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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

WE-DE-207B-11: Implementation of Size-Specific 3D Beam Modulation Filters On a Dedicated Breast CT Platform Using Breast Immobilization

Purpose: To implement a 3D beam modulation filter (3D-BMF) in dedicated breast CT (bCT) and develop a method for conforming the patient’s breast to a pre-defined shape, optimizing the effects of the filter. This work expands on previous work reporting the methodology for designing a 3D-BMF that can spare unnecessary dose and improve signal equalization at the detector by preferentially filtering the beam in the thinner anterior and peripheral breast regions. Methods: Effective diameter profiles were measured for 219 segmented bCT images, grouped into volume quintiles, and averaged within each group to represent the range of breast sizes found clinically. These profiles were then used to generate five size-specific computational phantoms and fabricate five size-specific UHMW phantoms. Each computational phantom was utilized for designing a size-specific 3D-BMF using previously reported methods. Glandular dose values and projection images were simulated in MCNP6 with and without the 3DBMF using the system specifications of our prototype bCT scanner “Doheny”. Lastly, thermoplastic was molded around each of the five phantom sizes and used to produce a series of breast immobilizers for use in conforming the patient’s breast during bCT acquisition. Results: After incorporating the 3D-BMF, MC simulations estimated an 80% average reduction in the detector dynamic range requirements across all phantom sizes. The glandular dose was reduced on average 57% after normalizing by the number of quanta reaching the detector under the thickest region of the breast. Conclusion: A series of bCT-derived breast phantoms were used to design size-specific 3D-BMFs and breast immobilizers that can be used on the bCT platform to conform the patient’s breast and therefore optimally exploit the benefits of the 3D-BMF. Current efforts are focused on fabricating several prototype 3D-BMFs and performing phantom scans on Doheny for MC simulation validation and image quality analysis. Research reported in this paper was supported in part by the National Cancer Institute of the National Institutes of Health under award R01CA181081. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institue of Health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-FG-BRB-02: Spatial Mapping of the RBE of Scanned Particle Beams

The physical pattern of energy deposition and the enhanced relative biological effectiveness (RBE) of protons and carbon ions compared to photons offer unique and not fully understood or exploited opportunities to improve the efficacy of radiation therapy. Variations in RBE within a pristine or spread out Bragg peak and between particle types may be exploited to enhance cell killing in target regions without a corresponding increase in damage to normal tissue structures. In addition, the decreased sensitivity of hypoxic tumors to photon-based therapies may be partially overcome through the use of more densely ionizing radiations. These and other differences between particle and photon beams may be used to generate biologically optimized treatments that reduce normal tissue complications. In this symposium, speakers will examine the impact of the RBE of charged particles on measurable biological endpoints, treatment plan optimization, and the prediction or retrospective assessment of treatment outcomes. In particular, an AAPM task group was formed to critically examine the evidence for a spatially-variant RBE in proton therapy. Current knowledge of proton RBE variation with respect to dose, biological endpoint, and physics parameters will be reviewed. Further, the clinical relevance of these variations will be discussed. Recent work focused on improving simulations of radiation physics and biological response in proton and carbon ion therapy will also be presented. Finally, relevant biology research and areas of research needs will be highlighted, including the dependence of RBE on genetic factors including status of DNA repair pathways, the sensitivity of cancer stem-like cells to charged particles, the role of charged particles in hypoxic tumors, and the importance of fractionation effects. In addition to the physical advantages of protons and more massive ions over photons, the future application of biologically optimized treatment plans and their potential to provide higher levels of local tumor control and improved normal tissue sparing will be discussed. Learning Objectives: To assess whether the current practice of a constant RBE of 1.1 should be revised or maintained in proton therapy and to evaluate the potential clinical consequences of delivering RBE-weighted dose distributions based on variable RBE To review current research on biological models used to predict the increased biological effectiveness of proton and carbon ions to help move towards a practical understanding and implementation of biological optimization in particle therapy To discuss potential differences in biological mechanisms between photons and charged particles (light and heavy ions) that could impact clinical cancer therapy H. Paganetti, NCI U19 CA21239D. Grosshans, Our research is supported by the NCIK. Held, Funding Support: National Cancer Institute of the National Institutes of Health, USA, under Award Number R21CA182259 and Federal Share of Program Income Earned by Massachusetts General Hospital on C06CA059267, Proton Therapy Research and Treatment Center.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-FG-BRB-03: Challenges and Opportunities for Implementing Biological Optimization in Particle Therapy

