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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

BETO 2021 Peer Review: 2.1.0.100 - Biochemical Platform Analysis

The objective of this project is to perform techno-economic analysis (TEA) to guide Biochemical Platform efforts, utilizing models for purposes of setting future R&D targets and tracking performance progress against those targets. Outcomes of our work are leveraged by BETO to guide program plans, as well as by other NREL/partner projects to quantify the impact of research on key technology barriers and to prioritize future efforts. This project provides high impact and relevance through establishing “bottom-up” TEA models as a basis for understanding the technical feasibility to meet “top-down” BETO cost targets. By providing a framework to translate technical performance to cost reductions in a biorefinery, our TEA models may be leveraged to maximize the efficiency of research funding towards the most economically impactful priorities, ultimately in support of BETO’s 2030 fuel cost targets below $2.5/GGE. In order to mitigate a key risk/challenge to this project in overly-constraining our analyses to a singular technology focus or TEA metric, our approach continuously re-assesses opportunities for better optimization and alternative technology pathway options, while maintaining close interaction with other BETO analysis partners. We have made numerous recent accomplishments rooted around identifying and working with the researchers to solve key technical and TEA/LCA challenges, reflected through notable improvements in State-of-Technology (SOT) updates over prior benchmarks.

biochemical↗
DOE OSTI2019

Integration of Local Knowledge is a Key for the Remediation Success of Legacy Uranium Mine Sites - 19564

A key to success, during all aspects of the remediation process on legacy uranium sites, and easily transferable between countries and projects, is the integration of local knowledge. However, before this knowledge can be integrated, local trust and project understanding must be developed. Local trust and project understanding can only be developed through a principle-centred approach to a shared project vision, two-way communication of knowledge, alignment of project goals, and understanding the local needs early in the remediation process. Once public confidence is obtained, local trust and project understanding must be maintained throughout the entirety of the remediation process. Project understanding is an ongoing endeavor that needs continuous communication and varied methods to succeed. During the management of assessment, remediation, and post remediation monitoring on the Saskatchewan Research Council's CLEANS project (remediation of 37 legacy uranium mine sites in Northern Saskatchewan, Canada), integration of local knowledge has been an instrumental tool attributing to success. During site assessment, integration of local knowledge has included the development and implementation of land use surveys to determine realistic travel and occupancy on and adjacent to these legacy sites, and site specific traditional knowledge and land use studies that have contributed to the development of a detailed knowledge of local land use, resource utilization, country food intake, and human history of the sites. The local knowledge gained during the assessment process is invaluable as it provides information required to support remediation planning, such as: valued ecosystem components, human trophic utilization, length of site occupancy, cultural value assessment, and future land use considerations. During remediation planning, local and traditional knowledge can also be incorporated through determination of plant species suitability for revegetation, development and selection of remediation options, and setting realistic sustainable development targets for local project inclusion. During site remediation operations, local involvement can be utilized through direct labour and equipment, cultural resource monitoring (i.e. archaeological and heritage protection), local logistics and historic information transfer, and stakeholder communications and engagement support. Post remediation, local involvement is an efficient mechanism for long-term monitoring, and care and maintenance activities. Although there may be differences between countries, regions and sites, the remediation process on legacy uranium mines can universally be more successful through the integration of local knowledge; while developing rights/stakeholder engagement and capacity building. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRSDate not supplied

Comparing and Automatically Optimizing the Performance of Systematic Error Correctors for TESS Light Curves

Accurate and precise removal of systematic errors from TESS light curves (lcs) is critical for exoplanet, stellar, and asteroseismology studies•Various approaches exist to correct for systematics while preserving intrinsic stellar variability (Luger et al. 2016, 2018; Hedges et al. 2021; Smith et al. 2012; Stumpe et al. 2014; Aigrain et al. 2017)•However, no comprehensive analyseshave been carried out to properly compare these approaches and determine their validity on different target types in both an individual and statistical manner•Current correctors have been usually demonstrated on their own and on limited sets of hand-picked targets•To apply these corrections more generally, it is important to compare multiple correctors on larger samples•We are particularly interested in the ability to remove scattered light contaminationfrom the Earth and the Moon, which is a key systematic for TESS

