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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

SU-F-J-19: Robust Region-Of-Interest (ROI) for Consistent Registration On Deteriorated Surface Images

Purpose: For African-American patients receiving breast radiotherapy with a bolus, skin darkening can affect the surface visualization when using optical imaging for daily positioning and gating at deep-inspiration breath holds (DIBH). Our goal is to identify a region-of-interest (ROI) that is robust against deteriorating surface image quality due to skin darkening. Methods: We study four patients whose post-mastectomy surfaces are imaged daily with AlignRT (VisionRT, UK) for DIBH radiotherapy and whose surface image quality is degraded toward the end of treatment. To simulate the effects of skin darkening, surfaces from the first ten fractions of each patient are systematically degraded by 25–35%, 40–50% and 65–75% of the total area of the clinically used ROI-ipsilateral-chestwall. The degraded surfaces are registered to the reference surface in six degrees-of-freedom. To identify a robust ROI, three additional reference ROIs — ROI-chest+abdomen, ROI-bilateral-chest and ROI-extended-ipsilateral-chestwall are created and registered to the degraded surfaces. Differences in registration using these ROIs are compared to that using ROI-ipsilateral-chestwall. Results: For three patients, the deviations in the registrations to ROI-ipsilateral-chestwall are > 2.0, 3.1 and 7.9mm on average for 25–35%, 40–50% and 65–75% degraded surfaces, respectively. Rotational deviations reach 11.1° in pitch. For the last patient, registration is consistent to within 2.6mm even on the 65–75% degraded surfaces, possibly because the surface topography has more distinct features. For ROI-bilateral-chest and ROI-extended-ipsilateral-chest registrations deviate in a similar pattern. However, registration on ROI-chest+abdomen is robust to deteriorating image qualities to within 4.2mm for all four patients. Conclusion: Registration deviations using ROI-ipsilateral-chestwall can reach 9.8mm on the 40–50% degraded surfaces. Caution is required when using AlignRT for patients experiencing skin darkening since the accuracy of AlignRT registration deteriorates. To avoid this inaccuracy, we recommend use of ROI-chest+abdomen, on which registration is consistent within 4.2mm even for highly degraded surfaces.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-I-96: A Study About the Influence of ROI Variation On Tumor Segmentation in PET

Purpose: To study the influence of different regions of interest (ROI) on tumor segmentation in PET. Methods: The experiments were conducted on a cylindrical phantom. Six spheres with different volumes (0.5ml, 1ml, 6ml, 12ml, 16ml and 20 ml) were placed inside a cylindrical container to mimic tumors of different sizes. The spheres were filled with 11C solution as sources and the cylindrical container was filled with 18F-FDG solution as the background. The phantom was continuously scanned in a Biograph-40 True Point/True View PET/CT scanner, and 42 images were reconstructed with source-to-background ratio (SBR) ranging from 16:1 to 1.8:1. We took a large and a small ROI for each sphere, both of which contain the whole sphere and does not contain any other spheres. Six other ROIs of different sizes were then taken between the large and the small ROI. For each ROI, all images were segmented by eitht thresholding methods and eight advanced methods, respectively. The segmentation results were evaluated by dice similarity index (DSI), classification error (CE) and volume error (VE). The robustness of different methods to ROI variation was quantified using the interrun variation and a generalized Cohen's kappa. Results: With the change of ROI, the segmentation results of all tested methods changed more or less. Compared with all advanced methods, thresholding methods were less affected by the ROI change. In addition, most of the thresholding methods got more accurate segmentation results for all sphere sizes. Conclusion: The results showed that the segmentation performance of all tested methods was affected by the change of ROI. Thresholding methods were more robust to this change and they can segment the PET image more accurately. This work was supported in part by National Natural Science Foundation of China (NNSFC), under Grant Nos. 60971112 and 61375018, and Fundamental Research Funds for the Central Universities, under Grant No. 2012QN086. Wei Lu was supported in part by the National Institutes of Health (NIH) Grant No. R01 CA172638.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

A method to estimate the fractional fat volume within a ROI of a breast biopsy for WAXS applications: Animal tissue evaluation