The physical pattern of energy deposition and the enhanced relative biological effectiveness (RBE) of protons and carbon ions compared to photons offer unique and not fully understood or exploited opportunities to improve the efficacy of radiation therapy. Variations in RBE within a pristine or spread out Bragg peak and between particle types may be exploited to enhance cell killing in target regions without a corresponding increase in damage to normal tissue structures. In addition, the decreased sensitivity of hypoxic tumors to photon-based therapies may be partially overcome through the use of more densely ionizing radiations. These and other differences between particle and photon beams may be used to generate biologically optimized treatments that reduce normal tissue complications. In this symposium, speakers will examine the impact of the RBE of charged particles on measurable biological endpoints, treatment plan optimization, and the prediction or retrospective assessment of treatment outcomes. In particular, an AAPM task group was formed to critically examine the evidence for a spatially-variant RBE in proton therapy. Current knowledge of proton RBE variation with respect to dose, biological endpoint, and physics parameters will be reviewed. Further, the clinical relevance of these variations will be discussed. Recent work focused on improving simulations of radiation physics and biological response in proton and carbon ion therapy will also be presented. Finally, relevant biology research and areas of research needs will be highlighted, including the dependence of RBE on genetic factors including status of DNA repair pathways, the sensitivity of cancer stem-like cells to charged particles, the role of charged particles in hypoxic tumors, and the importance of fractionation effects. In addition to the physical advantages of protons and more massive ions over photons, the future application of biologically optimized treatment plans and their potential to provide higher levels of local tumor control and improved normal tissue sparing will be discussed. Learning Objectives: To assess whether the current practice of a constant RBE of 1.1 should be revised or maintained in proton therapy and to evaluate the potential clinical consequences of delivering RBE-weighted dose distributions based on variable RBE To review current research on biological models used to predict the increased biological effectiveness of proton and carbon ions to help move towards a practical understanding and implementation of biological optimization in particle therapy To discuss potential differences in biological mechanisms between photons and charged particles (light and heavy ions) that could impact clinical cancer therapy H. Paganetti, NCI U19 CA21239D. Grosshans, Our research is supported by the NCIK. Held, Funding Support: National Cancer Institute of the National Institutes of Health, USA, under Award Number R21CA182259 and Federal Share of Program Income Earned by Massachusetts General Hospital on C06CA059267, Proton Therapy Research and Treatment Center.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-FG-BRB-04: RBEs for Human Lung Cancer Cells Exposed to Protons and Heavier Ions: Implications for Clinical Use of Charged Particles in Cancer Therapy

The physical pattern of energy deposition and the enhanced relative biological effectiveness (RBE) of protons and carbon ions compared to photons offer unique and not fully understood or exploited opportunities to improve the efficacy of radiation therapy. Variations in RBE within a pristine or spread out Bragg peak and between particle types may be exploited to enhance cell killing in target regions without a corresponding increase in damage to normal tissue structures. In addition, the decreased sensitivity of hypoxic tumors to photon-based therapies may be partially overcome through the use of more densely ionizing radiations. These and other differences between particle and photon beams may be used to generate biologically optimized treatments that reduce normal tissue complications. In this symposium, speakers will examine the impact of the RBE of charged particles on measurable biological endpoints, treatment plan optimization, and the prediction or retrospective assessment of treatment outcomes. In particular, an AAPM task group was formed to critically examine the evidence for a spatially-variant RBE in proton therapy. Current knowledge of proton RBE variation with respect to dose, biological endpoint, and physics parameters will be reviewed. Further, the clinical relevance of these variations will be discussed. Recent work focused on improving simulations of radiation physics and biological response in proton and carbon ion therapy will also be presented. Finally, relevant biology research and areas of research needs will be highlighted, including the dependence of RBE on genetic factors including status of DNA repair pathways, the sensitivity of cancer stem-like cells to charged particles, the role of charged particles in hypoxic tumors, and the importance of fractionation effects. In addition to the physical advantages of protons and more massive ions over photons, the future application of biologically optimized treatment plans and their potential to provide higher levels of local tumor control and improved normal tissue sparing will be discussed. Learning Objectives: To assess whether the current practice of a constant RBE of 1.1 should be revised or maintained in proton therapy and to evaluate the potential clinical consequences of delivering RBE-weighted dose distributions based on variable RBE To review current research on biological models used to predict the increased biological effectiveness of proton and carbon ions to help move towards a practical understanding and implementation of biological optimization in particle therapy To discuss potential differences in biological mechanisms between photons and charged particles (light and heavy ions) that could impact clinical cancer therapy H. Paganetti, NCI U19 CA21239D. Grosshans, Our research is supported by the NCIK. Held, Funding Support: National Cancer Institute of the National Institutes of Health, USA, under Award Number R21CA182259 and Federal Share of Program Income Earned by Massachusetts General Hospital on C06CA059267, Proton Therapy Research and Treatment Center.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-FG-BRB-00: The Challenges of Predicting RBE Effects in Particle Therapy and Opportunities for Improving Cancer Therapy