David Rapetti↗
DOE OSTI2019

Fueling strategy for controlled-autoignition engines

Methods and systems for controlling combustion performance of an engine during recompression HCCI combustion are provided. The method includes regulating a valve actuation timing and a fuel injection timing to cause a combustion phasing of at least one cylinder of the engine to approach a target combustion phasing, and estimating current combustion state information based on the combustion phasing. The current combustion state information includes at least one of a temperature, a pressure, and a pre-combustion charge composition associated with the at least one cylinder. The method further includes determining a target fuel injection amount, and determining whether the target fuel injection amount would require actuator settings that violate predetermined constraints in order to cause the combustion phasing to approach the target combustion phasing. A fuel injection amount is adjusted when the target fuel injection amount would require actuator settings that violate the predetermined constraints.

42 ENGINEERING↗
NASA NTRS1995

Long Duration Exposure Facility

The Long Duration Exposure Facility (LDEF) materials were useful for testing technical concepts that needed to be validated for the Cosmic Dust Collector Facility (CDCF) - a large instrument that was under study for inclusion on the upcoming Space Station. As it turned out, CDCF, which was approved at one point as an external payload, was ultimately canceled due to the scaling back of the Space Station capabilities. However, the study of cosmic dust is still a field of great interest for NASA, being the crucial part of at least one experiment that is still being considered for a Discovery mission. The optimum analysis of hypervelocity impacts is thus a continuing NASA concern. Two experiments, AO187-1 and AO187-2 were explicitly designed to measure the flux and composition of dust particles impacting LDEF. The former consisted of flat targets of Au and Al while the second comprised a set of 237 "capture cells" using Ge target plates covered by thin (2.5 microns) metallized plastic foils. The spacecraft maintained a constant orientation with respect to its velocity vector thereby defining leading and trailing edges that faced respectively into and away from the direction of motion. Both AO187-1 and AO187-2 had detectors on both the leading and trailing edges of LDEF. It was expected that interplanetary dust particles would dominate impacts on the trailing edge while the leading edge would be exposed to both cosmic dust and particles of orbital manmade debris. In AO187-1, the compositions of the impactors was determined by SEM-EDS techniques; in AO187-2 a sensitive surface analysis technique - SIMS - was used to determine both elemental and (some) isotopic compositions. In the initial analysis of AO187-2, it was found that SIMS was more sensitive for the analysis of thin impact debris layers than SEM-EDS. The present grant was made to determine if SIMS analysis could also be applied to impacts recorded in the Au surfaces of AO187-1. Success would extend the analysis of AO187-1 and would also contribute important technical information for the design of future cosmic dust space missions. The results obtained with this grant are incorporated in the attached paper presented at the Third LDEF Symposium and are briefly summarized here. In addition, we comment on the potential implications of this work for future research.

Source record↗
DOE OSTI2020

Composition Measurements of the LAW Phase 4 Glasses

In this report, the Savannah River National Laboratory provides results from the analyses of glass compositions for a series of simulated nuclear waste glasses fabricated at the Pacific Northwest National Laboratory. The series included quenched versions of the glasses. The resulting data will be used in the development of enhanced property/composition models for waste vitrification at Hanford. Chemical analyses were performed on a representative sample of each of the quenched glasses to allow for comparisons with the targeted compositions. For the quenched glasses from Set 1, measured concentrations of B 2 O 3 and SiO 2 were generally above the targeted values. Measured concentrations for MgO and P 2 O 5 were generally low for glasses. Measured concentrations of ZrO 2 were both below and above the targeted values for glasses. For the quenched glasses from Set 2, measured concentrations of AL 2 O 3 , B 2 O 3 , P 2 O 5 , SiO 2 , and ZrO 2 were generally below the targeted values. Measured concentrations of Na 2 0 were both below and above the targeted values for glasses. The measured concentrations of chlorine, fluorine, and SO 3 were below the targeted values for most of the study glasses, likely because of volatility during melting. Overall, there were no indications of errors in batching of the simulated waste glasses. These results can be used in further characterization of this series of glasses, including the normalization of Product Consistency Test results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Measurements of soil protist richness and community composition are influenced by primer pair, annealing temperature, and bioinformatics choices