Purpose: To develop a method to estimate the mean fractional volume of fat (ν{sup ¯}{sub fat}) within a region of interest (ROI) of a tissue sample for wide-angle x-ray scatter (WAXS) applications. A scatter signal from the ROI was obtained and use of ν{sup ¯}{sub fat} in a WAXS fat subtraction model provided a way to estimate the differential linear scattering coefficient μ{sub s} of the remaining fatless tissue. Methods: The efficacy of the method was tested using animal tissue from a local butcher shop. Formalin fixed samples, 5 mm in diameter 4 mm thick, were prepared. The two main tissue types were fat and meat (fibrous). Pure as well as composite samples consisting of a mixture of the two tissue types were analyzed. For the latter samples, ν{sub fat} for the tissue columns of interest were extracted from corresponding pixels in CCD digital x-ray images using a calibration curve. The means ν{sup ¯}{sub fat} were then calculated for use in a WAXS fat subtraction model. For the WAXS measurements, the samples were interrogated with a 2.7 mm diameter 50 kV beam and the 6° scattered photons were detected with a CdTe detector subtending a solid angle of 7.75 × 10{sup −5} sr. Using the scatter spectrum, an estimate of the incident spectrum, and a scatter model, μ{sub s} was determined for the tissue in the ROI. For the composite samples, a WAXS fat subtraction model was used to estimate the μ{sub s} of the fibrous tissue in the ROI. This signal was compared to μ{sub s} of fibrous tissue obtained using a pure fibrous sample. Results: For chicken and beef composites, ν{sup ¯}{sub fat}=0.33±0.05 and 0.32 ± 0.05, respectively. The subtractions of these fat components from the WAXS composite signals provided estimates of μ{sub s} for chicken and beef fibrous tissue. The differences between the estimates and μ{sub s} of fibrous obtained with a pure sample were calculated as a function of the momentum transfer x. A t-test showed that the mean of the differences did not vary from zero in a statistically significant way thereby validating the methods. Conclusions: The methodology to estimate ν{sup ¯}{sub fat} in a ROI of a tissue sample via CCD x-ray imaging was quantitatively accurate. The WAXS fat subtraction model allowed μ{sub s} of fibrous tissue to be obtained from a ROI which had some fat. The fat estimation method coupled with the WAXS models can be used to compare μ{sub s} coefficients of fibroglandular and cancerous breast tissue.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

SU-F-J-29: Dosimetric Effect of Image Registration ROI Size and Focus in Automated CBCT Registration for Spine SBRT

Purpose: Spinal stereotactic body radiotherapy (SBRT) involves highly conformal dose distributions and steep dose gradients due to the proximity of the spinal cord to the treatment volume. To achieve the planning goals while limiting the spinal cord dose, patients are setup using kV cone-beam CT (kV-CBCT) with 6 degree corrections. The kV-CBCT registration with the reference CT is dependent on a user selected region of interest (ROI). The objective of this work is to determine the dosimetric impact of ROI selection. Methods: Twenty patients were selected for this study. For each patient, the kV-CBCT was registered to the reference CT using three ROIs including: 1) the external body, 2) a large anatomic region, and 3) a small region focused in the target volume. Following each registration, the aligned CBCTs and contours were input to the treatment planning system for dose evaluation. The minimum dose, dose to 99% and 90% of the tumor volume (D99%, D90%), dose to 0.03cc and the dose to 10% of the spinal cord subvolume (V10Gy) were compared to the planned values. Results: The average deviations in the tumor minimum dose were 2.68%±1.7%, 4.6%±4.0%, 14.82%±9.9% for small, large and the external ROIs, respectively. The average deviations in tumor D99% were 1.15%±0.7%, 3.18%±1.7%, 10.0%±6.6%, respectively. The average deviations in tumor D90% were 1.00%±0.96%, 1.14%±1.05%, 3.19%±4.77% respectively. The average deviations in the maximum dose to the spinal cord were 2.80%±2.56%, 7.58%±8.28%, 13.35%±13.14%, respectively. The average deviation in V10Gy to the spinal cord were 1.69%±0.88%, 1.98%±2.79%, 2.71%±5.63%. Conclusion: When using automated registration algorithms for CBCT-Reference alignment, a small target-focused ROI results in the least dosimetric deviation from the plan. It is recommended to focus narrowly on the target volume to keep the spinal cord dose below tolerance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

XRF-ROI Finder: Machine Learning to Guide Region-of-Interest Scanning for X-ray Fluorescence Microscopy

The ROI-finder software is being developed for use by several Microscopy Group beamlines at Argonne National Laboratory, including 2-ID microprobes and 9-ID-B Bionanoprobe which use multi-scale scanning fluorescence microscopy to acquire elemental maps (multi-modal image data). Microscopy experiments require scan of samples at a coarse resolution followed by ROI identification using feature detection based on domain expertise. Finer resolution scans are then conducted based on identified ROI. The decision-making process based on domain expertise will be difficult to perform for faster data rates and much larger sampling volumes anticipated after APS-U necessitating the need for the ROI-finder software. The ROI- finder detects regions of interest through a continuous learning process, starting with a unsupervised representation learning and improving its recommendations through supervised learning and an interactive tool for user annotation. The scope of ongoing development efforts includes the integration of image registration module to correlate optical and X-ray images, extraction of feature morphology as well as elemental signatures in the image space and incorporation of beamtime streaming data by the scanning probe via EPICS.