The physical pattern of energy deposition and the enhanced relative biological effectiveness (RBE) of protons and carbon ions compared to photons offer unique and not fully understood or exploited opportunities to improve the efficacy of radiation therapy. Variations in RBE within a pristine or spread out Bragg peak and between particle types may be exploited to enhance cell killing in target regions without a corresponding increase in damage to normal tissue structures. In addition, the decreased sensitivity of hypoxic tumors to photon-based therapies may be partially overcome through the use of more densely ionizing radiations. These and other differences between particle and photon beams may be used to generate biologically optimized treatments that reduce normal tissue complications. In this symposium, speakers will examine the impact of the RBE of charged particles on measurable biological endpoints, treatment plan optimization, and the prediction or retrospective assessment of treatment outcomes. In particular, an AAPM task group was formed to critically examine the evidence for a spatially-variant RBE in proton therapy. Current knowledge of proton RBE variation with respect to dose, biological endpoint, and physics parameters will be reviewed. Further, the clinical relevance of these variations will be discussed. Recent work focused on improving simulations of radiation physics and biological response in proton and carbon ion therapy will also be presented. Finally, relevant biology research and areas of research needs will be highlighted, including the dependence of RBE on genetic factors including status of DNA repair pathways, the sensitivity of cancer stem-like cells to charged particles, the role of charged particles in hypoxic tumors, and the importance of fractionation effects. In addition to the physical advantages of protons and more massive ions over photons, the future application of biologically optimized treatment plans and their potential to provide higher levels of local tumor control and improved normal tissue sparing will be discussed. Learning Objectives: To assess whether the current practice of a constant RBE of 1.1 should be revised or maintained in proton therapy and to evaluate the potential clinical consequences of delivering RBE-weighted dose distributions based on variable RBE To review current research on biological models used to predict the increased biological effectiveness of proton and carbon ions to help move towards a practical understanding and implementation of biological optimization in particle therapy To discuss potential differences in biological mechanisms between photons and charged particles (light and heavy ions) that could impact clinical cancer therapy H. Paganetti, NCI U19 CA21239D. Grosshans, Our research is supported by the NCIK. Held, Funding Support: National Cancer Institute of the National Institutes of Health, USA, under Award Number R21CA182259 and Federal Share of Program Income Earned by Massachusetts General Hospital on C06CA059267, Proton Therapy Research and Treatment Center.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-FG-BRB-01: Clinical Significance of RBE Variations in Proton Therapy

The physical pattern of energy deposition and the enhanced relative biological effectiveness (RBE) of protons and carbon ions compared to photons offer unique and not fully understood or exploited opportunities to improve the efficacy of radiation therapy. Variations in RBE within a pristine or spread out Bragg peak and between particle types may be exploited to enhance cell killing in target regions without a corresponding increase in damage to normal tissue structures. In addition, the decreased sensitivity of hypoxic tumors to photon-based therapies may be partially overcome through the use of more densely ionizing radiations. These and other differences between particle and photon beams may be used to generate biologically optimized treatments that reduce normal tissue complications. In this symposium, speakers will examine the impact of the RBE of charged particles on measurable biological endpoints, treatment plan optimization, and the prediction or retrospective assessment of treatment outcomes. In particular, an AAPM task group was formed to critically examine the evidence for a spatially-variant RBE in proton therapy. Current knowledge of proton RBE variation with respect to dose, biological endpoint, and physics parameters will be reviewed. Further, the clinical relevance of these variations will be discussed. Recent work focused on improving simulations of radiation physics and biological response in proton and carbon ion therapy will also be presented. Finally, relevant biology research and areas of research needs will be highlighted, including the dependence of RBE on genetic factors including status of DNA repair pathways, the sensitivity of cancer stem-like cells to charged particles, the role of charged particles in hypoxic tumors, and the importance of fractionation effects. In addition to the physical advantages of protons and more massive ions over photons, the future application of biologically optimized treatment plans and their potential to provide higher levels of local tumor control and improved normal tissue sparing will be discussed. Learning Objectives: To assess whether the current practice of a constant RBE of 1.1 should be revised or maintained in proton therapy and to evaluate the potential clinical consequences of delivering RBE-weighted dose distributions based on variable RBE To review current research on biological models used to predict the increased biological effectiveness of proton and carbon ions to help move towards a practical understanding and implementation of biological optimization in particle therapy To discuss potential differences in biological mechanisms between photons and charged particles (light and heavy ions) that could impact clinical cancer therapy H. Paganetti, NCI U19 CA21239D. Grosshans, Our research is supported by the NCIK. Held, Funding Support: National Cancer Institute of the National Institutes of Health, USA, under Award Number R21CA182259 and Federal Share of Program Income Earned by Massachusetts General Hospital on C06CA059267, Proton Therapy Research and Treatment Center.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-FG-202-02: Exploration of High-Resolution Quantitative Ultrasonic Micro-Vascular Imaging for Early Assessment of Radiotherapy Tumor Response

