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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Control of Technology Transfer at JPL

Controlled Technology: 1) Design: preliminary or critical design data, schematics, technical flow charts, SNV code/diagnostics, logic flow diagrams, wirelist, ICDs, detailed specifications or requirements. 2) Development: constraints, computations, configurations, technical analyses, acceptance criteria, anomaly resolution, detailed test plans, detailed technical proposals. 3) Production: process or how-to: assemble, operated, repair, maintain, modify. 4) Manufacturing: technical instructions, specific parts, specific materials, specific qualities, specific processes, specific flow. 5) Operations: how-to operate, contingency or standard operating plans, Ops handbooks. 6) Repair: repair instructions, troubleshooting schemes, detailed schematics. 7) Test: specific procedures, data, analysis, detailed test plan and retest plans, detailed anomaly resolutions, detailed failure causes and corrective actions, troubleshooting, trended test data, flight readiness data. 8) Maintenance: maintenance schedules and plans, methods for regular upkeep, overhaul instructions. 9) Modification: modification instructions, upgrades kit parts, including software

Jet Propulsion Laboratory (JPL)↗
NASA NTRS2010

Use of DSC and DMA Techniques to Help Investigate a Material Anomaly for PTFE Used in Processing a Piston Cup for the Urine Processor Assembly (UPA) on International Space Station (ISS)

Human urine and flush water are eventually converted into drinking water with the Urine Processor Assembly (UPA) aboard the International Space Station (ISS). This conversion is made possible through the Distillation Assembly (DA) of the UPA. One component of the DA is a molded circular piston cup made of virgin polytetrafluoroethylene (PTFE). The piston cup is assembled to a titanium component using eight fasteners and washers. Molded PTFE produced for spare piston cups in the first quarter of 2010 was different in appearance and texture, and softer than material molded for previous cups. For the suspect newer PTFE material, cup fasteners were tightened to only one-half the required torque value, yet the washers embedded almost halfway into the material. The molded PTFE used in the DA piston cup should be Type II, based on AMS 3667D and ASTM D4894 specifications. The properties of molded PTFE are considerably different between Type I and II materials. Engineers working with the DA thought that if Type I PTFE was molded by mistake instead of Type II material, that could have resulted in the anomalous material properties. Typically, the vendor molds flat sheet PTFE from the same material lot used to mold the piston cups, and tensile testing as part of quality control should verify that the PTFE is Type II material. However, for this discrepant lot of material, such tensile data was not available. Differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA) were two of the testing techniques used at the NASA/Marshall Space Flight Center (MSFC) to investigate the anomaly for the PTFE material. Other techniques used on PTFE specimens were: Shore D hardness testing, tensile testing on dog bone specimens and a qualitative estimation of porosity by optical and scanning electron microscopy.

Wingard, Doug↗
NASA NTRS2011

Meteorological Necessities for the Stratospheric Observatory for Infrared Astronomy

The Stratospheric Observatory for Infrared Astronomy (SOFIA) is joint program with NASA and DLR (German Aerospace Center) of a highly modified Boeing 747-SP. The purpose of this modification is to include a 2.5 m infrared telescope in a rear bulkhead of the airplane, with a retractable door open to the atmosphere. The NASA Dryden Flight Research Center (DFRC) is responsible for verifying that the aerodynamics, acoustics, and flying qualities of the modified aircraft stay within safe limits. Flight testing includes determining meteorological limitations of the aircraft, which is done by setting strict temporary operating limits and verifying through data analysis, what conditions are acceptable. Line operations are calibration tests of various telescope instruments that are done on the ground prior to flights. The method in determining limitations for this type of operation is similar to that of flight testing, but the meteorological limitations are different. Of great concern are the particulates near the surface that could cause damage to the telescope, as well as condensation forming on the mirror. Another meteorological involvement for this program is the process of obtaining Reduced Vertical Separation Minimums (RVSM) Certification from the FAA. This heavily involves obtaining atmospheric data pertinent to the flight, analyzing data to actual conditions for validity, and computing necessary results for comparison to aircraft instrumentation.

