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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Augmented DNA Nanoarchitectures: A Structural Library of 3D Self-Assembling Tensegrity Triangle Variants

The DNA tensegrity triangle is known to reliably self-assemble into a 3D rhombohedral crystalline lattice via sticky-end cohesion. Here, in this study, the library of accessible motifs is expanded through covalent extensions of intertriangle regions and sticky-end-coordinated linkages of adjacent triangles with double helical segments using both geometrically symmetric and asymmetric configurations. The molecular structures of 18 self-assembled architectures at resolutions of 3.32–9.32 Å are reported; the observed cell dimensions, cavity sizes, and cross-sectional areas agree with theoretical expectations. These data demonstrate that fine control over triclinic and rhombohedral crystal parameters and the customizability of more complex 3D DNA lattices are attainable via rational design. It is anticipated that augmented DNA architectures may be fine-tuned for the self-assembly of designer nanocages, guest–host complexes, and proscriptive 3D nanomaterials, as originally envisioned. Finally, designer asymmetric crystalline building blocks can be seen as a first step toward controlling and encoding information in three dimensions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Supporting information for Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa

In this work, we use few-shot learning to segment the body and vein architecture of P. trichocarpa leaves from high-resolution scans obtained in the UC Davis common garden. Leaf and vein segmentation are formulated as separate tasks, in which convolutional neural networks (CNNs) are used to iteratively expand partial segmentations until reaching stopping criteria. Our leaf and vein segmentation approaches use just 50 and 8 manually traced images for training, respectively, and are applied to a set of 2,634 top and bottom leaf scans. We show that both methods achieve high segmentation accuracy, in some cases exceeding even human-level segmentation. The leaf and vein segmentations are subsequently used to extract 68 morphological traits using traditional open-source image processing tools, which are validated using real-world physical measurements. For a biological perspective, we perform a genome-wide association study using the vein density trait to discover novel genetic architectures associated with multiple physiological processes relating to leaf development and function. In addition to sharing all of the few-shot learning code (see https://github.com/jlager/few-shot-leaf-segmentation), we are releasing all images, manual segmentations, model predictions, 68 extracted leaf phenotypes, and a new set of SNPs called against the v4 P. trichocarpa genome for 1,419 genotypes. The data folder includes all images, ground truth segmentations, predicted segmentations, and extracted leaf traits. All images encode the sample ID in the file name by indicating the treatment, block, row, position, and leaf side, respectively. For example, the file, C_1_1_2_bot.jpeg, indicates the control treatment, block 1, row 1, position 2, and the bottom side of the leaf. Tabulated results include position IDs as well as the corresponding genotype IDs. The images folder includes the 2,906 high-resolution leaf scans taken in the field. The leaf_masks folder includes 50 ground truth segmentations used for training the leaf tracing algorithm. The leaf_preds folder includes the 2,906 predicted segmentations from the leaf tracing algorithm. The vein_masks folder includes 8 ground truth segmentations used for training the vein growing algorithm. The vein_preds folder includes the 1,453 predicted segmentations from the vein growing algorithm. The vein_probs folder includes the 1,453 predicted probability maps from the vein growing algorithm before thresholding. The genomes folder includes the set of SNPs called against the v4 P. trichocarpa genome for 1,419 genotypes with a README file detailing the steps taken. The results folder includes: (i) raw values of the 68 predicted leaf traits in digital_traits.tsv, (ii) manually measured values of petiole length and width in manual_traits.tsv, (iii) thin plate spline (TPS) adjusted values of the vein density trait in vein_density_tps_adj.tsv, (iv) best linear unbiased prediction (BLUP) adjusted values of the vein density trait in vein_density_blups.tsv, and (v) GWAS results for the vein density trait, including chromosome positions and corresponding P values, in gwas_results.csv.

