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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Validation of a 4D-PET Maximum Intensity Projection for Delineation of an Internal Target Volume

Purpose: The delineation of internal target volumes (ITVs) in radiation therapy of lung tumors is currently performed by use of either free-breathing (FB) {sup 18}F-fluorodeoxyglucose-positron emission tomography-computed tomography (FDG-PET/CT) or 4-dimensional (4D)-CT maximum intensity projection (MIP). In this report we validate the use of 4D-PET-MIP for the delineation of target volumes in both a phantom and in patients. Methods and Materials: A phantom with 3 hollow spheres was prepared surrounded by air then water. The spheres and water background were filled with a mixture of {sup 18}F and radiographic contrast medium. A 4D-PET/CT scan was performed of the phantom while moving in 4 different breathing patterns using a programmable motion device. Nine patients with an FDG-avid lung tumor who underwent FB and 4D-PET/CT and >5 mm of tumor motion were included for analysis. The 3 spheres and patient lesions were contoured by 2 contouring methods (40% of maximum and PET edge) on the FB-PET, FB-CT, 4D-PET, 4D-PET-MIP, and 4D-CT-MIP. The concordance between the different contoured volumes was calculated using a Dice coefficient (DC). The difference in lung tumor volumes between FB-PET and 4D-PET volumes was also measured. Results: The average DC in the phantom using 40% and PET edge, respectively, was lowest for FB-PET/CT (DCAir = 0.72/0.67, DCBackground 0.63/0.62) and highest for 4D-PET/CT-MIP (DCAir = 0.84/0.83, DCBackground = 0.78/0.73). The average DC in the 9 patients using 40% and PET edge, respectively, was also lowest for FB-PET/CT (DC = 0.45/0.44) and highest for 4D-PET/CT-MIP (DC = 0.72/0.73). In the 9 lesions, the target volumes of the FB-PET using 40% and PET edge, respectively, were on average 40% and 45% smaller than the 4D-PET-MIP. Conclusion: A 4D-PET-MIP produces volumes with the highest concordance with 4D-CT-MIP across multiple breathing patterns and lesion sizes in both a phantom and among patients. Freebreathing PET/CT consistently underestimates ITV when compared with 4D PET/CT for a lesion affected by respiration.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Dosimetric Impact of the Interplay Effect During Stereotactic Lung Radiation Therapy Delivery Using Flattening Filter-Free Beams and Volumetric Modulated Arc Therapy

Purpose: We investigated the dosimetric impact of the interplay effect during RapidArc stereotactic body radiation therapy for lung tumors using flattening filter-free (FFF) beams with different dose rates. Methods and Materials: Seven tumors with motion ≤20 mm, treated with 10-MV FFF RapidArc, were analyzed. A programmable phantom with sinusoidal longitudinal motion (30-mm diameter “tumor” insert; period = 5 s; individualized amplitude from planning 4-dimensional computed tomography) was used for dynamic dose measurements. Measurements were made with GafChromic EBT III films. Plans delivered the prescribed dose to 95% of the planning target volume, created by a 5-mm expansion of the internal target volume. They comprised 2 arcs and maximum dose rates of 400 and 2400 MU/min. For 2400 MU/min plans, measurements were repeated at 3 different initial breathing phases to model interplay over 2 to 3 fractions. For 3 cases, 2 extra plans were created using 1 full rotational arc (with contralateral lung avoidance sector) and 1 partial arc of 224° to 244°. Dynamic and convolved static measurements were compared by use of gamma analysis of 3% dose difference and 1 mm distance-to-agreement. Results: For 2-arc 2400 MU/min plans, maximum dose deviation of 9.4% was found in a single arc; 7.4% for 2 arcs (single fraction) and <5% and 3% when measurements made at 2 and 3 different initial breathing phases were combined, simulating 2 or 3 fractions. For all 7 cases, >99% of the area within the region of interest passed the gamma criteria when all 3 measurements with different initial phases were combined. Single-fraction single-arc plans showed higher dose deviations, which diminished when dose distributions were summed over 2 fractions. All 400 MU/min plans showed good agreement in a single fraction measurement. Conclusion: Under phantom conditions, single-arc and single-fraction 2400 MU/min FFF RapidArc lung stereotactic body radiation therapy is susceptible to interplay. Two arcs and ≥2 fractions reduced the effect to a level that appeared unlikely to be clinically significant.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Kinetic analysis of Cas12a and Cas13a RNA-Guided nucleases for development of improved CRISPR-Based diagnostics