Houtas, Franzeska↗
NASA NTRS2010

Global View of Aerosol Vertical Distributions from CALIPSO Lidar Measurements and GOCART Simulations: Regional and Seasonal Variations

This study examines seasonal variations of the vertical distribution of aerosols through a statistical analysis of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar observations from June 2006 to November 2007. A data-screening scheme is developed to attain good quality data in cloud-free conditions, and the polarization measurement is used to separate dust from non-dust aerosol. The CALIPSO aerosol observations are compared with aerosol simulations from the Goddard Chemistry Aerosol Radiation Transport (GOCART) model and aerosol optical depth (AOD) measurements from the MODerate resolution Imaging Spectroradiometer (MODIS). The CALIPSO observations of geographical patterns and seasonal variations of AOD are generally consistent with GOCART simulations and MODIS retrievals especially near source regions, while the magnitude of AOD shows large discrepancies in most regions. Both the CALIPSO observation and GOCART model show that the aerosol extinction scale heights in major dust and smoke source regions are generally higher than that in industrial pollution source regions. The CALIPSO aerosol lidar ratio also generally agrees with GOCART model within 30% on regional scales. Major differences between satellite observations and GOCART model are identified, including (1) an underestimate of aerosol extinction by GOCART over the Indian sub-continent, (2) much larger aerosol extinction calculated by GOCART than observed by CALIPSO in dust source regions, (3) much weaker in magnitude and more concentrated aerosol in the lower atmosphere in CALIPSO observation than GOCART model over transported areas in midlatitudes, and (4) consistently lower aerosol scale height by CALIPSO observation than GOCART model. Possible factors contributing to these differences are discussed.

Yu, Hongbin↗
NASA NTRS2012

Special Issue of Selected Papers from Visualization and Data Analysis 2011

This special issue features the best papers that were selected from the 18th SPIE Conference on Visualization and Data Analysis (VDA 2011). This annual conference is a major international forum for researchers and practitioners interested in data visualization and analytics research, development, and applications. VDA 2011 received 42 high-quality submissions from around the world. Twenty-four papers were selected for full conference papers. The top five papers have been expanded and reviewed for this special issue.

Kao, David L.↗
NASA NTRSDate not supplied

Determining the Most Influencing Medical Conditions in MEDPRAT’s SIN Directed Graph

INTRODUCTION: The Susceptibility Inference Network (SIN) is a network of medical conditions, part of the Medical Extensible Probabilistic Risk Assessment Tool (MEDPRAT) developed by NASA to assess human health and medical risk to space exploration missions. The SIN is subject matter expert informed and acts as a prototype that provides relationships and dependencies between events modeled by MEDPRAT. Each vertex in the SIN has a weight which evaluates the severity of having the condition regardless of the progression from or to that condition. In this presentation, we consider two statistics to measure that stand alone risk: Quality Time Lost (QTL) and Loss of Crew Life (LOCL). Our goal is to identify the medical conditions that contribute the most to crew members QTL and LOCL risks due to progression of conditions in the network. We investigate how different computation parameters result in different condition rankings and address the choice of parameters that allows appropriate interventions to ensure space mission success. METHODS: The Katz score, one of many centrality measures created for ranking purposes in network analysis, takes into account all possible walks through the network, penalizing each additional step in a walk by a factor α called the Katz parameter. The literature does not provide specific values for the choice of α. We derive an analytical relationship between α and the maximum path length which has influence on the Katz score and ranking. Based on the probability of progression of each condition in the SIN, we identify that maximum path length of interest and calculate α that is then used in the Katz formula to rank the conditions in the SIN. RESULTS AND CONCLUSION: The effective probabilities of the SIN matrix generally fall below 10−6, which is below the level of the least influencing condition in the set. This corresponds to the probability of at most six consecutive progressions of a condition. Consequently, we calculate the Katz Parameter α and get 0.32. We rank the medical conditions and find that Acute Radiation Symptom is the condition the most prone to contribute to quality time loss due to progression.