Purpose: Currently, we cannot predict an individual patient’s response to a given radiotherapy which normally is not detected for weeks to months post-treatment. As a result, precious time is wasted for patients with unresponsive tumors who could have switched to an alternative treatment much earlier. Presently, no early treatment response detection method exists that is effective, low-cost, non-invasive, and safe. We hypothesize that changes in tumor microvasculature predict tumor response to radiotherapy earlier than tumor volume changes. Recent radiobiology research suggests tumors undergo vascular remodeling in response to radiation well before manifesting changes in tumor volume. We propose monitoring tumor microvasculature post-radiation using Acoustic Angiography (AA), a novel ultrasound imaging modality developed and patented in-house. In this study, we investigate whether changes in tumor microvasculature, measured using AA, can be an early indicator of high-dose radiotherapy success, compared to changes in tumor volume. Methods: Fibrosarcoma xenograft tumor tissue was subcutaneously implanted into rodent flanks (N=10). Animal tumors (N=8) were irradiated with a single treatment of 15Gy using a clinical LINAC at 100SSD and 2×2cm field size. Two untreated rats were left as tumor controls. AA imaging was performed immediately posttreatment and every third day thereafter for 30 days, or until tumors disappeared. Tumor volumes and vascular densities were measured from anatomical b-mode ultrasound and AA images, respectively. Results: Statistical differences in vascular density between treatment responders and non-responders were observed on Day 10 (p=0.005), whereas statistical differences in tumor volume were not observed until Day 19 (p=0.02). Conclusions: Tumor vascularity differences may be observed substantially earlier than differences in tumor size. In addition, significant early increases in vascular density were observed in non-responding tumors. This data is consistent with a similar study we completed using the same tumor and animal models (N=10) at 20Gy. The project described was supported by the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, through Grant Award Number UL1TR001111. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-FG-202-06: The Use of Hybrid PET MRI for Identifying the Presence of Cardiac Inflammation Following External Beam Irradiation

Purpose: To monitor the evolution of radiation-induced cardiac inflammation in a canine model using hybrid positron emission tomography (PET/magnetic resonance imaging (MRI). Methods: Research ethics approval was obtained for a longitudinal imaging study of 5 canines after cardiac irradiation. Animals were imaged at baseline, 3 months, 6 months, and 12 months post cardiac irradiation using a hybrid PET-MRI system (Biograph mMR, Siemens Healthcare). The imaging protocol was designed to assess changes in cardiac inflammation using {sup 18}F-fluorodeoxyglucose ({sup 18}F-FDG) PET tracer. In order to image cardiac inflammation, the normal myocardial uptake of glucose was suppressed prior to the injection of {sup 18}F-FDG. The suppression of glycolysis was achieved through; fasting (16–21 hours prior to the start of imaging) and an intravenous injection of heparin immediately followed by a 20% lipid infusion 20 min prior to the injection of {sup 18}F-FDG. The standard uptake value (SUV) obtained from 17 myocardial regions were used to compare FDG scans. All animals received a simulation CT scan (GE Medical Systems) for radiation treatment planning. Radiation treatment plans were created using the Pinncale3 treatment planning system (Philips Radiation Oncology Systems) and designed to resemble the typical cardiac exposure during left-sided breast cancer radiotherapy. Cardiac irradiations were performed in a single fraction using a TrueBeam linear accelerator (Varian Medical Systems). Results: The delivered dose (mean ± standard error) to heart, left ventricle, and left anterior descending artery were 1.7±0.1 Gy, 2.7±0.1 Gy, and 5.5±0.3 Gy respectively. At these doses, a significant increase in {sup 18}F-FDG uptake within the entire heart relative to baseline (1.1±0.02 g/ml) uptake was observed. {sup 18}F-FDG uptake at 3 months, 6 months, and 12 months post irradiation were 1.8±0.03 g/ml, 2.4±0.06 g/ml, and 2.6±0.11 g/ml respectively. Conclusion: Low doses of limited cardiac irradiation show evidence of a persistent global inflammatory response that can be detected using {sup 18}F-FDG PET imaging. This work was supported through the Translational Breast Cancer Studentship award, funded in part by the Breast Cancer Society of Canada. Additional financial support is provided by the London Regional Cancer Program Catalyst Grant and the Thames Valley Veterinary Services.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-FG-207B-04: Noise Suppression for Energy-Resolved CT Via Variance Weighted Non-Local Filtration