Bacterial CRISPR systems provide acquired immunity against invading nucleic acids by activating RNA-programmable RNases and DNases. Cas13a and Cas12a enzymes bound to CRISPR RNA (crRNA) recognize specific nucleic acid targets, initiating cleavage of the targets as well as non-target (trans) nucleic acids. Here, we examine the kinetics of single-turnover target and multi-turnover trans-nuclease activities of both enzymes. High-turnover, non-specific Cas13a trans-RNase activity is coupled to rapid binding of target RNA. By contrast, low-turnover Cas12a trans-nuclease activity is coupled to relatively slow cleavage of target DNA, selective for DNA over RNA, indifferent to base identity, and preferential for single-stranded substrates. Combining multiple crRNA increases detection sensitivity of targets, an approach we use to quantify pathogen DNA in samples from patients suspected of Buruli ulcer disease. Results reveal that these enzymes are kinetically adapted to play distinct roles in bacterial adaptive immunity and show how kinetic analysis can be applied to CRISPR-based diagnostics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Volatile particles measured by vapor-particle separator

Vapor-Particle Separator (VPS) is a new technology developed for characterization of the volatile fraction of particulate matter in a combustion aerosol population. VPS incorporates a novel metallic membrane and operates in a cross-flow filtration mode for separation of vapor and solid (i.e. non-volatile) particles. Demonstration of the VPS technology on aircraft engine-emitted particles has led to the improvement of the technology and increased confidence on the robustness of its field performance. In this study, the performance of the VPS was evaluated against the Particle Measurement Programme (PMP) volatile particle remover (VPR), a standardized device used in heavy duty diesel engines for separation and characterization of non-volatile particulate matter. Using tetracontane particles in the laboratory reveals that the VPS performed reasonably well in removing the volatile species. In the field conditions, a single-mode particle size distribution was found for emitted particles from a T63 turboshaft engine at both idle and cruise engine power conditions. Removal of the volatile T63 engine particles by the VPS was consistent with that of PMP VPR. In tests on an F117 turbofan engine, the size distribution at the idle (4% rated) engine power condition was found to be bimodal, with the first mode consisting of particles smaller than 10nm, which are believed to be mostly semi-volatile particles, while the second mode of larger size was a mixture of semi-volatile and non-volatile particles. The distribution was single modal at the 33% rated engine power with no secondary mode observed. Altogether, for particles emitted by both engines, the removal efficiency of the VPS appears to surpass that of the PMP VPR by 8-10%.

42 ENGINEERING↗
DOE OSTI2022

Designing sequence-defined peptoids for fibrillar self-assembly and silicification

In the biological environment, mineral crystals exquisitely controlled by biomacromolecules often show intricate hierarchical structures and superior mechanical properties. Among these biominerals, spicules, hybrid silica/protein superstructures serving as skeletal elements in demosponges, represent an excellent example for motivating the synthesis of silica materials. Herein, by designing sequence-defined peptoids containing side chains with a strong binding to silica, we demonstrated that self-assembly of these peptoids into fiber structures enables the mimicking of both biocatalytic and templating functions of silicatein filaments for the formation of silica fibers at near-neutral pH and ambient temperature. We further showed that the presence of amino groups is significant for the nucleation of silica on self-assembled peptoid nanofibers. Molecular dynamics simulation further confirmed that having silica-binding of amino side chains is critical for self-assembled peptoid fibers in triggering silica formation. Here, we demonstrated that tuning inter-peptoid interactions by varying carboxyl and amino side chains significantly influences the assembly kinetics and final morphologies of peptoid assemblies as scaffolds for directing silica mineralization to form silica spheres, fibers, and sheets. The formation of silica shell on peptoid fibers increased the mechanical property of peptoid hydrogel materials by nearly 1000-fold, highlighting the great potential of using silicification to enhance the mechanical property of hydrogel materials for applications including tissue engineering. Since peptoids are highly robust and programmable, we expect that self-assembly of peptoids containing solid-binding side chains into hierarchical materials opens new opportunities in the design and synthesis of highly tunable scaffolds that direct the formation of composite nanomaterials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Accelerating solidification process simulation for large-sized system of liquid metal atoms using GPU with CUDA