CHOWDHURY, M. ARSHAD ZAHANGIR↗
DOE OSTI2025

Training Evaluation: A Case Study of Measuring Impact and ROI

The purpose of this case study was to evaluate the Compliance Officer/Coordinator (CO/CC) training program using Phillips’ approach to training evaluation. INL Learning Performance Consultants (LPCs) applied the ROI Methodology to measure the effectiveness of the CO/CC training program. INL CO/CCs participated in focus groups to provide feedback on their learning, enablers and barriers to applying what they learned, and opportunities to improve the CO/CC learning experience. With collaboration from the CO/CC training program owner, the LPCs demonstrated the business impact and return on investment (ROI) of the CO/CC training program. Included are training program recommendations and lessons learned using the ROI Methodology.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

ROI Hide and Seek Protocol v1

1. Segmentation We provide scripts for the model definition of the U-net architecture adapted from: https://github.com/jvanvugt/pytorch-unet/blob/master/unet.py We developed scripts for preparing the lung segmentation data set. We developed scripts for training the U-Net architecture. We developed scripts for applying the trained U-Net model to perform the ROI Hide and Seek protocol on the classification dataset to create the modified dataset. 2. Classification We provide scripts for the training of the COVID-Net models provided by Linda Wang, this code is adapted from her github repository: https://github.com/lindawangg/COVID-Net/tree/d7b36831d854f57de5bc7557217f5439e86e016f. These scripts were modified to save training log information as well as to load the provided models in their github repo. We developed scripts for training standard Neural Network Models (resnet 50, vgg 11, Alexnet) on the COVID datsets along with the ROI Hide and Seek altered datasets.

Sadre, Robbie↗
NASA NTRS1994

Reinterpretation of ozone data from Base Roi Baudouin

The ozone Dobson measurements obtained in Antarctica at the Belgian 'Base Roi Baudouin' (70 deg 26 min S, 24 deg 19 min E) in 1965 and 1966 were retrieved from the KNMI (Royal Netherlands Meteorological Institute) archives in De Bilt. Despite excellent treatment at the time by the meteorologists in charge at the KNMI (Wisse and Meerburg, 1969), a study of the original observers notes was made in order to check possible seasonal ozone phenomena. No systematic anomaly in the first analysis was found; meteorological data from the site together with Brewer-Mast ozone soundings concur that the conditions did not correspond either in 1965 nor 1966 to the current ozone hole (Farman et al., 1985) situation, however, the data yields excellent correlation with stratospheric temperature and shows in 1966 a clear November maximum in opposition to an October value around 344 Dobson units.

Kelder, H.↗
DOE OSTI2024

Assessment of ROI for Workforce Development Efforts National & Homeland Security

The IDEAL Professional Engagement project at Idaho National Laboratory (INL) aims to enhance the laboratory's workforce diversity and inclusivity efforts, focusing on the U.S. National and Homeland Security mission areas. This project involves researching and evaluating opportunities for INL to engage in various professional events, particularly cyber conferences, to support recruitment and professional development. The project involved several key tasks: compiling comprehensive information on national laboratories and their mission statements, developing a deep understanding of INL’s role and efforts in national and homeland security, and identifying and engaging key stakeholders. Interviews were conducted with a set of targeted questions, and the findings were analyzed to identify common themes, insights, and actionable recommendations. Additionally, relevant upcoming cyber conferences were identified, various sponsorship levels and their associated benefits were evaluated, and the recruitment potential of these conferences was assessed. A detailed cost analysis was performed, including registration fees and travel expenses, and a cost-benefit analysis was conducted to evaluate the financial viability and potential return on investment (ROI) of conference participation and sponsorship. Based on the research and analysis, actionable recommendations for conference participation and sponsorship were formulated, ensuring alignment with INL’s mission and diversity goals. Preliminary results include a comprehensive list of relevant cyber conferences, a detailed cost analysis, and a set of actionable recommendations for future conference participation and sponsorship. The analysis highlights the financial requirements and geographical distribution of these conferences, providing valuable insights for INL's engagement strategies. This project underscores the importance of strategic engagement in professional events to attract and develop a diverse and skilled workforce, ultimately supporting INL’s mission areas in U.S. National and Homeland Security.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Create full-scale predictive economic models on ROI and innovation with performance computing