risk analysis↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Transforming Energy Through Sustainable Mobility: Expanding Low-Carbon Transportation R&D Solutions

As the nation's premier laboratory for cutting-edge transportation decarbonization research and development solutions, the National Renewable Energy Laboratory (NREL) pioneers the creation and deployment of sustainable mobility technologies and strategies, with a focus on slashing transportation sector greenhouse gas emissions and combatting climate change. Trucks, planes, cargo ships, and other difficult-to-decarbonize vehicles are part of this essential transition for the transportation sector, which is currently the nation's largest source of the greenhouse gas emissions. NREL provides the scientific building blocks needed to spur innovation through multifaceted analysis, research, and engineering. This work acts as a catalyst to help industry bring affordable, high-performance, energy-efficient, and low-emission modes of transport and related infrastructure to market sooner. Our researchers collaborate closely with academic, government, and industry partners to design better batteries, drivetrains, and engines. They develop technologies for high-power charging, thermal management, energy storage, and power electronics. They are also reimagining fuels and combustion while creating sustainable lightweight materials. Unbiased expert guidance - backed by credible data and analysis, tools, and scientific rigor - empowers partners to make informed decisions about sustainable transportation. NREL recognizes that communities with limited mobility options face reduced access to employment opportunities, health care, and education, lowering overall quality of life. Alongside partners, NREL experts are creating transportation solutions that meet community-identified needs and increase mobility equity in historically underserved and overburdened communities. Rather than providing a one-size-fits-all solution, we take an interdisciplinary approach to mobility equity that considers the needs and challenges of diverse groups, maximizing benefits at the individual, community, and societal levels.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Optimization of key energy and performance metrics for drug product manufacturing

During the development of pharmaceutical manufacturing processes, detailed systems-based analysis and optimization are required to control and regulate critical quality attributes within specific ranges, to maintain product performance. As discussions on carbon footprint, sustainability, and energy efficiency are gaining prominence, the development and utilization of these concepts in pharmaceutical manufacturing are seldom reported, which limits the potential of pharmaceutical industry in maximizing key energy and performance metrics. Based on an integrated modeling and techno-economic analysis framework previously developed by the authors, this study presents the development of a combined sensitivity analysis and optimization approach to minimize energy consumption while maintaining product quality and meeting operational constraints in a pharmaceutical process. The optimal input process conditions identified were validated against experiments and good agreement resulted between simulated and experimental data. Here, the results also allowed for a comparison of the capital and operational costs for batch and continuous manufacturing schemes under nominal and optimized conditions. Using the nominal batch operations as a basis, the optimized batch operation results in a 71.7% reduction of energy consumption, whereas the optimized continuous case results in an energy saving of 83.3%.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1997

CONDUIT: A New Multidisciplinary Integration Environment for Flight Control Development

A state-of-the-art computational facility for aircraft flight control design, evaluation, and integration called CONDUIT (Control Designer's Unified Interface) has been developed. This paper describes the CONDUIT tool and case study applications to complex rotary- and fixed-wing fly-by-wire flight control problems. Control system analysis and design optimization methods are presented, including definition of design specifications and system models within CONDUIT, and the multi-objective function optimization (CONSOL-OPTCAD) used to tune the selected design parameters. Design examples are based on flight test programs for which extensive data are available for validation. CONDUIT is used to analyze baseline control laws against pertinent military handling qualities and control system specifications. In both case studies, CONDUIT successfully exploits trade-offs between forward loop and feedback dynamics to significantly improve the expected handling, qualities and minimize the required actuator authority. The CONDUIT system provides a new environment for integrated control system analysis and design, and has potential for significantly reducing the time and cost of control system flight test optimization.