Molecular dynamics simulation is a powerful tool to simulate and analyze complex physical processes and phenomena at atomic characteristic for predicting the natural time-evolution of a system of atoms. Precise simulation of physical processes has strong requirements both in the simulation size and computing timescale. Therefore, finding available computing resources is crucial to accelerate computation. However, a tremendous computational resource (GPGPU) are recently being utilized for general purpose computing due to its high performance of floating-point arithmetic operation, wide memory bandwidth and enhanced programmability. As for the most time-consuming component in MD simulation calculation during the case of studying liquid metal solidification processes, this paper presents a fine-grained spatial decomposition method to accelerate the computation of update of neighbor lists and interaction force calculation by take advantage of modern graphics processors units (GPU), enlarging the scale of the simulation system to a simulation system involving 10 000 000 atoms. In addition, a number of evaluations and tests, ranging from executions on different precision enabled-CUDA versions, over various types of GPU (NVIDIA 480GTX, 580GTX and M2050) to CPU clusters with different number of CPU cores are discussed. The experimental results demonstrate that GPU-based calculations are typically 9∼11 times faster than the corresponding sequential execution and approximately 1.5∼2 times faster than 16 CPU cores clusters implementations. On the basis of the simulated results, the comparisons between the theoretical results and the experimental ones are executed, and the good agreement between the two and more complete and larger cluster structures in the actual macroscopic materials are observed. Moreover, different nucleation and evolution mechanism of nano-clusters and nano-crystals formed in the processes of metal solidification is observed with large-sized system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Learning functional priors and posteriors from data and physics

In this work, we develop a new Bayesian framework based on deep neural networks to be able to extrapolate in space-time using historical data and to quantify uncertainties arising from both noisy and gappy data in physical problems. Specifically, the proposed approach has two stages: (1) prior learning and (2) posterior estimation. At the first stage, we employ the physics-informed Generative Adversarial Networks (PI-GAN) to learn a functional prior either from a prescribed function distribution, e.g., Gaussian process, or from historical data and physics. At the second stage, we employ the Hamiltonian Monte Carlo (HMC) method to estimate the posterior in the latent space of PI-GANs. In addition, we use two different approaches to encode the physics: (1) automatic differentiation, used in the physicsinformed neural networks (PINNs) for scenarios with explicitly known partial differential equations (PDEs), and (2) operator regression using the deep operator network (DeepONet) for PDE-agnostic scenarios. We then test the proposed method for (1) meta-learning for one-dimensional regression, and forward/inverse PDE problems (combined with PINNs); (2) PDE-agnostic physical problems (combined with DeepONet), e.g., fractional diffusion as well as saturated stochastic (100-dimensional) flows in heterogeneous porous media; and (3) spatial-temporal regression problems, i.e., inference of a marine riser displacement field using experimental data from the Norwegian Deepwater Programme (NDP). The results demonstrate that the proposed approach can provide accurate predictions as well as uncertainty quantification given very limited scattered and noisy data, since historical data could be available to provide informative priors. In summary, the proposed method is capable of learning flexible functional priors, e.g., both Gaussian and non-Gaussian process, and can be readily extended to big data problems by enabling mini-batch training using stochastic HMC or normalizing flows since the latent space is generally characterized as low dimensional.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

UQpy: A general purpose Python package and development environment for uncertainty quantification

In this paper, we present the UQpy software toolbox, an open-source Python package for general uncertainty quantification (UQ) in mathematical and physical systems. The software serves as both a user-ready toolbox that includes many of the latest methods for UQ in computational modeling and a convenient development environment for Python programmers advancing the field of UQ. The paper presents an introduction to the software's architecture and existing capabilities, divided in the code in a set of modules centered around different UQ tasks such as sampling methods, generation of random processes and random fields, probabilistic inverse modeling, reliability analysis, surrogate modeling, and active learning. The paper also highlights the importance of the RunModel module, which is used to drive simulations in the uncertainty analyses performed in UQpy. This module conveniently allows the user to define computational models directly in Python, or to run simulations from a third-party software in serial or in parallel. To illustrate the various capabilities, two examples are tracked throughout the paper and analyzed repeatedly for various UQ tasks. The first is a Python model solving a nonlinear structural dynamics problem, used to illustrate UQpy's capabilities in sampling and forward propagation of high dimensional random vectors (stochastic processes), and probabilistic inference. The second model is a third-party Abaqus finite element model solving the thermomechanical response of a beam structure. This example is used to illustrate UQpy's capabilities in variance reduction sampling techniques, reliability analysis, surrogate modeling and active learning techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Fine-grained Asynchronous Bulk Synchronous parallelism model for PGAS applications