ABSTRACT Protists are a diverse and understudied group of microbial eukaryotic organisms especially in terrestrial environments. Advances in molecular methods are increasing our understanding of the distribution and functions of these creatures; however, there is a vast array of choices researchers make including barcoding genes, primer pairs, PCR settings, and bioinformatic options that can impact the outcome of protist community surveys. Here, we tested four commonly used primer pairs targeting the V4 and V9 regions of the 18S rRNA gene using different PCR annealing temperatures and processed the sequences with different bioinformatic parameters in 10 diverse soils to evaluate how primer pair, amplification parameters, and bioinformatic choices influence the composition and richness of protist and non-protist taxa using Illumina sequencing. Our results showed that annealing temperature influenced sequencing depth and protist taxon richness for most primer pairs, and that merging forward and reverse sequencing reads for the V4 primer pairs dramatically reduced the number of sequences and taxon richness of protists. The data sets of primers that targeted the same 18S rRNA gene region (e.g., V4 or V9) had similar protist community compositions; however, data sets from primers targeting the V4 18S rRNA gene region detected a greater number of protist taxa compared to those prepared with primers targeting the V9 18S rRNA region. There was limited overlap of protist taxa between data sets targeting the two different gene regions (80/549 taxa). Together, we show that laboratory and bioinformatic choices can substantially affect the results and conclusions about protist diversity and community composition using metabarcoding. IMPORTANCE Ecosystem functioning is driven by the activity and interactions of the microbial community, in both aquatic and terrestrial environments. Protists are a group of highly diverse, mostly unicellular microbes whose identity and roles in terrestrial ecosystem ecology have been largely ignored until recently. This study highlights the importance of choices researchers make, such as primer pair, on the results and conclusions about protist diversity and community composition in soils. In order to better understand the roles protist taxa play in terrestrial ecosystems, biases in methodological and analytical choices should be understood and acknowledged.

Biotechnology & Applied Microbiology↗
DOE OSTI2014

TH-A-19A-07: The Effect of Particle Tracking Step Size Limit On Monte Carlo- Computed LET Spectrum of Therapeutic Proton Beams

Purpose: To investigate the effect of charged particle tracking step size limit in the determination of the LET spectrum of therapeutic proton beams using Monte Carlo simulations. Methods: The LET spectra at different depths in a water phantom from a 79.7 MeV spot-scanning proton beam were calculated using Geant4. Five different tracking step limits 0.5 mm, 0.1 mm, 0.05 mm, 0.01 mm and 1 μm were adopted. The field size was set to 10×10 cm{sup 2} on the isocenter plane. A 40×40×6 cm{sup 3} water phantom was modelled as the irradiation target. The voxel size was set to 1×1×0.5 mm{sup 3} to obtain high resolution results. The LET spectra were scored ranging from 0.01 keV/μm to 10{sup 4}keV/μm in the logarithm scale. In addition, the proton energy spectra at different depths were also scored. Results: The LET spectra calculated using different step size limits were compared at four depths along the Bragg curve. At any depths, the spread of the LET spectra increases with the decrease of step size limit. In the dose buildup region (z = 1.9 cm) and in the region proximal to the Bragg peak (z = 3.95 cm), the frequency mean LET does not vary with decreasing step size limit. At Bragg peak (z = 4.75 cm) and in the distal edge (z = 4.85 cm), frequency mean LET decreases with decreasing step size limit. The energy spectrum at any specified depths does not vary with the step size limit. Conclusion: The calculated LET has a spectral distribution rather than a single value at any depths along the Bragg curve and the spread of the computed spectrum depends on the tracking step limit. Incorporating the LET spectrum distribution into the robust IMPT optimization plan may provide more accurate biological dose distribution than using the dose- or fluence-averaged LET. NIH Program Project Grant P01CA021239.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

Better Plants Resources for Achieving Science Based Targets

The US Department of Energy’s (DOE’s) Better Buildings, Better Plants Program is a voluntary initiative that works with leading manufacturers to showcase leadership and drive deep energy savings in the U.S. industrial sector. Partners agree to set ambitious sustainability goals and in return receive robust technical assistance, national recognition, facilitated peer-to-peer learning opportunities, and access to innovation. Many Better Plants partners are looking to translate their energy intensity progress towards carbon reduction goals and exploring if setting a Science-Based Target (SBT) is right for their organization. This document provides an overview of the SBT initiative and compares it to the offerings of the Better Plants program. To help companies who are participating in both initiatives, DOE tools and resources are highlighted and discussed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

