume end points. The new concept DVPH has been proved to provide a more accurate DVH evaluation in proton therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-430: Feasibility Study On Using Overlap Volume Histogram to Predict the Dose Difference by Respiratory Motion

Purpose: The dose difference between three-dimensional dose (3D dose) and 4D dose which considers motion due to respiratory can be varied according to geometrical relationship between planning target volume (PTV) and organ at risk (OAR). The purpose of the study is to investigate the dose difference between 3D and 4D dose using overlap volume histogram (OVH) which is an indicator that quantify geometrical relationship between a PTV and an OAR. Methods: Five liver cancer patients who previously treated stereotactic body radiotherapy (SBRT) were investigated. Four-dimensional computed tomography (4DCT) images were acquired for all patients. ITV-based treatment planning was performed. 3D dose was calculated on the end-exhale phase image as a reference phase image. 4D dose accumulation was implemented from all phase images using dose warping technique used deformable image registration (DIR) algorithm (Horn and Schunck optical flow) in DIRART. In this study OVH was used to quantify geometrical relationship between a PTV and an OAR. OVH between a PTV and a selected OAR was generated for each patient case and compared for all cases. The dose difference between 3D and 4D dose for normal organ was calculated and compared for all cases according to OVH. Results: The 3D and 4D dose difference for OAR was analyzed using dose-volume histogram (DVH). On the basis of a specific point which corresponds to 10% of OAR volume overlapped with expanded PTV, mean dose difference was 34.56% in minimum OVH distance case and 13.36% in maximum OVH distance case. As the OVH distance increased, mean dose difference between 4D and 3D dose was decreased. Conclusion: The tendency of dose difference variation was verified according to OVH. OVH is seems to be indicator that has a potential to predict the dose difference between 4D and 3D dose. This work was supported by the Radiation Technology R&D program (No. 2013M2A2A7043498) and the Mid-career Researcher Program (2014R1A2A1A10050270) through the National Research Foundation of Korea funded by the Ministry of Science, ICT&Future Planning.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Flux Histograms for 2017 Analysis

Flux for 2017 Analysis histograms for FHC and RHC:- The fluxes are integrated over the fiducial volume limited by -176 < X <177, -172 < Y < 179 and 25 < Z < 1150 cm. The fluxes are PPFX corrected, and both, the hadron production and focusing systematics are included (see docs 23441 and 17608 for more details).- These fluxes have been made with the official FHC (RHC) ND MC CAFAna files using the function DeriveFlux from CAFAna/XSec/Flux.*.- The root files have two types of histograms: flux_N and fractional_uncertainty_N, where N is the neutrino type. The entries are normalized by 10^6 POT, m2 and the bin width.- The text files contain tables of the flux normalized by 10^6 POT and m2.- The fractional uncertainties are made by the hadron production and beam focusing uncertainties added in quadrature. This corresponds to the sigma fraction respect to the central value and it meant to be used to make an uncertainty band (the covariance matrix is not included yet).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Improved Figure of Merit for Feynman Histograms

Typically a FOM that takes into account the differences between each bin of the histograms, as compared to the magnitude of the combination of the corresponding uncertainties, is used. Because the uncertainties corresponding to the larger multiplet bins are inherently larger than those of the smaller multiplet bins, this type of FOM puts more weight on differences between smaller multiplet bins. New proposed FOM also takes into account sensitivity of leakage multiplication (which is most sensitive to higher multiplet bins) to each bin in the histogram.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

NMF-Based Anomaly Detection in CMS 2D Tracking Occupancy Histograms

The CMS experiment relies on Data Quality Monitoring (DQM) to ensure that recorded collision data are suitable for physics analysis. During LHC Run 3, each run contains many lumisections and tracking monitoring elements, making offline inspection challenging, especially for localized detector effects that may appear only for short periods of time. This poster presents an unsupervised machine-learning approach to identify anomalous lumisections in CMS tracking occupancy histograms using Non-Negative Matrix Factorization (NMF). The workflow uses offline CMS DQMIO tracking histograms retrieved with the CMS DIALS API and organized as two-dimensional occupancy maps for each lumisection. After selecting stable lumisections, the occupancy maps are normalized and arranged into a non-negative data matrix. The NMF model learns a compact set of basis patterns describing normal tracking occupancy. Each lumisection is then reconstructed from these learned components, and the reconstruction error is used as an anomaly score. Large residuals indicate occupancy patterns that deviate from normal detector behavior and are flagged for further inspection. This NMF-based approach provides a fast and interpretable way to flag lumisections whose tracking occupancy patterns differ from normal detector behavior. Preliminary studies show sensitivity to known tracking anomalies, and ongoing work is focused on validating the method across additional Run 3 Pixel and Strip detector issues.