Tischler, Mark B.↗
NASA NTRS2020

Understanding and Improving Model Representation of Aerosol Optical Properties for a Chinese Haze Event Measured During KORUS-AQ

KORUS-AQ was an international cooperative air quality field study in South Korea that measured local and remote sources of air pollution affecting the Korean Peninsula during May–June 2016. Some of the largest aerosol mass concentrations were measured during a Chinese haze transport event (24 May). Air quality forecasts using the WRF-Chem model with aerosol optical depth (AOD) data assimilation captured AOD during this pollution episode but overpredicted surface particulate matter concentrations in South Korea, especially PM2.5, often by a factor of 2 or larger. Analysis revealed multiple sources of model deficiency related to the calculation of optical properties from aerosol mass that explain these discrepancies. Using in situ observations of aerosol size and composition as inputs to the optical properties calculations showed that using a low-resolution size bin representation (four bins) underestimates the efficiency with which aerosols scatter and absorb light (mass extinction efficiency). Besides using finer-resolution size bins (8–16 bins), it was also necessary to increase the refractive indices and hygroscopicity of select aerosol species within the range of values reported in the literature to achieve better consistency with measured values of the mass extinction efficiency (6.7 m2 g−1 observed average) and light-scattering enhancement factor (f(RH)) due to aerosol hygroscopic growth (2.2 observed average). Furthermore, an evaluation of the optical properties obtained using modeled aerosol properties revealed the inability of sectional and modal aerosol representations in WRF-Chem to properly reproduce the observed size distribution, with the models displaying a much wider accumulation mode. Other model deficiencies included an underestimate of organic aerosol density (1.0 g cm−3 in the model vs. observed average of 1.5 g cm−3) and an overprediction of the fractional contribution of submicron inorganic aerosols other than sulfate, ammonium, nitrate, chloride, and sodium corresponding to mostly dust (17 %–28 % modeled vs. 12 % estimated from observations). These results illustrate the complexity of achieving an accurate model representation of optical properties and provide potential solutions that are relevant to multiple disciplines and applications such as air quality forecasts, health impact assessments, climate projections, solar power forecasts, and aerosol data assimilation.

Pablo E. Saide↗
NASA NTRS1986

Flared landing approach flying qualities. Volume 1: Experiment design and analysis

An inflight research study was conducted utilizing the USAF Total Inflight Simulator (TIFS) to investigate longitudinal flying qualities for the flared landing approach phase of flight. The purpose of the experiment was to generate a consistent set of data for: (1) determining what kind of commanded response the pilot prefers in order to flare and land an airplane with precision, and (2) refining a time history criterion that took into account all the necessary variables and their characteristics that would accurately predict flying qualities. The result of the first part provides guidelines to the flight control system designer, using MIL-F-8785-(C) as a guide, that yield the dynamic behavior pilots perfer in flared landings. The results of the second part provides the flying qualities engineer with a newly derived flying qualities predictive tool which appears to be highly accurate. This time domain predictive flying qualities criterion was applied to the flight data as well as six previous flying qualities studies, and the results indicate that the criterion predicted the flying qualities level 81% of the time and the Cooper-Harper pilot rating, within + or - 1, 60% of the time.

Weingarten, Norman C.↗
NASA NTRS2010

Improving Forecast Skill by Assimilation of AIRS Temperature Soundings

AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU-A are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The AIRS Version 5 retrieval algorithm, is now being used operationally at the Goddard DISC in the routine generation of geophysical parameters derived from AIRS/AMSU data. A major innovation in Version 5 is the ability to generate case-by-case level-by-level error estimates delta T(p) for retrieved quantities and the use of these error estimates for Quality Control. We conducted a number of data assimilation experiments using the NASA GEOS-5 Data Assimilation System as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The model was run at a horizontal resolution of 0.5 deg. latitude X 0.67 deg longitude with 72 vertical levels. These experiments were run during four different seasons, each using a different year. The AIRS temperature profiles were presented to the GEOS-5 analysis as rawinsonde profiles, and the profile error estimates delta (p) were used as the uncertainty for each measurement in the data assimilation process. We compared forecasts analyses generated from the analyses done by assimilation of AIRS temperature profiles with three different sets of thresholds; Standard, Medium, and Tight. Assimilation of Quality Controlled AIRS temperature profiles significantly improve 5-7 day forecast skill compared to that obtained without the benefit of AIRS data in all of the cases studied. In addition, assimilation of Quality Controlled AIRS temperature soundings performs better than assimilation of AIRS observed radiances. Based on the experiments shown, Tight Quality Control of AIRS temperature profile performs best on the average from the perspective of improving Global 7 day forecast skill.

