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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

NLSI Focus Group on Missing ALSEP Data Recovery: Progress and Plans

On the six Apollo landed missions, the Astronauts deployed the Apollo Lunar Surface Experiments Package (ALSEP) science stations which measured active and passive seismic events, magnetic fields, charged particles, solar wind, heat flow, the diffuse atmosphere, meteorites and their ejecta, lunar dust, etc. Today's scientists are able to extract new information and make new discoveries from the old ALSEP data utilizing recent advances in computer capabilities and new analysis techniques. However, current-day investigators are encountering problems trying to use the ALSEP data. In 2007 archivists from NASA Goddard Space Flight Center (GSFC) National Space Science Data Center (NSSDC) estimated only about 50 percent of the processed ALSEP lunar surface data-of-interest to current lunar science investigators were in the NSSDC archives. The current-day lunar science investigators found most of the ALSEP data, then in the NSSDC archives. were extremely difficult to use. The data were in forms often not well described in the published reports and rerecording anomalies existed in the data which could only be resolved by tape experts. To resolve this problem, the DPS Lunar Data Node was established in 2008 at NSSDC and is in the process of successfully making the existing archived ALSEP data available to current-day investigators in easily useable forms. In July of 2010 the NASA Lunar Science Institute (NLSI) at Ames Research Center established the Recovery of Missing ALSEP Data Focus Group in recognition of the importance of the current activities to find the raw and processed ALSEP data missing from the NSSDC archives.

Lewis, L. R.↗
DOE OSTI2017

Imputation for multisource data with comparison and assessment techniques

Missing data are prevalent issue in analyses involving data collection. The problem of missing data is exacerbated for multisource analysis, where data from multiple sensors are combined to arrive at a single conclusion. In this scenario, it is more likely to occur and can lead to discarding a large amount of data collected; however, the information from observed sensors can be leveraged to estimate those values not observed. We propose two methods for imputation of multisource data, both of which take advantage of potential correlation between data from different sensors, through ridge regression and a state-space model. These methods, as well as the common median imputation, are applied to data collected from a variety of sensors monitoring an experimental facility. Performance of imputation methods is compared with the mean absolute deviation; however, rather than using this metric to solely rank themethods,we also propose an approach to identify significant differences. Imputation techniqueswill also be assessed by their ability to produce appropriate confidence intervals, through coverage and length, around the imputed values. Finally, performance of imputed datasets is compared with a marginalized dataset through a weighted k-means clustering. In general, we found that imputation through a dynamic linearmodel tended to be the most accurate and to produce the most precise confidence intervals, and that imputing the missing values and down weighting them with respect to observed values in the analysis led to the most accurate performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Assessing methods in fusion and fitting for time series construction in remote sensing-based earth observations

This study evaluates the comparative performance of spatiotemporal fusion and time-series fitting methods for constructing high-spatiotemporal-resolution remote sensing time-series data. Due to in-class similarity of fusion methods and fitting methods, we employ the Fit-FC (Fitting, spatial Filtering, and residual Compensation) model as a representative fusion method and the linear harmonic fitting model as a representative fitting method. Both Fit-FC and the linear harmonic fitting are widely used for high-spatiotemporal-resolution time-series data construction, and we modify the original Fit-FC model to enable automatic time-series fusion. To ensure data representativeness, we use 3 years (2019–2021) of Harmonized Landsat and Sentinel-2 surface reflectance datasets and Terra MCD43A4 products. Eight experimental regions are selected worldwide to guarantee generalization of the comparative performance between fusion and fitting methods, covering diverse land-use types (cropland, developed land, forest, and grassland) and varying climatological conditions. Time-series of NDVI and surface reflectance are analyzed under both actual observations and simulated data-missing scenarios. The constructed time-series data reveals that (1) the modified Fit-FC and linear harmonic fitting model achieve excellent performance in constructing high-resolution time-series images; (2) the fusion method outperforms the fitting method in constructing time-series of NDVI and surface reflectance images in cropland-, forest-, and grassland-dominated regions; (3) both methods achieve comparable performance in developed-dominated regions; (4) the fusion method is more robust to missing data, and better captures abrupt phenological transitions under conditions of continuous missing data; (5) the fitting method is computationally more efficient, making it suitable for large-scale time-series image reconstruction. This study provides valuable insights for selecting optimal strategies to generate high-resolution time-series images across diverse application scenarios and lays a foundation for extensions to other vegetation indices or land surface variables.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Managing Large Datasets for Atmospheric Research

Since the mid-1980s, airborne and ground measurements have been widely used to provide comprehensive characterization of atmospheric composition and processes. Field campaigns have generated a wealth of insitu data and have grown considerably over the years in terms of both the number of measured parameters and the data volume. This can largely be attributed to the rapid advances in instrument development and computing power. The users of field data may face a number of challenges spanning data access, understanding, and proper use in scientific analysis. This tutorial is designed to provide an introduction to using data sets, with a focus on airborne measurements, for atmospheric research. The first part of the tutorial provides an overview of airborne measurements and data discovery. This will be followed by a discussion on the understanding of airborne data files. An actual data file will be used to illustrate how data are reported, including the use of data flags to indicate missing data and limits of detection. Retrieving information from the file header will be discussed, which is essential to properly interpreting the data. Field measurements are typically reported as a function of sampling time, but different instruments often have different sampling intervals. To create a combined data set, the data merge process (interpolation of all data to a common time base) will be discussed in terms of the algorithm, data merge products available from airborne studies, and their application in research. Statistical treatment of missing data and data flagged for limit of detection will also be covered in this section. These basic data processing techniques are applicable to both airborne and ground-based observational data sets. Finally, the recently developed Toolsets for Airborne Data (TAD) will be introduced. TAD (tad.larc.nasa.gov) is an airborne data portal offering tools to create user defined merged data products with the capability to provide descriptive statistics and the option to treat measurement uncertainty.

