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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2016

SU-F-T-359: Incorporating Dose Volume Histogram Prediction Into Auto-Planning for Volumetric-Modulated Arc Therapy in Rectal Cancer

Purpose: To incorporate dose volume histogram (DVH) prediction into Auto-Planning for volumetric-modulated arc therapy (VMAT) treatment planning and investigate the benefit of this new technique for rectal cancer. Methods: Ninety clinically accepted VMAT plans for patients with rectal cancer were selected and trained in the RapidPlan for DVH prediction. Both internal and external validations were performed before implementing the prediction model. A new VMAT planning method (hybrid-VMAT) was created with combining the DVH prediction and Auto-Planning. For each new patient, the DVH will be predicted and individual DVH constrains will be obtained and were exported as the original optimization parameters to the Auto-Planning (Pinnacle3 treatment planning system, v9.10) for planning. A total of 20 rectal cancer patients previously treated with manual VMAT (manual-VMAT) plans were replanned using this new method. Dosimetric comparisons were performed between manual VMAT and new method plans. Results: Hybrid-VMAT shows similar PTV coverage to manual-VMAT in D2%, D98% and HI (p>0.05) and superior coverage in CI (p=0.000). For the bladder, the means of V40 and mean dose are 36.0% and 35.6Gy for hybrid-VMAT and 42% and 38.0Gy for the manual-VMAT. For the left (right) femur, the means of V30 and mean dose are 10.6% (11.6%) and 17.9Gy (19.2Gy) for the hybrid-VMAT and 25.6% (24.1%) and 27.3Gy (26.2Gy) for the manual-VMAT. The hybrid-VMAT has significantly improved the organs at risk sparing. Conclusion: The integration of DVH prediction and Auto-Planning significantly improve the VMAT plan quality in the rectal cancer radiotherapy. Our results show the benefit of the new method and will be further investigated in other tumor sites.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-454: Dose-Mass-Histogram Sensitivity to Anatomical Changes During Radiotherapy for HNSCC

Purpose: To determine the sensitivity of dose-mass-histogram (DMH) due to anatomical changes of head-and-neck squamous cell carcinoma (HNSCC) radiotherapy (RT). Methods: Eight patients undergoing RT treatment for HNSCC were scanned during the third and sixth week of RT. These second (CT2) and third (CT3) CTs were co-registered to the planning CT (CT1). Contours were propagated via deformable registration from CT1 and doses were re-calculated. DMHs were extracted for each CT set. DMH sensitivity was assessed by dose-mass indices (DMIs), which represent the dose delivered to a certain mass of and anatomical structure. DMIs included: dose to 98%, 95% and 2% of the target masses (PTV1, PTV2, and PTV3) and organs-at-risk (OARs): cord DMI2%, brainstem DMI2%, left- and right-parotid DMI2% and DMI50%, and mandible DMI2%. A two-tailed paired t-test was used to compare changes to DMIs in CT2 and CT3 with respect to CT1 (CT2/CT1 and CT3/CT1). Results: Changes to DMHs were found for all OARs and PTVs, but they were significant only for the PTVs. Maximum dose to PTVs increased significantly for CT2/CT1 in all three PTVs, but CT3/CT1 changes were only significantly different for PTV1 and PTV2. Dose coverage to the three PTVs was also significantly different, DMI98% was lower for both CT2/CT1 and CT3/CT1. DMI95% was significantly lower for PTV1 for CT2/CT1, PTV2 for CT2/CT1 and CT3/CT1, and PTV3 for CT3/CT1. Conclusion: Changes in anatomy significantly change dose-mass coverage for the planning targets, making it necessary to re-plan in order to maintain the therapeutic goals. Maximum dose to the PTVs increase significantly as RT progresses, which may not be problematic as long as the high dose remains in the gross tumor volume. Doses to OARs were minimally affected and the differences were not significant.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-FG-202-04: Decomposition of FDG-PET Based Differential Uptake Volume Histograms in Rectal Cancer Patients