The Partitioned Global Address Space (PGAS) model is well suited for executing irregular applications on cluster-based systems, due to its efficient support for short, one-sided messages. Separately, the actor model has been gaining popularity as a productive asynchronous message-passing approach for distributed objects in enterprise and cloud computing platforms, typically implemented in languages such as Erlang, Scala or Rust. To the best of our knowledge, there has been no past work on using the actor model to deliver both productivity and scalability to irregular PGAS applications with large number of small messages. In this paper, we introduce a new programming system for PGAS applications, in which point-to-point remote operations can be expressed as fine-grained asynchronous actor messages. In our approach, the programmer does not need to worry about programming complexities related to message aggregation and termination detection. Our approach can be viewed as extending the classical Bulk Synchronous Parallelism model with fine-grained asynchronous communications within a phase or superstep. Here, we believe that our approach offers a desirable point in the productivity-performance space for PGAS applications, with more scalable performance and higher productivity relative to past approaches. Specifically, for seven irregular mini-applications from the Bale Kernels and three graph kernels executed using 2048 cores in the NERSC Cori system, our approach shows geometric mean performance improvements of ≥ 20X relative to standard PGAS versions (UPC and OpenSHMEM) while maintaining comparable productivity to those versions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Kokkos: Enabling manycore performance portability through polymorphic memory access patterns

The manycore revolution can be characterized by increasing thread counts, decreasing memory per thread, and diversity of continually evolving manycore architectures. High performance computing (HPC) applications and libraries must exploit increasingly finer levels of parallelism within their codes to sustain scalability on these devices. We found that a major obstacle to performance portability is the diverse and conflicting set of constraints on memory access patterns across devices. Contemporary portable programming models address manycore parallelism (e.g., OpenMP, OpenACC, OpenCL) but fail to address memory access patterns. The Kokkos C++ library enables applications and domain libraries to achieve performance portability on diverse manycore architectures by unifying abstractions for both fine-grain data parallelism and memory access patterns. In this paper we describe Kokkos’ abstractions, summarize its application programmer interface (API), present performance results for unit-test kernels and mini-applications, and outline an incremental strategy for migrating legacy C++ codes to Kokkos. Furthermore, the Kokkos library is under active research and development to incorporate capabilities from new generations of manycore architectures, and to address a growing list of applications and domain libraries.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Parallel processing of filtered queries in attributed semantic graphs

Execution of complex analytic queries on massive semantic graphs is a challenging problem in big-data analytics that requires high-performance parallel computing. In a semantic graph, vertices and edges carry attributes of various types and the analytic queries typically depend on the values of these attributes. Thus, the computation must view the graph through a filter that passes only those individual vertices and edges of interest. Previous investigations have developed Knowledge Discovery Toolbox (KDT), a sophisticated Python library for parallel graph computations. In KDT, the user can write custom graph algorithms by specifying operations between edges and vertices ( semiring operations ). The user can also customize existing graph algorithms by writing filters. While the high-level language for this customization enables domain scientists to productively express their graph analytics requirements, the customized queries perform poorly due to the overhead of having to call into the Python virtual machine for each vertex and edge. In this work, we use the Selective Embedded Just-In-Time Specialization (SEJITS) approach to automatically translate semiring operations and filters defined by programmers into a lower-level efficiency language, bypassing the upcall into Python. We evaluate our approach by comparing it with the high-performance Combinatorial BLAS engine and show that our approach combines the benefits of programming in a high-level language with executing in a low-level parallel environment. We increase the system's flexibility by developing techniques that provide users with the ability to define new vertex and edge types from Python. We also present a new Roofline model for graph traversals and show that we achieve performance that is significantly closer to the bounds suggested by the Roofline. Finally, to further understand the complex interaction with the underlying architecture, we present an analysis using performance counters that quantifies the improvement in hardware behavior in the context our SEJITS methodology. Comprehensively, we demonstrate the first known solution to the problem of obtaining high performance from a productivity language when applying graph algorithms selectively on semantic graphs with hundreds of millions of edges and scaling to thousands of processors for graphs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