RFQAmodel: Random Forest Quality Assessment to identify a predicted protein structure in the correct fold

While template-free protein structure prediction protocols now produce good quality models for many targets, modelling failure remains common. For these methods to be useful it is important that users can both choose the best model from the hundreds to thousands of models that are commonly generated for a target, and determine whether this model is likely to be correct. We have developed Random Forest Quality Assessment (RFQAmodel), which assesses whether models produced by a protein structure prediction pipeline have the correct fold. RFQAmodel uses a combination of existing quality assessment scores with two predicted contact map alignment scores. These alignment scores are able to identify correct models for targets that are not otherwise captured. Our classifier was trained on a large set of protein domains that are structurally diverse and evenly balanced in terms of protein features known to have an effect on modelling success, and then tested on a second set of 244 protein domains with a similar spread of properties. When models for each target in this second set were ranked according to the RFQAmodel score, the highest-ranking model had a high-confidence RFQAmodel score for 67 modelling targets, of which 52 had the correct fold. At the other end of the scale RFQAmodel correctly predicted that for 59 targets the highest-ranked model was incorrect. In comparisons to other methods we found that RFQAmodel is better able to identify correct models for targets where only a few of the models are correct. We found that RFQAmodel achieved a similar performance on the model sets for CASP12 and CASP13 free-modelling targets. Finally, by iteratively generating models and running RFQAmodel until a model is produced that is predicted to be correct with high confidence, we demonstrate how such a protocol can be used to focus computational efforts on difficult modelling targets. RFQAmodel and the accompanying data can be downloaded from http://opig.stats.ox.ac.uk/resources.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

FluxRETAP: a REaction TArget Prioritization genome-scale modeling technique for selecting genetic targets

MOTIVATION: Metabolic engineering is rapidly evolving as a result of new advances in synthetic biology tools and automation platforms that enable high throughput strain construction, as well as the development of machine learning tools (ML) for biology. However, selecting genetic engineering targets that effectively guide the metabolic engineering process is still challenging. ML can provide predictive power for synthetic biology, but current technical limitations prevent the independent use of ML approaches without previous biological knowledge. RESULTS: Here, we present FluxRETAP, a simple and computationally inexpensive method that leverages the prior mechanistic knowledge embedded in genome-scale models for suggesting targets for genetic overexpression, downregulation or deletion, with the final goal of increasing the production of a desired metabolite. This method can provide a list of desirable engineering targets that can be combined with current ML pipelines. FluxRETAP captured 100% of reaction targets experimentally verified to improve Escherichia coli isoprenol production, 50% of targets that experimentally improved taxadiene production in E. coli and ∼60% of genetic targets from a verified minimal constrained cut-set in Pseudomonas putida, while providing additional high priority targets that could be tested. Overall, FluxRETAP is an efficient algorithm for identifying a prioritized list of testable genetic and reaction targets. AVAILABILITY AND IMPLEMENTATION: FluxRETAP is implemented in python and released under the creative commons license. The implementation and code are freely available at: https://github.com/JBEI/FluxRETAP.

Czajka, Jeffrey J↗
DOE OSTI2024

Flux REaction TArget Prioritization (Flux RETAP) v1

Metabolic engineering is evolving rapidly as a result of new advances in synthetic biology and automation, as well as the irruption of machine learning (ML). ML has been shown to provide the predictive power synthetic biology lacked and needed, and to be able to effectively guide the metabolic engineering process. However, current technical limitations prevent the independent application of ML approaches to metabolic engineering without the use of previous biological knowledge in the form of a prioritized list of desirable engineering targets. Here, we present FluxRETAP, a simple and computationally inexpensive method that leverages the prior mechanistic knowledge embedded in genome-scale metabolic models (GSMs) for suggesting targets for genetic overexpression, downregulation or deletion, with the final goal of increasing metabolite production. FluxRETAP captured 100% of reaction targets experimentally verified to improve Escherichia coli isoprenol production in the literature accessible to us, 50% of targets that experimentally improved taxadiene production in E. coli and ~60% of genetic targets from a verified minimal constrained cut-set in Pseudomonas putida while providing additional high priority targets that could be tested. Overall, FluxRETAP is an efficient algorithm for identifying a prioritized list of testable genetic and reaction targets which can also be utilized in ML pipelines.