09 BIOMASS FUELS↗
DOE OSTI2025

PDF Entity Annotation Tool (PEAT)

While different text mining approaches – including the use of Artificial Intelligence (AI) and other machine based methods - continue to expand at a rapid pace, the tools used by researchers to create the labeled datasets required for training, modeling, and evaluation remain rudimentary. Labeled datasets contain the target attributes the machine is going to learn; for example, training an algorithm to delineate between images of a car or truck would generally require a set of images with a quantitative description of the underlying features of each vehicle type. Development of labeled textual data that can be used to build natural language machine learning models for scientific literature is not currently integrated into existing manual workflows used by domain experts. Published literature is rich with important information, such as different types of embedded text, plots, and tables that can all be used as inputs to train ML/natural language processing (NLP) models, when extracted and prepared in machine readable formats. Currently, both normalized data extraction of use to domain experts and extraction to support development of ML/NLP models are labor intensive and cumbersome manual processes. Automatic extraction of data and information from formats such as PDFs that are optimized for layout and human readability, not machine readability. The PDF (Portable Document Format) Entity Annotation Tool (PEAT) was developed with the goal of allowing users to annotate publications within their current print format, while also allowing those annotations to be captured in a machine-readable format. One of the main issues with traditional annotation tools is that they require transforming the PDF into plain text to facilitate the annotation process. While doing so lessens the technical challenges of annotating data, the user loses all structure and provenance that was inherent in the underlying PDF. Also, textual data extraction from PDFs can be an error prone process. Challenges include identifying sequential blocks of text and a multitude of document formats (multiple columns, font encodings, etc.). As a result of these challenges, using existing tools for development of NLP/ML models directly from PDFs is difficult because the generated outputs are not interoperable. We created a system that allows annotations to be completed on the original PDF document structure, with no plain text extraction. The result is an application that allows for easier and more accurate annotations. In addition, by including a feature that grants the user the ability to easily create a schema, we have developed a system that can be used to annotate text for different domain-centric schemas of relevance to subject matter experts. Different knowledge domains require distinct schemas and annotation tags to support machine learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Building blocks for subleading helicity operators

On-shell helicity methods provide powerful tools for determining scattering amplitudes, which have a one-to-one correspondence with leading power helicity operators in the Soft-Collinear Effective Theory (SCET) away from singular regions of phase space. We show that helicity based operators are also useful for enumerating power suppressed SCET operators, which encode subleading amplitude information about singular limits. In particular, we present a complete set of scalar helicity building blocks that are valid for constructing operators at any order in the SCET power expansion. In conclusion, we also describe an interesting angular momentum selection rule that restricts how these building blocks can be assembled.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature’s own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and “smart” architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Furthermore, continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Generation of a chromosome-scale genome assembly of the insect-repellent terpenoid-producing Lamiaceae species, Callicarpa americana

Plants exhibit wide chemical diversity due to the production of specialized metabolites that function as pollinator attractants, defensive compounds, and signaling molecules. Lamiaceae (mints) are known for their chemodiversity and have been cultivated for use as culinary herbs, as well as sources of insect repellents, health-promoting compounds, and fragrance. We report the chromosome-scale genome assembly of Callicarpa americana L. (American beautyberry), a species within the early-diverging Callicarpoideae clade of Lamiaceae, known for its metallic purple fruits and use as an insect repellent due to its production of terpenoids. Using long-read sequencing and Hi-C scaffolding, we generated a 506.1-Mb assembly spanning 17 pseudomolecules with N50 contig and N50 scaffold sizes of 7.5 and 29.0 Mb, respectively. In all, 32,164 genes were annotated, including 53 candidate terpene synthases and 47 putative clusters of specialized metabolite biosynthetic pathways. Our analyses revealed 3 putative whole-genome duplication events, which, together with local tandem duplications, contributed to gene family expansion of terpene synthases. Kolavenyl diphosphate is a gateway to many of the bioactive terpenoids in C. americana; experimental validation confirmed that CamTPS2 encodes kolavenyl diphosphate synthase. Syntenic analyses with Tectona grandis L. f. (teak), a member of the Tectonoideae clade of Lamiaceae known for exceptionally strong wood resistant to insects, revealed 963 collinear blocks and 21,297 C. americana syntelogs. Access to the C. americana genome provides a road map for rapid discovery of genes encoding plant-derived agrichemicals and a key resource for understanding the evolution of chemical diversity in Lamiaceae.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Programming Function via Soft Materials (PFvSM) (Final Report)