The U.S. Department of Energy (DOE), the world's largest buyer and user of supercomputers, awarded IDC Research, Inc. a grant to create two macroeconomic models capable of quantifying, respectively, financial and non-financial (innovation) returns on investments in HPC resources. Following a 2013 pilot study in which we created the models and tested them on about 200 real-world HPC cases, DOE authorized us to conduct a full-out, three-year grant study to collect and measure many more examples, a process that would also subject the methodology to further testing and validation. A secondary, "stretch" goal of the full-out study was to advance the methodology from association toward (but not all the way to) causation, by eliminating the effects of some of the other factors that might be contributing, along with HPC investments, to the returns produced in the investigated projects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

ROI-Finder : machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy

The microscopy research at the Bionanoprobe (currently at beamline 9-ID and later 2-ID after APS-U) of Argonne National Laboratory focuses on applying synchrotron X-ray fluorescence (XRF) techniques to obtain trace elemental mappings of cryogenic biological samples to gain insights about their role in critical biological activities. The elemental mappings and the morphological aspects of the biological samples, in this instance, the bacterium Escherichia coli ( E. Coli ), also serve as label-free biological fingerprints to identify E. coli cells that have been treated differently. The key limitations of achieving good identification performance are the extraction of cells from raw XRF measurements via binary conversion, definition of features, noise floor and proportion of cells treated differently in the measurement. Automating cell extraction from raw XRF measurements across different types of chemical treatment and the implementation of machine-learning models to distinguish cells from the background and their differing treatments are described. Principal components are calculated from domain knowledge specific features and clustered to distinguish healthy and poisoned cells from the background without manual annotation. The cells are ranked via fuzzy clustering to recommend regions of interest for automated experimentation. The effects of dwell time and the amount of data required on the usability of the software are also discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Return on Investment (ROI) Framework Case Study: CTH.

CTH is a Eulerian code developed at Sandia National Laboratories capable of modeling the hydrodynamic response of explosives, liquids, gases, and solids. The code solves complex multi-dimensional problems characterized by large deformations and strong shocks that are composed of various material configurations. CTH includes models for material strength, fracture, porosity, and high explosive detonation and initiation. The code is an acronym for a complex series of names relating to its origin. A full explanation can be seen in Appendix A. The software breaks penetration simulations into millions of grid-like “cells”. As a modeled projectile impacts and penetrates a target, progressively smaller blocks of cells are placed around the projectile, which show in detail deformations and breakups. Additionally, the code is uniquely suited to modeling blunt impact and blast loading leading to human body injury.

42 ENGINEERING↗
DOE OSTI2019

Practical applications of the sigma factor alarm method within alpha CAMs utilizing either ROI, tail-fitting, or peak-fitting algorithms

Some test must be applied to the low-level count data from alpha continuous air monitors (CAMs) to determine if the count is statistically significant (i.e., significantly above background). The test should also automatically account for different levels of background (i.e., different ambient radon progeny concentrations). In other words, the method should be as sensitive as possible, automatically desensitizing when required, yet in such a manner as to not exceed some previously-chosen acceptable false-alarm rate. The test that has been historically utilized within many alpha CAMs to accomplish these goals is the so-called sigma factor approach.

61 RADIATION PROTECTION AND DOSIMETRY↗
NASA NTRS2012

NASA InterCenter Collaboration Increases ROI

Funding for National Aeronautics and Space Administration (NASA) space mission operations is tighter than ever in the current environment of federal government deficit reductions. Conventional wisdom would expect this environment to drive increasing competition between NASA centers for the limited available funds. However, recent inter-center activities at the Huntsville Operations Support Center (HOSC) at NASA's Marshall Space Flight Center emphasize collaboration rather than competition and demonstrate the value of partnerships to increase the return on shrinking investments. These efforts cover a variety of activities and potential returns. To facilitate sharing data from test and verification through operations without levying requirements on data format or software tools, the HOSC is working with multiple centers on an evolutionary path toward a distributed data architecture and archive. The approach reduces the required investment by allowing the partners to reuse their existing formats and tools, while facilitating gone ]stop h user visibility into and controlled access to the full complement of data regardless of user or data location. The HOSC is also working on two activities to promote sharing operations implementations and leveraging the experts and expertise across multiple NASA sites. In one, the use of Consultative Committee for Space Data Systems (CCSDS) standards for the message abstraction layer provides an interoperability layer on top of existing ground data system communication architectures. This allows missions to select the most appropriate solutions for their requirements with a minimal investment in rehosting the components in a coherent operational environment. The other emphasizes shared tools and increased remote access to minimize travel for tests and critical activities and reduce the floor space required for a dedicated operations center. This paper summarizes these and other inter-center collaboration activities at the HOSC and the benefits that each can bring, not just to the participants, but to the broader operations community.

Lankford, Kimberly↗