Purpose: The photon starvation problem is exacerbated in energy-resolved CT, since the detected photons are shared by multiple energy channels. Using pixel similarity-based non-local filtration, we aim to produce accurate and high-resolution energy-resolved CT images with significantly reduced noise. Methods: Averaging CT images reconstructed from different energy channels reduces noise at the price of losing spectral information, while conventional denoising techniques inevitably degrade image resolution. Inspired by the fact that CT images of the same object at different energies share the same structures, we aim to reduce noise of energy-resolved CT by averaging only pixels of similar materials - a non-local filtration technique. For each CT image, an empirical exponential model is used to calculate the material similarity between two pixels based on their CT values and the similarity values are organized in a matrix form. A final similarity matrix is generated by averaging these similarity matrices, with weights inversely proportional to the estimated total noise variance in the sinogram of different energy channels. Noise suppression is achieved for each energy channel via multiplying the image vector by the similarity matrix. Results: Multiple scans on a tabletop CT system are used to simulate 6-channel energy-resolved CT, with energies ranging from 75 to 125 kVp. On a low-dose acquisition at 15 mA of the Catphan©600 phantom, our method achieves the same image spatial resolution as a high-dose scan at 80 mA with a noise standard deviation (STD) lower by a factor of >2. Compared with another non-local noise suppression algorithm (ndiNLM), the proposed algorithms obtains images with substantially improved resolution at the same level of noise reduction. Conclusion: We propose a noise-suppression method for energy-resolved CT. Our method takes full advantage of the additional structural information provided by energy-resolved CT and preserves image values at each energy level. Research reported in this publication was supported by the National Institute Of Biomedical Imaging And Bioengineering of the National Institutes of Health under Award Number R21EB019597. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-AB-201-03: Characterization of Electron Beam Out-Of-Field Doses and Neutron Contamination from Modern Varian and Elekta Linear Accelerators

Purpose: To investigate out-of-field electron doses and neutron production from electron beams from modern Varian and Elekta linear accelerators. Methods: Electron dose measurements were made using 10×10cm{sup 2} applicators on two Varian 21iXs, a Varian TrueBeam, and an Elekta Versa HD operating at energies from 6 to 20 MeV. Out-of-field dose profiles and PDD curves were measured in a Wellhofer water phantom using a Farmer chamber. Neutron measurements were made with a combination of moderator buckets and gold activation-foils placed on the treatment couch at various locations in the patient plane on both the 21iX and Versa HD linear accelerators. Results: Electron doses were highest for the highest electron energies. Dose profile curves for the Varian units were found to be lower than those from the Versa HD unit, and were lower than photon beams. Elekta’s dose profiles were higher and exhibited a second dose peak around 20–30 cm from central-axis. Electron doses in this region (0.8–1.3% of dmax at central-axis) were close to 5 times (2.5–4.8) greater than doses from photon beams with similar energies. Electron doses decreased sharply with depth before becoming nearly constant; the dose was found to decrease to a depth of approximately E(MeV)/4 in cm. Q-values and neutron dose equivalent increased with energy and were typically higher on central-axis. 18 MV photon beam neutron dose equivalents were greater than any electron beam, being approximately 40 times greater than for the 20 MeV electron beam (21iX). Conclusion: The Versa HD exhibited higher than expected out-of-field electron doses in comparison to typical radiotherapy photon beams. Fortunately, out-of-field electron doses can be substantially reduced by applying a water-equivalent bolus with thickness of E(MeV)/4 in cm. Neutron contamination from clinical electron beams can be considered negligible in relation to photon beams but may need to be considered for special cases. This work was supported by Public Health Service Grant CA180803 awarded by the National Cancer Institute, United States Department of Health and Human Services.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

TH-AB-209-03: Overcoming Resolution Limitations of Diffuse Optical Signals in X-Ray Induced Luminescence (XIL) Imaging Via Selective Plane Illumination and 2D Deconvolution