Czajka, Jeffrey [Battelle Memorial Institute, Paci↗
NASA NTRS1987

Stochastic nature of Landsat MSS data

A multiple series generalization of the ARIMA models is used to model Landsat MSS scan lines as sequences of vectors, each vector having four elements (bands). The purpose of this work is to investigate if Landsat scan lines can be described by a general multiple series linear stochastic model and if the coefficients of such a model vary as a function of satellite system and target attributes. To accomplish this objective, an exploratory experimental design was set up incorporating six factors, four representing target attributes - location, cloud cover, row (within location), and column (within location) - and two factors representing system attributes - satellite number and detector bank. Each factor was included in the design at two levels and, with two replicates per treatment, 128 scan lines were analyzed. The results of the analysis suggests that a multiple AR(4) model is an adequate representation across all scan lines. Furthermore, the coefficients of the AR(4) model vary with location, particularly changes in physiography (slope regimes), and with percent cloud cover, but are insensitive to changes in system attributes.

Labovitz, M. L.↗
NASA NTRSDate not supplied

The TESS Mission Target Selection Procedure

We describe the target selection procedure by which stars are selected for 2 minute and 20 s observations by TESS. We first list the technical requirements of the TESS instrument and ground systems processing that limit the total number of target slots. We then describe algorithms used by the TESS Payload Operation Center (POC) to merge candidate targets requested by the various TESS mission elements (the Target Selection Working Group, TESS Asteroseismic Science Consortium, and Guest Investigator office). Lastly, we summarize the properties of the observed TESS targets over the two-year primary TESS mission. We find that the POC target selection algorithm results in 2.1–3.4 times as many observed targets as target slots allocated for each mission element. We also find that the sky distribution of observed targets is different from the sky distributions of candidate targets due to technical constraints that require a relatively even distribution of targets across the TESS fields of view. We caution researchers exploring statistical analyses of TESS planet-host stars that the population of observed targets cannot be characterized by any simple set of criteria applied to the properties of the input Candidate Target Lists.

Michael Fausnaugh↗
DOE OSTI2018

Diagnosing and Correcting Mass Accuracy and Signal Intensity Error Due to Initial Ion Position Variations in a MALDI TOFMS

Frustrated by worse than expected error for both peak area and time-of-flight (TOF) in matrix assisted laser desorption ionization (MALDI) experiments using samples prepared by electrospray deposition, it was finally determined that there was a correlation between sample location on the target plate and the measured TOF/peak area. Variations in both TOF and peak area were found to be due to small differences in the initial position of ions formed in the source region of the TOF mass spectrometer. These differences arise largely from misalignment of the instrument sample stage, with a smaller contribution arising from the non-ideal shape of the target plates used. By physically measuring the target plates used and comparing TOF data collected from three different instruments, an estimate of the magnitude and direction of the sample stage misalignment was determined for each of the instruments. A correction method was developed to correct the TOFs and peak areas obtained for a given combination of target plate and instrument. Two correction factors are determined, one by initially collecting spectra from each sample position used and another by using spectra from a single position for each set of samples on a target plate. For TOF and mass values, use of the correction factor reduced the error by a factor of 4, with the relative standard deviation (RSD) of the corrected masses being reduced to 12-24 ppm. For the peak areas, the RSD was reduced from 28% to 16% for samples deposited twice onto two target plates over two days. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Improving the chances of successful protein structure determination with a random forest classifier