The PFvSM cluster at the University of Illinois greatly deepened the science of soft materials and expanded the means for embedding within them unprecedented functionalities. We pioneered a mesoscopic approach to materials design that combined leading competencies in fundamental experimental and theoretical soft matter science and fabrication that powerfully establishes equilibrium and nonequilibrium assembly of novel and complex building blocks as a preeminent contributor to progress in materials chemistry. Our cluster’s vision was an integrated program of work centered on soft matter science with the ultimate goal of impacting important energy technologies. As elementary units we employed function-encoded colloids, nanoparticles, large mesh fibrillar gels, deterministically fabricated networks and hybrid objects. These materials were employed as building blocks for diverse model systems that allowed fundamental soft matter principles of wide relevance to be established. Their assembly into materials with useful functional behavior provided new approaches to challenging problems in basic energy science including energy harvesting, transport and storage. To achieve these goals the cluster brought together a team of recognized world-leaders in materials synthesis and processing, in-situ characterization, theory and modeling, and materials design, integration and fabrication

36 MATERIALS SCIENCE↗
DOE OSTI2017

Chromosome-level genome assembly and transcriptome of the green alga Chromochloris zofingiensis illuminates astaxanthin production

Microalgae have potential to help meet energy and food demands without exacerbating environmental problems. There is interest in the unicellular green alga Chromochloris zofingiensis, because it produces lipids for biofuels and a highly valuable carotenoid nutraceutical, astaxanthin. Here, to advance understanding of its biology and facilitate commercial development, we present a C. zofingiensis chromosome-level nuclear genome, organelle genomes, and transcriptome from diverse growth conditions. The assembly, derived from a combination of short- and long-read sequencing in conjunction with optical mapping, revealed a compact genome of ~58 Mbp distributed over 19 chromosomes containing 15,274 predicted protein-coding genes. The genome has uniform gene density over chromosomes, low repetitive sequence content (~6%), and a high fraction of protein-coding sequence (~39%) with relatively long coding exons and few coding introns. Functional annotation of gene models identified orthologous families for the majority (~73%) of genes. Synteny analysis uncovered localized but scrambled blocks of genes in putative orthologous relationships with other green algae. Two genes encoding beta-ketolase (BKT), the key enzyme synthesizing astaxanthin, were found in the genome, and both were up-regulated by high light. Isolation and molecular analysis of astaxanthin-deficient mutants showed that BKT1 is required for the production of astaxanthin. Moreover, the transcriptome under high light exposure revealed candidate genes that could be involved in critical yet missing steps of astaxanthin biosynthesis, including ABC transporters, cytochrome P450 enzymes, and an acyltransferase. Finally, the high-quality genome and transcriptome provide insight into the green algal lineage and carotenoid production.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Deletion of either the regulatory gene ara1 or metabolic gene xki1 in Trichoderma reesei leads to increased CAZyme gene expression on crude plant biomass

Background:Trichoderma reesei is one of the major producers of enzymes for the conversion of plant biomass to sustainable fuels and chemicals. Crude plant biomass can induce the production of CAZymes in T. reesei, but there is limited understanding of how the transcriptional response to crude plant biomass is regulated. In addition, it is unknown whether induction on untreated recalcitrant crude plant biomass (with a large diversity of inducers) can be sustained for longer. We investigated the transcriptomic response of T. reesei to the two industrial feedstocks, corn stover (CS) and soybean hulls (SBH), over time (4 h, 24 h and 48 h), and its regulatory basis using transcription factor deletion mutants (Δxyr1 and Δara1). We also investigated whether deletion of a xylulokinase gene (Δxki1) from the pentose catabolic pathway that converts potential inducers could lead to increased CAZyme gene expression. Results:By analyzing the transcriptomic responses using clustering as well as differential and cumulative expression of plant biomass degrading CAZymes, we found that corn stover induced a broader range and higher expression of CAZymes in T. reesei, while SBH induced more pectinolytic and mannanolytic transcripts. XYR1 was the major TF regulating CS utilization, likely due to the significant amount of d-xylose in this substrate. In contrast, ARA1 had a stronger effect on SBH utilization, which correlates with a higher abundance of l-arabinose in SBH that activates ARA1. Blocking pentose catabolism by deletion of xki1 led to higher expression of CAZyme encoding genes on both substrates at later time points. Surprisingly, this was also observed for Δara1 at later time points. Many of these genes were XYR1 regulated, suggesting that inducers for this regulator accumulated over time on both substrates. Conclusion:Our data demonstrates the complexity of the regulatory system related to plant biomass degradation in T. reesei and the effect the feedstock composition has on this. Furthermore, this dataset provides leads to improve the efficiency of a T. reesei enzyme cocktail, such as by the choice of substrate or by deleting xki1 to obtain higher production of plant biomass degrading CAZymes.