Purpose: To evaluate the resolution and sensitivity of XIL imaging using a surface radiance simulation based on optical diffusion and maximum likelihood expectation maximization (MLEM) image reconstruction. XIL imaging seeks to determine the distribution of luminescent nanophosphors, which could be used as nanodosimeters or radiosensitizers. Methods: The XIL simulation generated a homogeneous slab with optical properties similar to tissue. X-ray activated nanophosphors were placed at 1.0 cm depth in the tissue in concentrations of 10{sup −4} g/mL in two volumes of 10 mm{sup 3} with varying separations between each other. An analytical optical diffusion model determined the surface radiance from the photon distributions generated at depth in the tissue by the nanophosphors. The simulation then determined the detected luminescent signal collected with a f/1.0 aperture lens and back-illuminated EMCCD camera. The surface radiance was deconvolved using a MLEM algorithm to estimate the nanophosphors distribution and the resolution. To account for both Poisson and Gaussian noise, a shifted Poisson imaging model was used in the deconvolution. The deconvolved distributions were fitted to a Gaussian after radial averaging to measure the full width at half maximum (FWHM) and the peak to peak distance between distributions was measured to determine the resolving power. Results: Simulated surface radiances for doses from 1mGy to 100 cGy were computed. Each image was deconvolved using 1000 iterations. At 1mGy, deconvolution reduced the FWHM of the nanophosphors distribution by 65% and had a resolving power is 3.84 mm. Decreasing the dose from 100 cGy to 1 mGy increased the FWHM by 22% but allowed for a dose reduction of a factor of 1000. Conclusion: Deconvolving the detected surface radiance allows for dose reduction while maintaining the resolution of the nanophosphors. It proves to be a useful technique in overcoming the resolution limitations of diffuse optical imaging in tissue. C. S. acknowledges support from the NIH National Institute of General Medical Sciences (Award number R25GM109439, Project Title: University of Chicago Initiative for Maximizing Student Development, IMSD). B. Q. and P. L. acknowledge support from NIH grant R01EB017293.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-CD-207B-11: Multi-Vendor Phantom Study of CT Lung Density Metrics: Is a Reproducibility of Less Than 1 HU Achievable?

Purpose: To standardize the calibration procedures of CT lung density measurements using low-density reference foams in a phantom, and to demonstrate a reproducibility of less than 1 HU for lung equivalent foam densities measured across CT vendor platforms and protocols. Methods: A phantom study was conducted on CT scanner models from 4 vendors at 100, 120, and 135/140 kVp and 1.5, 3, and 6 mGy dose settings, using a lung density phantom containing air, water, and 3 reference foams (indirectly calibrated) with discrete densities simulating a 5-cm slice of the human chest. Customized segmentation software was used to analyze the images and generate a mean HU and variance for each of the density for the 22 vendor/protocols. A 3-step calibration process was devised to remove a scanner-dependent parameter using linear regression of the HU value vs the relative electron density. The results were mapped to a single energy (80 keV) for final comparison. Results: The heterogeneity across vendor platforms for each density assessed by a random effects model was reduced by 50% after re-calibration, while the standard deviation of the mean HU values also improved by about the same amount. The 95% CI of the final HU value was within +/−1 HU for all 3 reference foam densities. For the backing lung foam in the phantom (served as an “unknown”), this CI is +/− 1.6 HU. The kVp and dose settings did not appear to have significant contributions to the variability. Conclusion: With the proposed calibration procedures, the inter-scanner reproducibility of better than 1 HU is demonstrated in the current phantom study for the reference foam densities, but not yet achieved for a test density. The sources of error are being investigated in the next round of scanning with a certified Standard Reference Material for direct calibration. Fain: research funding from GE Healthcare to develop pulmonary MRI techniques. Hoppel: employee of Toshiba Medical Research Institute USA/financial interest with GE Healthcare. M. Fuld: employee of Siemens Healthcare for medical device equipment and software. This project is supported partially by RSNA QIBA Concept Award (Fain), NIH/NIBIB, HHSN268201300071C (Y).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-EF-BRA-03: Assessment of Data-Driven Respiratory Motion-Compensation Methods for 4D-CBCT Image Registration and Reconstruction Using Clinical Datasets