Using an extended set of protein features calculated separately for protein surface and interior, a new version of XtalPred based on a random forest classifier achieves a significant improvement in predicting the success of structure determination from the primary amino-acid sequence. Obtaining diffraction quality crystals remains one of the major bottlenecks in structural biology. The ability to predict the chances of crystallization from the amino-acid sequence of the protein can, at least partly, address this problem by allowing a crystallographer to select homologs that are more likely to succeed and/or to modify the sequence of the target to avoid features that are detrimental to successful crystallization. In 2007, the now widely used XtalPred algorithm [Slabinski et al. (2007 ▶), Protein Sci.16, 2472–2482] was developed. XtalPred classifies proteins into five ‘crystallization classes’ based on a simple statistical analysis of the physicochemical features of a protein. Here, towards the same goal, advanced machine-learning methods are applied and, in addition, the predictive potential of additional protein features such as predicted surface ruggedness, hydrophobicity, side-chain entropy of surface residues and amino-acid composition of the predicted protein surface are tested. The new XtalPred-RF (random forest) achieves significant improvement of the prediction of crystallization success over the original XtalPred. To illustrate this, XtalPred-RF was tested by revisiting target selection from 271 Pfam families targeted by the Joint Center for Structural Genomics (JCSG) in PSI-2, and it was estimated that the number of targets entered into the protein-production and crystallization pipeline could have been reduced by 30% without lowering the number of families for which the first structures were solved. The prediction improvement depends on the subset of targets used as a testing set and reaches 100% (i.e. twofold) for the top class of predicted targets.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2022

Automatic variable selection in ecological niche modeling: A case study using Cassin’s Sparrow (Peucaea cassinii)

MERRA/Max provides a feature selection approach to dimensionality reduction that enables direct use of global climate model outputs in ecological niche modeling. The system accomplishes this reduction through a Monte Carlo optimization in which many independent MaxEnt runs, operating on a species occurrence file and a small set of randomly selected variables in a large collection of variables, converge on an estimate of the top contributing predictors in the larger collection. These top predictors can be viewed as potential candidates in the variable selection step of the ecological niche modeling process. MERRA/Max’s Monte Carlo algorithm operates on files stored in the underlying filesystem, making it scalable to large data sets. Its software components can run as parallel processes in a high-performance cloud computing environment to yield near real-time performance. In tests using Cassin’s Sparrow (Peucaea cassinii) as the target species, MERRA/Max selected a set of predictors from Worldclim’s Bioclim collection of 19 environmental variables that have been shown to be important determinants of the species’ bioclimatic niche. It also selected biologically and ecologically plausible predictors from a more diverse set of 86 environmental variables derived from NASA’s Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) reanalysis, an output product of the Goddard Earth Observing System Version 5 (GEOS-5) modeling system. We believe these results point to a technological approach that could expand the use global climate model outputs in ecological niche modeling, foster exploratory experimentation with otherwise difficult-to-use climate data sets, streamline the modeling process, and, eventually, enable automated bioclimatic modeling as a practical, readily accessible, low-cost, commercial cloud service.

John L. Schnase↗
DOE OSTI2015

WE-AB-BRA-02: Development of Biomechanical Models to Describe Dose-Volume Response to Liver Stereotactic Body Radiation Therapy (SBRT) Patients

Purpose: Previous studies have shown that radiotherapy treatment for liver metastases causes marked liver hypertrophy in areas receiving low dose and atrophy/fibrosis in areas receiving high dose. The purpose of this work is to develop and evaluate a biomechanical model-based dose-response model to describe these liver responses to SBRT. Methods: In this retrospective study, a biomechanical model-based deformable registration algorithm, Morfeus, was expanded to include dose-based boundary conditions. Liver and tumor volumes were contoured on the planning images and CT/MR images three months post-RT and converted to finite element models. A thermal expansion-based relationship correlating the delivered dose and volume response was generated from 22 patients previously treated. This coefficient, combined with the planned dose, was applied as an additional boundary condition to describe the volumetric response of the liver of an additional cohort of metastatic liver patients treated with SBRT. The accuracy of the model was evaluated based on overall volumetric liver comparisons and the target registration error (TRE) using the average deviations in positions of identified vascular bifurcations on each set of registered images, with a target accuracy of the 2.5mm isotropic dose grid (vector dimension 4.3mm). Results: The thermal expansion coefficient models the volumetric change of the liver to within 3%. The accuracy of Morfeus with dose-expansion boundary conditions a TRE of 5.7±2.8mm compared to 11.2±3.7mm using rigid registration and 8.9±0.28mm using Morfeus with only spatial boundary conditions. Conclusion: A biomechanical model has been developed to describe the volumetric and spatial response of the liver to SBRT. This work will enable the improvement of correlating functional imaging with delivered dose, the mapping of the delivered dose from one treatment onto the planning images for a subsequent treatment, and will further provide information to assist with the biological characterization of patients’ response to radiation.

60 APPLIED LIFE SCIENCES↗