09 BIOMASS FUELS↗
DOE OSTI2021

Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution

Recent works exploring deep learning application to dynamical systems modeling have demonstrated that embedding physical priors into neural networks can yield more effective, physically-realistic, and data-efficient models. However, in the absence of complete prior knowledge of a dynamical system's physical characteristics, determining the optimal structure and optimization strategy for these models can be difficult. In this work, we explore methods for discovering neural state space dynamics models for system identification. Starting with a design space of block-oriented state space models and structured linear maps with strong physical priors, we encode these components into a model genome alongside network structure, penalty constraints, and optimization hyperparameters. Demonstrating the overall utility of the design space, we employ an asynchronous genetic search algorithm that alternates between model selection and optimization and obtains accurate physically consistent models of three physical systems: an aerodynamics body, a continuous stirred tank reactor, and a two tank interacting system.

genetic algorithms, neural architecture search, ne↗
DOE OSTI2012

Inorganic arsenic represses interleukin-17A expression in human activated Th17 lymphocytes

Trivalent inorganic arsenic [As(III)] is an efficient anticancer agent used to treat patients suffering from acute promyelocytic leukemia. Recently, experimental studies have clearly demonstrated that this metalloid can also cure lymphoproliferative and/or pro-inflammatory syndromes in different murine models of chronic immune-mediated diseases. T helper (Th) 1 and Th17 lymphocytes play a central role in development of these diseases, in mice and humans, especially by secreting the potent pro-inflammatory cytokine interferon-γ and IL-17A, respectively. As(III) impairs basic functions of human T cells but its ability to modulate secretion of pro-inflammatory cytokines by differentiated Th lymphocytes is unknown. In the present study, we demonstrate that As(III), used at concentrations clinically achievable in plasma of patients, has no effect on the secretion of interferon-γ from Th1 cells but almost totally blocks the expression and the release of IL-17A from human Th17 lymphocytes co-stimulated for five days with anti-CD3 and anti-CD28 antibodies, in the presence of differentiating cytokines. In addition, As(III) specifically reduces mRNA levels of the retinoic-related orphan receptor (ROR)C gene which encodes RORγt, a key transcription factor controlling optimal IL-17 expression in fully differentiated Th17 cells. The metalloid also blocks initial expression of IL-17 gene induced by the co-stimulation, probably in part by impairing activation of the JNK/c-Jun pathway. In conclusion, our results demonstrate that As(III) represses expression of the major pro-inflammatory cytokine IL-17A produced by human Th17 lymphocytes, thus strengthening the idea that As(III) may be useful to treat inflammatory immune-mediated diseases in humans. -- Highlights: ► Arsenic inhibits secretion of IL-17A from human naïve and memory Th17 lymphocytes. ► Arsenic represses early expression of IL-17A gene in human activated T lymphocytes. ► Arsenic interferes with activation of the JNK/c-Jun pathway in human T lymphocytes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Self-assembled elastomers with molecularly encoded tissue-like softness, strain-adaptive stiffening and coloration

The invention generally relates to linear-bottlebrush-linear copolymer blocks and methods of making and using same. The disclosed copolymer blocks can be useful in, for example, the formation of polymer networks that replicate biological stress-strain behavior. This abstract is intended as a scanning tool for purposes of searching in the particular art and is not intended to be limiting of the present invention.