Purpose: To evaluate the performance of a 4D-CBCT registration and reconstruction method that corrects for respiratory motion and enhances image quality under clinically relevant conditions. Methods: Building on previous work, which tested feasibility of a motion-compensation workflow using image datasets superior to clinical acquisitions, this study assesses workflow performance under clinical conditions in terms of image quality improvement. Evaluated workflows utilized a combination of groupwise deformable image registration (DIR) and image reconstruction. Four-dimensional cone beam CT (4D-CBCT) FDK reconstructions were registered to either mean or respiratory phase reference frame images to model respiratory motion. The resulting 4D transformation was used to deform projection data during the FDK backprojection operation to create a motion-compensated reconstruction. To simulate clinically realistic conditions, superior quality projection datasets were sampled using a phase-binned striding method. Tissue interface sharpness (TIS) was defined as the slope of a sigmoid curve fit to the lung-diaphragm boundary or to the carina tissue-airway boundary when no diaphragm was discernable. Image quality improvement was assessed in 19 clinical cases by evaluating mitigation of view-aliasing artifacts, tissue interface sharpness recovery, and noise reduction. Results: For clinical datasets, evaluated average TIS recovery relative to base 4D-CBCT reconstructions was observed to be 87% using fixed-frame registration alone; 87% using fixed-frame with motion-compensated reconstruction; 92% using mean-frame registration alone; and 90% using mean-frame with motion-compensated reconstruction. Soft tissue noise was reduced on average by 43% and 44% for the fixed-frame registration and registration with motion-compensation methods, respectively, and by 40% and 42% for the corresponding mean-frame methods. Considerable reductions in view aliasing artifacts were observed for each method. Conclusion: Data-driven groupwise registration and motion-compensated reconstruction have the potential to improve the quality of 4D-CBCT images acquired under clinical conditions. For clinical image datasets, the addition of motion compensation after groupwise registration visibly reduced artifact impact. This work was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA166119. Hugo and Weiss hold a research agreement with Philips Healthcare and license agreement with Varian Medical Systems. Weiss receives royalties from UpToDate. Christensen receives funds from Roger Koch to support research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Generalizable coordination of large multiscale workflows: challenges and learnings at scale

The advancement of machine learning techniques and the heterogeneous architectures of most current supercomputers are propelling the demand for large multiscale simulations that can automatically and autonomously couple diverse components and map them to relevant resources to solve complex problems at multiple scales. Nevertheless, despite the recent progress in workflow technologies, current capabilities are limited to coupling two scales. In the first-ever demonstration of using three scales of resolution, we present a scalable and generalizable framework that couples pairs of models using machine learning and in situ feedback. We expand upon the massively parallel Multiscale Machine-Learned Modeling Infrastructure (MuMMI), a recent, award-winning workflow, and generalize the framework beyond its original design. We discuss the challenges and learnings in executing a massive multiscale simulation campaign that utilized over 600,000 node hours on Summit and achieved more than 98% GPU occupancy for more than 83% of the time. We present innovations to enable several orders of magnitude scaling, including simultaneously coordinating 24,000 jobs, and managing several TBs of new data per day and over a billion files in total. Finally, we describe the generalizability of our framework and, with an upcoming open-source release, discuss how the presented framework may be used for new applications.

Bhatia, Harsh↗
DOE OSTI2012

Building Agent™ Software (BA) [SWRs 12-05 and 17-21]

Energy interfaces provide a new level of motivational information. Many studies have reported energy savings after dashboards for operators are installed, but these provide little actionable information to the occupant and the occupant cannot report individual comfort. The Building Agent™ (BA), in development at the National Renewable Energy Laboratory (NREL), provides an interface for the occupant to converse with the building control system and aims to actively engage and affect occupant behavior to achieve positive energy outcomes. Energy savings achieved via occupant behavioral changes could be as high as 25%, and could rival savings achieved by technological measures. For example, energy audits often show that more than 50% of the energy used in a building is consumed at night. Occupants leaving equipment and lighting on after they leave work is a large contributing factor. Similar savings opportunities have been identified when occupants were away from their desks 50% of the time, while most of their computers, lights, and other equipment remained powered on. Building Agent aims to create cohesion between building automation system (BAS) data, local measurements, and occupant feedback to provide visualizations, empowering occupants and building engineers to take diagnostic, proactive, energy-saving, and comfort-improving actions. The multilayer BA architecture consists of central server, measurement, and distributed application layers. The server layer aggregates global and localized measurements with occupant feedback to correlate perceived comfort with actual data throughout the building. The measurement layer complements BAS data with localized, environmental condition measurements at workstations. The application layer provides a bidirectional communications mechanism between the building and the occupants that accepts comfort feedback and provides energy performance information. The interface layer also uses three displays to influence building energy use: empathetic and personalized gauges, graphical data, and competitive rankings. Building Agent was implemented at NREL in the Research Support Facility (RSF), a LEED Platinum and Net Zero office building, which has received many awards. The constant feedback from occupants has enabled BA to better enhance the BAS data controls. Continued work is being done on BA to increase visualization of data and enhanced the user experience.

Long, Nicholas↗
DOE OSTI2023

GOAT. jl

SF-23-008 This project is a Julia implementation of the Gradient Optimization of Analytic conTrols (GOAT) optimal control methodology. It integrates with other packages in Julia's ecosystem to provide memory-efficient, parallelized solutions to quantum optimal control tasks. A prototype implementation of some of these algorithms was initially developed and funded by the ASCR Early Career Research Award program under PI Travis Humble at Oak Ridge National Laboratory. The current version was funded under the ASCR AIDE-QC Program under PI Paul Hovland. The current package to be released has a novel implementation, syntax, and structure making it substantially different than the original prototype (which was not released under copyright to the best of my knowledge).