MATAR: A performance portability and productivity implementation of data-oriented design with Kokkos

There is a need for simple, fast, and memory-efficient multidimensional data structures for dense and sparse storage that arise with numerical methods and in software applications. The data structures must perform equally well across multiple computer architectures, including CPUs and GPUs. For this purpose, we developed MATAR, a C++ software library that allows for simple creation and use of intricate data structures that is also portable across disparate architectures using Kokkos. Here, the performance aspect is achieved by forcing contiguous memory layout (or as close to contiguous as possible) for multidimensional and multi-size dense or sparse MATrix and ARray (hence, MATAR) types. Our results show that MATAR has the capability to improve memory utilization, performance, and programmer productivity in scientific computing. This is achieved by fitting more work into the available memory, minimizing memory loads required, and by loading memory in the most efficient order. This document describes the purpose of the work, the implementation of each of the data types, and the resulting performance both in some simple baseline test cases and in an application code.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Nebo: An efficient, parallel, and portable domain-specific language for numerically solving partial differential equations

This study presents Nebo, a declarative domain-specific language embedded in C++ for discretizing partial differential equations for transport phenomena on multiple architectures. Application programmers use Nebo to write code that appears sequential but can be run in parallel, without editing the code. Currently Nebo supports single-thread execution, multi-thread execution, and many-core (GPU-based) execution. With single-thread execution, Nebo performs on par with code written by domain experts. With multi-thread execution, Nebo can linearly scale (with roughly 90% efficiency) up to 12 cores, compared to its single-thread execution. Moreover, Nebo’s many-core execution can be over 140x faster than its single-thread execution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Creep and microstructural processes in a low-alloy 2.25%Cr1.6%W steel (ASTM Grade 23)

A low-alloy 2.25%Cr1%Mo steel (ASTM Grade 22) has been greatly improved by the substitution of almost all of the 1%Mo by 1.6%W. The improved material has been standardized as P/T23 steel (Fe–2.25Cr–1.6W–0.25V–0.05Nb–0.07C). The present investigation was conducted on T23 steel in an effort to obtain a more complete description and understanding of the role of the microstructural evolution and deformation processes in high-temperature creep. Constant load tensile creep tests were carried out in an argon atmosphere in the temperature range 500–650 °C at stresses ranging from 50 to 400 MPa. It was found that the diffusion in the matrix lattice is the creep-rate controlling process. The results of an extensive transmission electron microscopy (TEM) analysis programme to investigate microstructure evolution as a function of temperature are described and compared with the thermodynamic calculations using the software package Thermo-Calc. The significant creep-strength drop of T23 steel after long-term creep exposures can be explained by the decrease in dislocation hardening, precipitation hardening and solid solution hardening due to the instability of the microstructure at high temperature. - Highlights: • The constant load creep tests of T23 steel were carried out at 500–650 °C. • The stress exponents of the creep rate correspond to power law (dislocation) creep. • Diffusion in the matrix lattice is the creep-rate controlling process. • The microstructure instability is the main creep degradation process in T23 steel.

36 MATERIALS SCIENCE↗
DOE OSTI2013

An overview—Functional nanomaterials for lithium rechargeable batteries, supercapacitors, hydrogen storage, and fuel cells