Sheiko, Sergei↗
DOE OSTI2018

DNA-Encoded Protein Janus Nanoparticles

Asymmetric functionality and directional interactions, which are characteristic of noncentrosymmetric particles, such as Janus particles, present an opportunity to encode particles with properties, but also a great synthetic challenge. Here, we exploit the chemical anisotropy of proteins, and the versatile chemistry of DNA to synthesize a protein-based Janus nanoparticle comprised of two proteins encoded with sequence-specific nucleic acid domains, tethered together by an interprotein “DNA bond”. We use these novel nanoparticles to realize a new class of three-dimensional superlattice, only possible when two sides of the particle are modified with orthogonal oligonucleotide sequences. The low symmetry, intrinsic to Janus particles, enables the realization of unprecedented multicomponent nanoparticle superlattices with unique, hexagonal layered architectures. In addition, the interprotein “DNA bond” can be modulated to selectively expand the lattice in a single direction. The results presented herein not only emphasize the power of rationally designing nanoscale building blocks to create highly engineered colloidal crystals, but also establish a precedent for applications of multidomain DNA-encoded nanoparticles, especially in the field of colloidal crystallization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Structural and compositional complexities of hierarchical self-assembly: A hypergraph approach

Programmable self-assembly enables the construction of complex molecular, supramolecular, and crystalline architectures from well-designed building blocks. In this work, we introduce a hypergraph-based formalism, Blocks & Bonds (B&B), which generalizes classical chemical graph theory by incorporating directed and multicolored interactions, internal symmetries, and hierarchical organization. Within this framework, we develop the Structure Code (SC), a compact and versatile language for describing self-assembled architectures. We define a Kolmogorov-style structural complexity as the total information content of SC, obtained through its tokenization and Shannon information assignment. Complementing this encoding-based measure, we introduce a much simpler quantity, the compositional complexity, which depends only on the number and cumulative usage of block and bond types in the construction set. A central result of this work is a strong empirical correlation between the token-based structural complexity and the compositional complexity across all examined systems. Owing to this agreement, the compositional complexity emerges as the most practical and broadly applicable measure: it is easy to compute, requires no explicit encoding, and yet closely tracks the actual information content of structurally diverse architectures. Applications to molecular systems (ethylene glycol and glucose), DNA-origami lattices, and crystalline assemblies show that B&B hypergraphs provide a unified, scalable, and information-efficient representation of structural organization, naturally capturing symmetry, modularity, and stereochemistry. This framework establishes a quantitative foundation for complexity-aware classification and inverse design of programmable matter.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Structurally Ordered Nanowire Formation from Co-Assembly of DNA Origami and Collagen-Mimetic Peptides

In this paper, we describe the co-assembly of two different building units: collagen-mimetic peptides and DNA origami. Two peptides CP ++ and sCP ++ are designed with a sequence comprising a central block (Pro-Hyp-Gly) and two positively charged domains (Pro-Arg-Gly) at both N- and C-termini. Co-assembly of peptides and DNA origami two-layer (TL) nanosheets affords the formation of one-dimensional nanowires with repeating periodicity of similar to 10 nm. Structural analyses suggest a face-to-face stacking of DNA nanosheets with peptides aligned perpendicularly to the sheet surfaces. We demonstrate the potential of selective peptide-DNA association between face-to-face and edge-to-edge packing by tailoring the size of DNA nanostructures. Finally, this study presents an attractive strategy to create hybrid biomolecular assemblies from peptide and DNA-based building blocks that takes advantage of the intrinsic chemical and physical properties of the respective components to encode structural and, potentially, functional complexity within readily accessible biomimetic materials.

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

Development of a novel depth of interaction PET detector using highly multiplexed G-APD cross-strip encoding