Susskind, Joel↗
DOE OSTI2022

Gamma Spectrometry Code Rodeo for Uranium Enrichment—FY 2021 Report

In FY22, the participating Department of Energy (DOE) laboratories continued the work on the project “Gamma Spectrometry Code Rodeo for Uranium Enrichment”. Oak Ridge National Laboratory (ORNL) and Lawrence Livermore National Laboratory (LLNL) continued their efforts to acquire spectra using uranium sources. A collimator (with side and back shields) was custom designed and constructed at ORNL. Spectra were acquired at ORNL using the uranium enrichment standards supplied by New Brunswick Laboratory (NBL) and the collimated M400 detector. Spectra were also acquired using steel absorbers of different thickness placed between the source and the collimated M400 detector. The plan was to ship the collimator to LLNL so that spectra could be acquired using the LLNL detector in collimated geometries. This activity was suspended since problems were noticed with the performance of the LLNL M400 detector. Also, it was brought to light to the project team that some of the aspects of the M400 detector had been updated by the vendor H3D in response to requests by the IAEA. In February 2021, the ORNL and LLNL M400 detectors were returned to the H3D factory in order to resolve the problems with the LLNL detector and to update ORNL and LLNL detectors to the same configuration as the M400 supplied to the IAEA. H3D repaired the LLNL M400 and performed the IAEA updates on both the ORNL and LLNL detectors. ORNL and LLNL received the detectors in April 2021. Data acquisition was continued using the upgraded detectors. ORNL re-collected the spectra using point sources and the NBL standards in uncollimated and collimated geometries. The collimator was shipped from ORNL to LLNL. Spectra were acquired at LLNL through Q4 of FY22. When the ORNL spectra were examined closely, it became apparent that there were spurious artifacts present in some of the spectra. This rendered suspect the ORNL spectra collected in June/July 2021 time frame. ORNL contacted H3D and per H3D’s advice, the M400 detector was returned to H3D for repairs in mid-August 2021. H3D diagnosed the problem and established that the root cause was the extra tight packaging that created a strain on the CZT crystals, causing one of them to break down. The repaired M400 was returned to ORNL in early September 2021. ORNL re-started data acquisition for the third time. Data was continued to be acquired at ORNL and LLNL through September 2021. The ORNL and LLNL data will be shared with the principal investigators from the analyzing laboratories. The GADRAS code and FRAM have been developed to have the capability to analyze the M400 spectra. Good quality spectra from FY21 have been analyzed using modified versions of GADRAS and FRAM. The spectra have also been analyzed using the GEM code. The analysis results from GADRAS, FRAM and GEM codes are presented and discussed in this report. Some preliminary results from FRAM and GEM analysis were presented in February 2021 at the U-Pu Isotopics Workshop sponsored by the IAEA. CZTU code is being modified to analyze M400 spectra. Data collection, code development and analysis will be continued in FY22 as per the FY22 Project Work Plan (PWP).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?