Rodríguez Ramos, Iliomar [Puerto Rico U., Mayaguez↗
DOE OSTI2021

Method for determining a histogram of variable sample rate waveforms

A computer-implemented method comprises receiving a plurality of sampled data points, each data point including a y value and a t value; defining an array of bins, each bin identified by a unique number and including histogram data for a range of y values; for each consecutive pair of data points including a current data point and a next data point, determining a corresponding one of a plurality of linear equations, each linear equation defining a line between the current data point and the next data point; for each line, determining an amount of time that the y value of the line is within the range of values for each bin from the current data point to the next data point; and adding the time to the histogram data for each bin.

Tohlen, Michael Aaron↗
DOE OSTI2024

Understanding atom probe’s analytical performance for iron oxides using correlation histograms and ab initio calculations

Abstract Field evaporation from ionic or covalently bonded materials often leads to the emission of molecular ions. The metastability of these molecular ions, particularly under the influence of the intense electrostatic field (10 10 Vm −1 ), makes them prone to dissociation with or without an exchange of energy amongst them. These processes can affect the analytical performance of atom probe tomography (APT). For instance, neutral molecules formed through dissociation may not be detected at all or with a time of flight no longer related to their mass, causing their loss from the analysis. Here, we evaluated the changes in the measured composition of FeO, Fe 2 O 3 and Fe 3 O 4 across a wide range of analysis conditions. Possible dissociation reactions are predicted by density-functional theory calculations considering the spin states of the molecules. The energetically favoured reactions are traced on to the multi-hit ion correlation histograms, to confirm their existence within experiments, using an automated Python-based routine. The detected reactions are carefully analyzed to reflect upon the influence of these neutrals from dissociation reactions on the performance of APT for analysing iron oxides.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2018

Dose–volume histogram evaluation scheme for beam orientations in intensity-modulated radiation therapy

Beam orientations in intensity-modulated radiation therapy (IMRT) is an important, but difficult NP-hard optimization problem. A considerable part of this difficulty is due to the essential prerequisite of defining, appropriately, counter-intuitive criteria for a mathematical plan evaluation that coincides with the clinical judgment of the considered plan. Moreover, the quality of beam directions depends heavily on their intensities. Usually a stochastic selector is utilized for optimizing beam orientations, and the intensities are thus calculated many thousands of times, each time for a different selection of beam directions, resulting in excessive time complexity. Therefore, selecting an appropriate set of beam directions in IMRT is still a time-consuming manual trial-and-error search procedure that depends on intuition and empirical knowledge. To overcome these difficulties, this work utilizes the concept of dose–volume histogram (DVH), which is one of the main recognized quantitative measurement tools used for plan judgment, and presents a DVH evaluation scheme that parallelizes plan evaluation with clinical plan judgment. This scheme is then utilized to optimize the intensities of each individual beam, enabling it to be represented by its DVHs, produced for the different irradiated volumes. Finally, an evolutionary algorithm is employed to search for the best combination of those DVHs reflecting the clinical optimal. The presented methods have been applied to three real clinical examples. In comparison to the standard equally spaced beam plans, improvements were reported in all three cases. In comparison with the greedy algorithm, improvements were reported in those examples requiring more than five beam directions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Histogram-free multicanonical Monte Carlo sampling to calculate the density of states