Purpose: The aim of this study was to develop a prototype PET detector module for a combined small animal positron emission tomography and magnetic resonance imaging (PET/MRI) system. The most important factor for small animal imaging applications is the detection sensitivity of the PET camera, which can be optimized by utilizing longer scintillation crystals. At the same time, small animal PET systems must yield a high spatial resolution. The measured object is very close to the PET detector because the bore diameter of a high field animal MR scanner is limited. When used in combination with long scintillation crystals, these small-bore PET systems generate parallax errors that ultimately lead to a decreased spatial resolution. Thus, we developed a depth of interaction (DoI) encoding PET detector module that has a uniform spatial resolution across the whole field of view (FOV), high detection sensitivity, compactness, and insensitivity to magnetic fields. Methods: The approach was based on Geiger mode avalanche photodiode (G-APD) detectors with cross-strip encoding. The number of readout channels was reduced by a factor of 36 for the chosen block elements. Two 12 × 2 G-APD strip arrays (25μm cells) were placed perpendicular on each face of a 12 × 12 lutetium oxyorthosilicate crystal block with a crystal size of 1.55 × 1.55 × 20 mm. The strip arrays were multiplexed into two channels and used to calculate the x, y coordinates for each array and the deposited energy. The DoI was measured in step sizes of 1.8 mm by a collimated {sup 18}F source. The coincident resolved time (CRT) was analyzed at all DoI positions by acquiring the waveform for each event and applying a digital leading edge discriminator. Results: All 144 crystals were well resolved in the crystal flood map. The average full width half maximum (FWHM) energy resolution of the detector was 12.8% ± 1.5% with a FWHM CRT of 1.14 ± 0.02 ns. The average FWHM DoI resolution over 12 crystals was 2.90 ± 0.15 mm. Conclusions: The novel DoI PET detector, which is based on strip G-APD arrays, yielded a DoI resolution of 2.9 mm and excellent timing and energy resolution. Its high multiplexing factor reduces the number of electronic channels. Thus, this cross-strip approach enables low-cost, high-performance PET detectors for dedicated small animal PET and PET/MRI and potentially clinical PET/MRI systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Per-page control of physical address space distribution among memory modules

Systems, apparatuses, and methods for implementing per-page control of physical address space distribution among memory modules are disclosed. A computing system includes a plurality of processing units coupled to a plurality of memory modules. A determination is made as to which physical address space distribution granularity to implement for physical memory pages allocated for a first data structure. The determination can be made on a per-data-structure basis (e.g., file, page, block, etc.) or on a per-application-basis. A physical address space distribution granularity is encoded as a property of each physical memory page allocated for the first data structure, and physical memory pages of the first data structure distributed across the plurality of memory modules based on a selected physical address space distribution granularity. Page table entries (PTEs) may be annotated with the selected physical address space distribution granularity, using an addressing mapping granularity (AMG) field of a page table entry, where the granularity may be, for example, a fine-grain distribution granularity or a coarse-grain distribution granularity.

Jayasena, Nuwan S.↗
DOE OSTI2016

SU-F-E-19: A Novel Method for TrueBeam Jaw Calibration

Purpose: A simple jaw calibration method is proposed for Varian TrueBeam using an EPID-Encoder combination that gives accurate fields sizes and a homogeneous junction dose. This benefits clinical applications such as mono-isocentric half-beam block breast cancer or head and neck cancer treatment with junction/field matching. Methods: We use EPID imager with pixel size 0.392 mm × 0.392 mm to determine the radiation jaw position as measured from radio-opaque markers aligned with the crosshair. We acquire two images with different symmetric field sizes and record each individual jaw encoder values. A linear relationship between each jaw’s position and its encoder value is established, from which we predict the encoder values that produce the jaw positions required by TrueBeam’s calibration procedure. During TrueBeam’s jaw calibration procedure, we move the jaw with the pendant to set the jaw into position using the predicted encoder value. The overall accuracy is under 0.1 mm. Results: Our in-house software analyses images and provides sub-pixel accuracy to determine field centre and radiation edges (50% dose of the profile). We verified the TrueBeam encoder provides a reliable linear relationship for each individual jaw position (R{sup 2}>0.9999) from which the encoder values necessary to set jaw calibration points (1 cm and 19 cm) are predicted. Junction matching dose inhomogeneities were improved from >±20% to <±6% using this new calibration protocol. However, one technical challenge exists for junction matching, if the collimator walkout is large. Conclusion: Our new TrueBeam jaw calibration method can systematically calibrate the jaws to crosshair within sub-pixel accuracy and provides both good junction doses and field sizes. This method does not compensate for a larger collimator walkout, but can be used as the underlying foundation for addressing the walkout issue.

60 APPLIED LIFE SCIENCES↗