Abstract The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub‐daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state‐of‐the art applications of ML for water quality models and discuss opportunities to improve the use of ML with emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model‐data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge‐guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision‐relevant predictions of riverine water quality.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

High-fidelity wind farm simulation methodology with experimental validation

The complexity and associated uncertainties involved with atmospheric-turbine-wake interactions produce challenges for accurate wind farm predictions of generator power and other important quantities of interest (QoIs), even with state-of-the-art high-fidelity atmospheric and turbine models. A comprehensive computational study was undertaken with consideration of simulation methodology, parameter selection, and mesh refinement on atmospheric, turbine, and wake QoIs to identify capability gaps in the validation process. For neutral atmospheric boundary layer conditions, the massively parallel large eddy simulation (LES) code Nalu-Wind was used to produce high-fidelity computations for experimental validation using high-quality meteorological, turbine, and wake measurement data collected at the Department of Energy/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility located at Texas Tech University’s National Wind Institute. The wake analysis showed the simulated lidar model implemented in Nalu-Wind was successful at capturing wake profile trends observed in the experimental lidar data.

17 WIND ENERGY↗
DOE OSTI2022

Monte Carlo Simulation and Reconstruction: Assessment of Myocardial Perfusion Imaging of Tracer Dynamics With Cardiac Motion Due to Deformation and Respiration Using Gamma Camera With Continuous Acquisition

Purpose: Myocardial perfusion imaging (MPI) with single photon emission computed tomography (SPECT) is routinely used for stress testing in nuclear medicine. Recently, our group extended its potential going from 3D visual qualitative image analysis to 4D spatiotemporal reconstruction of dynamically acquired data to capture the time variation of the radiotracer concentration and the estimated myocardial blood flow (MBF) and coronary flow reserve (CFR). However, the quality of reconstructed image is compromised due to cardiac deformation and respiration. The work presented here develops an algorithm that reconstructs the dynamic sequence of separate respiratory and cardiac phases and evaluates the algorithm with data simulated with a Monte Carlo simulation for the continuous image acquisition and processing with a slowly rotating SPECT camera. Methods: A clinically realistic Monte Carlo (MC) simulation is developed using the 4D Extended Cardiac Torso (XCAT) digital phantom with respiratory and cardiac motion to model continuous data acquisition of dynamic cardiac SPECT with slowly rotating gamma cameras by incorporating deformation and displacement of the myocardium due to cardiac and respiratory motion. We extended our previously developed 4D maximum-likelihood expectation-maximization (MLEM) reconstruction algorithm for a data set binned from a continuous list mode (LM) simulation with cardiac and respiratory information. Our spatiotemporal image reconstruction uses splines to explicitly model the temporal change of the tracer for each cardiac and respiratory gate that delineates the myocardial spatial position as the tracer washes in and out. Unlike in a fully list-mode data acquisition and reconstruction the accumulated photons are binned over a specific but very short time interval corresponding to each cardiac and respiratory gate. Reconstruction results are presented showing the dynamics of the tracer in the myocardium as it continuously deforms. These results are then compared with the conventional 4D spatiotemporal reconstruction method that models only the temporal changes of the tracer activity. Mean Stabilized Activity (MSA), signal to noise ratio (SNR) and Bias for the myocardium activities for three different target-to-background ratios (TBRs) are evaluated. Dynamic quantitative indices such as wash-in (K1) and wash-out (k2) rates at each gate were also estimated. Results: The MSA and SNR are higher with higher TBRs while biases were improved with higher TBRs to less than 10%. The correlation between exhalation-inhalation sequence with the ground truth during respiratory cycle was excellent. Our reconstruction method showed better resolved myocardial walls during diastole to systole as compared to the ungated 4D image. Estimated values of K1 and k2 were also consistent with the ground truth. Conclusion: The continuous image acquisition for dynamic scan using conventional two-head gamma cameras can provide valuable information for MPI. Our study demonstrated the viability of using a continuous image acquisition method on a widely used clinical two-head SPECT system. Our reconstruction method showed better resolved myocardial walls during diastole to systole as compared to the ungated 4D image. Precise implementation of reconstruction algorithms, better segmentation techniques by generating images of different tissue types and background activity would improve the feasibility of the method in real clinical environment.

60 APPLIED LIFE SCIENCES↗