Here, we report a new multicanonical Monte Carlo algorithm to obtain the density of states for physical systems with continuous state variables in statistical mechanics. Our algorithm is able to obtain a closed-form expression for the density of states expressed in a chosen basis set, instead of a numerical array of finite resolution as in previous variants of this class of MC methods such as the multicanonical sampling and Wang–Landau sampling. This is enabled by storing the visited states directly and avoiding the explicit collection of a histogram. This practice also has the advantage of avoiding undesirable artificial errors caused by the discretization and binning of continuous state variables. Our results show that this scheme is capable of obtaining converged results with a much reduced number of Monte Carlo steps, leading to a significant speedup over existing algorithms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Magnetic Resonance, Vendor-independent, Intensity Histogram Analysis Predicting Pathologic Complete Response After Radiochemotherapy of Rectal Cancer

The objective of this study is finding an intensity based histogram (IBH) signature to predict pathologic complete response (pCR) probability using only pre-treatment magnetic resonance (MR) and validate it externally in order to create a workflow for the external validation of an MR IBH signature and to apply the model out of the environment where it has been tuned. The impact of pCR and the final predictors on the survival outcome were also evaluated.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Daily Rectal Dose-volume Histogram Variation in Prostate Intensity-modulated Radiation Therapy: Is It Clinically Significant in the Era of Image Guidance?

Because of the steep dose gradient associated with dose-escalated intensity-modulated radiation therapy, interfraction motion and variation in rectal volume may result in the rectum receiving a larger dose than predicted at treatment planning. This study aims to quantify the variation in daily rectal dose-volume histograms (DVHs) from the treatment plan and to discuss the potential clinical significance of this variation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Effect of various methods for rectum delineation on relative and absolute dose-volume histograms for prostate IMRT treatment planning

Several reports have dealt with correlations of late rectal toxicity with rectal dose-volume histograms (DVHs) for high dose levels. There are 2 techniques to assess rectal volume for reception of a specific dose: relative-DVH (R-DVH, %) that indicates relative volume for a vertical axis, and absolute-DVH (A-DVH, cc) with its vertical axis showing absolute volume of the rectum. The parameters of DVH vary depending on the rectum delineation method, but the literature does not present any standardization of such methods. The aim of the present study was to evaluate the effects of different delineation methods on rectal DVHs. The enrollment for this study comprised 28 patients with high-risk localized prostate cancer, who had undergone intensity-modulated radiation therapy (IMRT) with the prescription dose of 78 Gy. The rectum was contoured with 4 different methods using 2 lengths, short (Sh) and long (Lg), and 2 cross sections, rectum (Rec) and rectal wall (Rw). Sh means the length from 1 cm above the seminal vesicles to 1 cm below the prostate and Lg the length from the rectosigmoid junction to the anus. Rec represents the entire rectal volume including the rectal contents and Rw the rectal volume of the area with a wall thickness of 4 mm. We compared dose-volume parameters by using 4 rectal contour methods for the same plan with the R-DVHs as well as the A-DVHs. For the high dose levels, the R-DVH parameters varied widely. The mean of V{sub 70} for Sh-Rw was the highest (19.4%) and nearly twice as high as that for Lg-Rec (10.4%). On the contrary, only small variations were observed in the A-DVH parameters (4.3, 4.3, 5.5, and 5.5 cc for Sh-Rw, Lg-Rw, Sh-Rec, and Lg-Rec, respectively). As for R-DVHs, the parameters of V{sub 70} varied depending on the rectal lengths (Sh-Rec vs Lg-Rec: R = 0.76; Sh-Rw vs Lg-Rw: R = 0.85) and cross sections (Sh-Rec vs Sh-Rw: R = 0.49; Lg-Rec vs Lg-Rw: R = 0.65). For A-DVHs, however, the parameters of Sh rectal A-DVHs hardly changed regardless of differences in rectal length at all dose levels. Moreover, at high dose levels (V{sub 70}), the parameters of A-DVHs showed less dependence on rectal cross sections (Sh-Rec vs Sh-Rw: R = 0.66; Lg-Rec vs Lg-Rw: R = 0.59). This study showed that A-DVHs were less dependent on the delineation methods than R-DVHs, especially for evaluating the rectal dose at higher dose levels. It can therefore be assumed that, in addition to R-DVHs, A-DVHs can be used for evaluating rectal toxicity.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials

A major obstacle for machine learning (ML) in chemical science is the lack of physically informed feature representations that provide both accurate prediction and easy interpretability of the ML model. In this work, we describe adsorption systems using novel two-dimensional energy histogram (2D-EH) features, which are obtained from the probe-adsorbent energies and energy gradients at grid points located throughout the adsorbent. The 2D-EH features encode both energetic and structural information of the material and lead to highly accurate ML models (coefficient of determination R2 ~ 0.94–0.99) for predicting single-component adsorption capacity in metal–organic frameworks (MOFs). Here, we consider the adsorption of spherical molecules (Kr and Xe), linear alkanes with a wide range of aspect ratios (ethane, propane, n-butane, and n-hexane), and a branched alkane (2,2-dimethylbutane) over a wide range of temperatures and pressures. The interpretable 2D-EH features enable the ML model to learn the basic physics of adsorption in pores from the training data. We show that these MOF-data-trained ML models are transferrable to different families of amorphous nanoporous materials. We also identify several adsorption systems where capillary condensation occurs, and ML predictions are more challenging. Nevertheless, our 2D-EH features still outperform structural features including those derived from persistent homology. The novel 2D-EH features may help accelerate the discovery and design of advanced nanoporous materials using ML for gas storage and separation in the future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Flat-Histogram Monte Carlo as an Efficient Tool To Evaluate Adsorption Processes Involving Rigid and Deformable Molecules

Monte Carlo simulations are the foundational technique for predicting thermodynamic properties of open systems where the process of interest involves the exchange of particles. Thus, they have been used extensively to computationally evaluate the adsorption properties of nanoporous materials and are critical for the in silico identification of promising materials for a variety of gas storage and chemical separation applications. In this work we demonstrate that a well-known biasing technique, known as “flat-histogram” sampling, can be combined with temperature extrapolation of the free energy landscape to efficiently provide significantly more useful thermodynamic information than standard open ensemble MC simulations. Namely, we can accurately compute the isosteric heat of adsorption and number of particles adsorbed for various adsorbates over an extremely wide range of temperatures and pressures from a set of simulations at just one temperature. We extend this derivation of the temperature extrapolation to adsorbates with intramolecular degrees of freedom when Rosenbluth sampling is employed. Consequently, the working capacity and isosteric heat can be computed for any given combined temperature/pressure swing adsorption process for a large range of operating conditions with both rigid and deformable adsorbates. Finally, continuous thermodynamic properties can be computed with this technique at very moderate computational cost, thereby providing a strong case for its application to the in silico identification of promising nanoporous adsorbents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Nanothermodynamics of large iron clusters by means of a flat histogram Monte Carlo method

The thermodynamics of iron clusters of various sizes, from 76 to 2452 atoms, typical of the catalyst particles used for carbon nanotubes growth, has been explored by a flat histogram Monte Carlo (MC) algorithm (called the σ-mapping), developed by Soudan et al. [J. Chem. Phys. 135, 144109 (2011), Paper I]. This method provides the classical density of states, g{sub p}(E{sub p}) in the configurational space, in terms of the potential energy of the system, with good and well controlled convergence properties, particularly in the melting phase transition zone which is of interest in this work. To describe the system, an iron potential has been implemented, called “corrected EAM” (cEAM), which approximates the MEAM potential of Lee et al. [Phys. Rev. B 64, 184102 (2001)] with an accuracy better than 3 meV/at, and a five times larger computational speed. The main simplification concerns the angular dependence of the potential, with a small impact on accuracy, while the screening coefficients S{sub ij} are exactly computed with a fast algorithm. With this potential, ergodic explorations of the clusters can be performed efficiently in a reasonable computing time, at least in the upper half of the solid zone and above. Problems of ergodicity exist in the lower half of the solid zone but routes to overcome them are discussed. The solid-liquid (melting) phase transition temperature T{sub m} is plotted in terms of the cluster atom number N{sub at}. The standard N{sub at}{sup −1/3} linear dependence (Pawlow law) is observed for N{sub at} >300, allowing an extrapolation up to the bulk metal at 1940 ±50 K. For N{sub at} <150, a strong divergence is observed compared to the Pawlow law. The melting transition, which begins at the surface, is stated by a Lindemann-Berry index and an atomic density analysis. Several new features are obtained for the thermodynamics of cEAM clusters, compared to the Rydberg pair potential clusters studied in Paper I.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