Graphical abstract: Nanomaterials play important role in lithium ion batteries, supercapacitors, hydrogen storage and fuel cells. - Highlights: • Nanomaterials play important role for lithium rechargeable batteries. • Nanostructured materials increase the capacitance of supercapacitors. • Nanostructure improves the hydrogenation/dehydrogenation of hydrogen storage materials. • Nanomaterials enhance the electrocatalytic activity of the catalysts in fuel cells. - Abstract: There is tremendous worldwide interest in functional nanostructured materials, which are the advanced nanotechnology materials with internal or external dimensions on the order of nanometers. Their extremely small dimensions make these materials unique and promising for clean energy applications such as lithium ion batteries, supercapacitors, hydrogen storage, fuel cells, and other applications. This paper will highlight the development of new approaches to study the relationships between the structure and the physical, chemical, and electrochemical properties of functional nanostructured materials. The Energy Materials Research Programme at the Institute for Superconducting and Electronic Materials, the University of Wollongong, has been focused on the synthesis, characterization, and applications of functional nanomaterials, including nanoparticles, nanotubes, nanowires, nanoporous materials, and nanocomposites. The emphases are placed on advanced nanotechnology, design, and control of the composition, morphology, nanostructure, and functionality of the nanomaterials, and on the subsequent applications of these materials to areas including lithium ion batteries, supercapacitors, hydrogen storage, and fuel cells.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Exploring the structure of high temperature, iron-bearing liquids

This paper describes the direct measurements of the structure of iron-bearing liquids using a combination of containerless techniques and in–situ high energy x-ray diffraction. These capabilities provide data that is important to help model and optimize processes such as smelting, steel making, and controlling slag chemistry. A successful programme of liquid studies has been undertaken and the Advanced Photon Source using these combined techniques which include the provision of gas mixing and the control of pO₂ and the changing influence of mixed valance elements. It is possible to combine rapid image acquisition with quenching of liquids to obtain the full diffraction patterns of deeply supercooled liquids and the metastable supercooled liquid regime, where the liquid structures and viscosity change most dramatically, can also be explored.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Mitigated FPGA design of multi-gigabit transceivers for application in high radiation environments of High Energy Physics experiments

SRAM-ba8ed Field Programmable Gate Array (FPGA) logic devices arc very attractive in applications where high data throughput is needed, such as the latest generation of High Energy Physics (HEP) experiments. FPGAs have been rarely used in such experiments because of their sensitivity to radiation. The present paper proposes a mitigation approach applied to commercial FPGA devices to meet the reliability requirements for the front-end electronics of the Liquid Argon (LAr) electromagnetic calorimeter of the ATLAS experiment, located at CERN. Particular attention will be devoted to define a proper mitigation scheme of the multi-gigabit transceivers embedded in the FPGA, which is a critical part of the LAr data acquisition chain. A demonstrator board is being developed to validate the proposed methodology. :!\litigation techniques such as Triple Modular Redundancy (T:t\IR) and scrubbing will be used to increase the robustness of the design and to maximize the fault tolerance from Single-Event Upsets (SEUs).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Evaluations of secondary cancer risk in spine radiotherapy using 3DCRT, IMRT, and VMAT: A phantom study

This study evaluated the secondary cancer risk from volumetric-modulated arc therapy (VMAT) for spine radiotherapy compared with intensity-modulated radiotherapy (IMRT) and 3-dimensional conformal radiotherapy (3DCRT). Computed tomography images of an Radiological Physics Center spine anthropomorphic phantom were exported to a treatment planning system (Pinnacle{sup 3}, version 9.4). Radiation treatment plans for spine were prepared using VMAT (dual-arc), 7-field IMRT (beam angles: 110°, 130°, 150°, 180°, 210°, 230°, and 250°), and 4-field 3DCRT technique. The mean and maximum doses, dose-volume histograms, and volumes receiving more than 2 and 4 Gy to organs at risk (OARs) were calculated and compared. The lifetime risk for secondary cancers was estimated according to the National Cancer Registry Programme Report 116. VMAT delivered the lowest maximum dose to the esophagus (4.03 Gy), bone (8.11 Gy), heart (2.11 Gy), spinal cord (6.45 Gy), and whole lung (5.66 Gy) as compared with other techniques (IMRT and 3DCRT). The volumes of OAR (esophagus) receiving more than 4 Gy were 0% for VMAT, 27.06% for IMRT, and up to 32.35% for 3DCRT. The estimated risk for secondary cancer in the respective OAR is considerably lower in VMAT compared with other techniques. The results of maximum doses and volumes of OARs suggest that the risk of secondary cancer induction for the spine in VMAT is lower than IMRT and 3DCRT, whereas VMAT has the best target coverage compared with the other techniques.

61 RADIATION PROTECTION AND DOSIMETRY↗