KAIRYS, PAUL↗
DOE OSTI2020

GHEP WIBT (Guidelines for Home Energy Professionals Weatherization Installer Badges Toolkit) [SWR-20-69]

The National Renewable Energy Laboratory (NREL) and the U.S. Department of Energy's (DOE) Weatherization Assistance Program (WAP) are collaborating with the home energy retrofit industry to support the development of skilled workers. The Installer Badges Toolkit provides a flexible, customizable, and voluntary approach to training and skills recognition for WAP implementers, utility programs, private-sector workers, and contractors. In Fiscal Year 2018, DOE, along with the Crew Leader scheme committee, determined the Retrofit Installer Technician (RIT) Job Task Analysis (JTA) could be eliminated and its tasks inserted in the Crew Leader JTA. The RIT tasks became the basis of the Installer Badges. The Badges Toolkit was updated in 2020 to add licensing and copyright agreements. Users must cite the copyright information when using or distributing copies of the Badges Toolkit in any way. The technical content has remained unchanged from previous versions of the Badges Toolkit. The Toolkit for home retrofits consists of 25 Badges, each representing different energy efficiency tasks that an installer could perform on a home. Each Badge defines the desired outcome, criteria to verify, applicable material requirements, and references to Standard Work Specifications (SWS) or other relevant standards. The Badges provide a consistent approach to training by ensuring that installers in different regions are learning the same skills nationwide. Organizations can also customize the Toolkit by choosing only those Badges that are relevant to their program. The Badges Toolkit includes five pieces: "How to Use the Badges Toolkit" provides a brief overview of how a Grantee, Subgrantee, or training provider may approach using the toolkit. The "Badges Toolkit: Worksheet" includes what to consider when determining whether and how to best incorporate the Badges into a weatherization assistance program. The "Crew Leader Job Task Analysis Spreadsheet" indicates how the Badges align with specific areas of the JTA. The "Installer Badges Passport" features separate pages for each Badge, which include places for the installer and the supervisor/trainer to record the number of times a task has been successfully completed. The "Installer Badges Verification Criteria" includes sample inspection checklists for each Badge. These can be modified as needed based on approved variance requests or more stringent requirements. They also provide a basis for consistent inspections and awarding of Badges. Note: All elements of the Badges Toolkit are designed for use by potential program implementers. They are not off-the-shelf products intended for immediate deployment. Program implementers must review and modify as needed, revise verification criteria to match local requirements, complete worksheets, and otherwise define the parameters of their own badging program.

Desai, Jal↗
DOE OSTI2024

ComStock™ 2024 Release 1 [SWR-19-33 and SWR-20-32]

ComStock™ is an NREL model of the U.S. commercial building stock. The model takes some building characteristics from the U.S. Department of Energy's (DOE's) Commercial Prototype Building Models and Commercial Reference Building. However, unlike many other building stock models, ComStock also combines these with a variety of additional public- and private-sector data sets. Collectively, this information provides high-fidelity building stock representation with a realistic diversity of building characteristics. This repository contains the source code used to build and execute ComStock models, including upgrade scenarios. In addition, the sampling of buildings characteristics used for the initial ComStock (V1.0) release is provided. The ComStock model is under active calibration and development, which is publicly visible on this repository. Execution of the ComStock workflow is managed through the buildstockbatch repository, a shared asset of ResStock™ and ComStock™ , specifically developed to scale to execution of tens of millions of simulations through multiple infrastructure providers. The dataset output from the initial ComStock (V1.0) release can be found at the accompanying ComStock data viewer website and additional information about ComStock found on the NREL Buildings Website. For more details about ongoing model development please consult the End Use Load Profiles website. ComStock is a direct result of the NREL residential stock modeling tool ResStock™ (recipient of a R&D100 award) and was inspired by the high-fidelity solar & storage adoption model dGen™. Additionally, this tool would not be possible without the decades of work undertaken by the OpenStudio® and EnergyPlus® visionaries and contributors, significant funding, feedback and support from the Los Angeles Department of Water and Power, and the Department of Energy's Building Technology Office ongoing support of and investment in building energy modeling software. is an analytic methodology for modeling the energy usage of the commercial building stock within the United States of America. The commercial building stock is represented through a sampling of complex probabilistic distributions of various features of interest for modeling energy usage within commercial buildings. Each sample from these distributions is converted into a building energy model based on the features of that specific sample. Each building energy model can be simulated as is, but additional changes can be made to the model through addition of energy conservation measures, component faults, or other desired alterations. The results of the simulations are then processed to provide insights for various stakeholders, including but not limited to policy makers, engineers, and marketers.

Horsey, Henry↗