SU-E-T-294: Dosimetric Analysis of Planning Phase Using Overlap Volume Histogram for Respiratory Gated Radiotherapy

Purpose: End-of-exhale (EOE) phase is generally preferred for gating window because tumor position is more reproducible. However, other gating windows might be more appropriate for dose distribution perspective. In this pilot study, we proposed to utilize overlap volume histogram (OVH) to search optimized gating window and demonstrated its feasibility. Methods: We acquired 4DCT of 10 phases for 3 lung patients (2 with a target at right middle lobe and 1 at right upper lobe). After structures were defined in every phase, the OVH of each OAR was generated to quantify the three dimensional spatial relationship between the PTV and OARs (bronchus, esophagus, heart and cord etc.) at each phase. OVH tells the overlap volume of an OAR according to outward distance from the PTV. Relative overlap volume at 20 mm outward distance from the PTV (ROV-20) was also defined as a metric for measuring overlap volume and obtained. For dose calculation, 3D CRT plans were made for all phases under the same beam angles and objectives (e.g., 95% of the PTV coverage with at least 100% of the prescription dose of 50 Gy). The gating window phase was ranked according to ROV-20, and the relationship between the OVH and dose distribution at each phase was evaluated by comparing the maximum dose, mean dose, and equivalent uniform dose of OAR. Results: OVHs showed noticeable difference from phase to phase, implying it is possible to find optimal phases for gating window. For 2 out of 3 patients (both with a target at RML), maximum dose, mean dose, and EUD increased as ROV-20 increased. Conclusion: It is demonstrated that optimal phases (in dose distribution perspective) for gating window could exist and OVH can be a useful tool for determining such phases without performing dose optimization calculations in all phases. This work was supported by the Radiation Technology R&D program (No. 2013M2A2A7043498) and the Mid-career Researcher Program (2012-007883) through the National Research Foundation (NRF) funded by the Ministry of Science, ICT & Future Planning (MSIP) of Korea.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-348: The Impact of Model Library Population On RapidPlan Based Dose-Volume Histograms (DVHs) Prediction for Rectal Cancer Patients Treated with Volumetric-Modulated Radiotherapy (VMAT)

Purpose: RapidPlan uses a library consisting of expert plans from different patients to create a model that can predict achievable dose-volume histograms (DVHs) for new patients. The goal of this study is to investigate the impacts of model library population (plan numbers) on the DVH prediction for rectal cancer patients treated with volumetric-modulated radiotherapy (VMAT) Methods: Ninety clinically accepted rectal cancer patients’ VMAT plans were selected to establish 3 models, named as Model30, Model60 and Model90, with 30,60, and 90 plans in the model training. All plans had sufficient target coverage and bladder and femora sparings. Additional 10 patients were enrolled to test the DVH prediction differences with these 3 models. The predicted DVHs from these 3 models were compared and analyzed. Results: Predicted V40 (Vx, percent of volume that received x Gy for the organs at risk) and Dmean (mean dose, cGy) of the bladder were 39.84±13.38 and 2029.4±141.6 for the Model30,37.52±16.00 and 2012.5±152.2 for the Model60, and 36.33±18.35 and 2066.5±174.3 for the Model90. Predicted V30 and Dmean of the left femur were 23.33±9.96 and 1443.3±114.5 for the Model30, 21.83±5.75 and 1436.6±61.9 for the Model60, and 20.31±4.6 and 1415.0±52.4 for the Model90.There were no significant differences among the 3 models for the bladder and left femur predictions. Predicted V40 and Dmean of the right femur were 19.86±10.00 and 1403.6±115.6 (Model30),18.97±6.19 and 1401.9±68.78 (Model60), and 21.08±7.82 and 1424.0±85.3 (Model90). Although a slight lower DVH prediction of the right femur was found on the Model60, the mean differences for V30 and mean dose were less than 2% and 1%, respectively. Conclusion: There were no significant differences among Model30, Model60 and Model90 for predicting DVHs on rectal patients treated with VMAT. The impact of plan numbers for model library might be limited for cancers with similar target shape.

60 APPLIED LIFE SCIENCES↗