Purpose: The goal of this study is to test the possible use of the analytical decomposition of differential uptake volume histograms (dUVHs) obtained from FDG-PET/CT data to isolate sub-volumes within a tumor known as biological target volumes (BTVs). Methods: : A retrospective study was conducted on a cohort of 20 histo-pathologically confirmed rectal adenocarcinoma patients having PET/CT scans for staging. All patients (T3N0) underwent pre-operative endorectal brachytherapy. After surgery, patients were restaged: 10 patients were T0N0 and 10 were restaged as remaining T3N0. The extent of the disease was sampled in order to create dUVHs; subsequently decomposed into the fewest number of analytical Gaussian functions. Results: With the assumption that each function fit corresponded to a single sub-volume within the tumor, six sub-volumes were found to consistently emerge. The first two sub-volumes were influenced by contouring and were not considered in the analysis. For the T3N0 population, abundances for volumes V3-V6 were 63.6%±11.3%, 25.7%±8.4%, 6.1%±4.9%, and 4.7%±2.6%. For the T0N0 population, they were 50.2%±6.8%, 33.4%±4.3%, 11.8%±7.6%, and 4.7%±2.4%. The two populations were compared using two tailed T-tests: volumes 3 and 4 were statistically different with p values of 0.021 and 0.056 respectively. V6 was located at 8.63 ± 2.2 for T0N0 and 6.14 ± 0.78 for T3N0 group (p=0.016). Conclusion: We described a method for dUVH decomposition using FDG-PET images of rectal adenocarcinoma patients that subsequently went for pre-operative brachytherapy. In addition to extracting different sub-volumes corresponding to different FDG uptake levels, we observed different abundances of two sub-volumes as well as positions of the maximum uptake between the two patient groups. In addition to opening the door to further investigation into underlying physiological phenotypes of segmented subvolumes and their use for biological radiotherapy treatment planning, this method may also provide parameters that could correlate to clinical outcomes in radiotherapy patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

A Histogram-Free Multicanonical Monte Carlo Algorithm for the Basis Expansion of Density of States

We report a new multicanonical Monte Carlo (MC) algorithm to obtain the density of states (DOS) for physical systems with continuous state variables in statistical mechanics. Our algorithm is able to obtain an analytical form for the DOS 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 (MUCA) sampling and Wang-Landau (WL) sampling. This is enabled by storing the visited states directly in a data set 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.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A copula-based rank histogram ensemble filter

Serial ensemble filters implement triangular probability transport maps to reduce high-dimensional inference problems to sequences of state-by-state univariate inference problems. The univariate inference problems are solved by sampling posterior probability densities obtained by combining constructed prior densities with observational likelihoods according to Bayes' rule. Many serial filters in the literature focus on representing the marginal posterior densities of each state. However, rigorously capturing the conditional dependencies between the different univariate inferences is crucial to correctly sampling multidimensional posteriors. This work proposes a new serial ensemble filter, called the copula rank histogram filter (CoRHF), that seeks to capture the conditional dependency structure between variables via empirical copula estimates; these estimates are used to rigorously implement the triangular (state-by-state univariate) Bayesian inference. The success of the CoRHF is demonstrated on two-dimensional examples and the Lorenz'63 problem. A practical extension to the high-dimensional setting is developed by localizing the empirical copula estimation, and is demonstrated on the Lorenz'96 problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Dynamic color mapping with a multi-scale histogram: A design study with physical scientists

Many research and development activities for scientific data analysis have focused on scalability challenges and data-driven features. Conversely, data visualization that focus on models requiring human interaction rarely involve practical and largescale scientific data analysis. Therefore, a gap exists between interactive data visualization and scientific data analysis applications. In this paper, we present a design study of interactive data visualization to support scientists who visually analyze data from neutron scattering experiments. This study was conducted in multiple phases: 1) problem characterization; 2) initial design and formative evaluation; and 3) iterative design. We characterize the problems and the design requirements for the analysis of the specific physical science data. We discuss the design, development, evaluation of our visual analytics tool and as well as our iterative developments with physical scientists. We show how to bridge the gap between the two disciplines uncovering new potential to solve their challenges in this design study. We focus on a specific challenge, finding an optimal color mapping, which plays a critical role in neutron scattering science and is broadly applicable to other scientific domains. To address the challenge, we propose two interactive visualization techniques: a dynamic color scale bar (DCSB) and a multi-scale histogram (MSH).

Chae, Junghoon↗
NASA NTRS1979

Multidimensional histograms

Program computes four dimensional histogram of Landsat multispectral image data. System enhances image handling and significantly reduces reprocessing costs.

Jayroe, R. R., Jr.↗