Chen, Gao↗
DOE OSTI2023

Accelerating full-waveform inversion using source stacking: synthetic experiments at the global scale in a realistic 3-D earth model

SUMMARY The spectral element method is currently the method of choice for computing accurate synthetic seismic wavefields in realistic 3-D earth models at the global scale. However, it requires significantly more computational time, compared to normal mode-based approximate methods. Source stacking, whereby multiple earthquake sources are aligned on their origin time and simultaneously triggered, can reduce the computational costs by several orders of magnitude. We present the results of synthetic tests performed on a realistic radially anisotropic 3-D model, slightly modified from model SEMUCB-WM1 with three component synthetic waveform ‘data’ for a duration of 10 000 s, and filtered at periods longer than 60 s, for a set of 273 events and 515 stations. We consider two definitions of the misfit function, one based on the stacked records at individual stations and another based on station-pair cross-correlations of the stacked records. The inverse step is performed using a Gauss–Newton approach where the gradient and Hessian are computed using normal mode perturbation theory. We investigate the retrieval of radially anisotropic long wavelength structure in the upper mantle in the depth range 100–800 km, after fixing the crust and uppermost mantle structure constrained by fundamental mode Love and Rayleigh wave dispersion data. The results show good performance using both definitions of the misfit function, even in the presence of realistic noise, with degraded amplitudes of lateral variations in the anisotropic parameter ξ. Interestingly, we show that we can retrieve the long wavelength structure in the upper mantle, when considering one or the other of three portions of the cross-correlation time series, corresponding to where we expect the energy from surface wave overtone, fundamental mode or a mixture of the two to be dominant, respectively. We also considered the issue of missing data, by randomly removing a successively larger proportion of the available synthetic data. We replace the missing data by synthetics computed in the current 3-D model using normal mode perturbation theory. The inversion results degrade with the proportion of missing data, especially for ξ, and we find that a data availability of 45 per cent or more leads to acceptable results. We also present a strategy for grouping events and stations to minimize the number of missing data in each group. This leads to an increased number of computations but can be significantly more efficient than conventional single-event-at-a-time inversion. We apply the grouping strategy to a real picking scenario, and show promising resolution capability despite the use of fewer waveforms and uneven ray path distribution. Source stacking approach can be used to rapidly obtain a starting 3-D model for more conventional full-waveform inversion at higher resolution, and to investigate assumptions made in the inversion, such as trade-offs between isotropic, anisotropic or anelastic structure, different model parametrizations or how crustal structure is accounted for.

Geochemistry & Geophysics↗
DOE OSTI2025

Explainable multi-fidelity Bayesian neural network for distribution system state estimation

Distribution System State Estimation (DSSE) is frequently constrained by limited real-time measurements, the uncertainties introduced by distributed energy resources, and the presence of bad data. To address them, this paper proposes an enhanced Multi-Fidelity Bayesian Neural Network (MFBNN) DSSE approach. A low-fidelity layer based on a Deep Neural Network (DNN) is first pre-trained on pseudo-measurement data to learn fundamental state features. Subsequently, a high-fidelity Bayesian Neural Network (BNN) layer leverages limited but high-quality real-time measurements to refine these features, thereby achieving accurate DSSE. Additionally, the deep SHapley Additive exPlanation (SHAP) is developed to quantify the influence of measurement data on DSSE through dual perspectives of global feature importance and local nodal contributions, establishing a hierarchical explainability framework for machine learning-based DSSE. Comparative studies conducted on the IEEE 13-bus system and a real-world 2135-node system from Dominion Energy demonstrate that the proposed method excels in estimation accuracy, even under situations of high noise levels, bad data, and missing data. Further comparisons with Weighted Least Squares (WLS) and other machine learning-based DSSE approaches verify that the proposed framework offers higher accuracy, improved interpretability, and enhanced robustness.

Bad data↗
DOE OSTI2022

Robust Event Classification Using Imperfect Real-world PMU Data

Here, this paper studies robust event classification using imperfect real-world phasor measurement unit (PMU) data. By analyzing the real-world PMU data, we find it is challenging to directly use this dataset for event classifiers due to the low data quality observed in PMU measurements and event logs. To address these challenges, we develop a novel machine learning framework for training robust event classifiers, which consists of three main steps: data preprocessing, fine-grained event data extraction, and feature engineering. Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e.g., bad data and missing data); in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs; and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers. Based on the proposed framework, we develop a workflow for event classification using the real-world PMU data streaming into the system in real time. Using the proposed framework, robust event classifiers can be efficiently trained based on many off-the-shelf lightweight machine learning models. Numerical experiments using the real-world dataset from the Western Interconnection of the U.S power transmission grid show that the event classifiers trained under the proposed framework can achieve high classification accuracy while being robust against low-quality data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2016

MO-FG-CAMPUS-IeP2-05: Feasibility Demonstration of High-Voltage Clinical CT and Impact On X-Ray Penetration Through Metal Objects

Purpose: To demonstrate the possibility and quantify the impact of operating a clinical CT scanner at exceptionally high x-ray tube voltage for better penetration through metal objects and facilitating metal artifact reduction. Methods: We categorize metal objects according to the data corruption severeness (level of distortion and complete photon starvation fraction). To demonstrate feasibility and investigate the impact of high voltage scanning we modified a commercial GE LightSpeed VCT scanner (generator and software) to enable CT scans with x-ray tube voltages as high as 175 kVp. A 20 cm diameter water phantom with two metal rods (10 mm stainless and 25 mm titanium) and a water phantom with realistic metal object (spine cage) were used to evaluate the data corruption and image artifacts in the absence of any algorithm correction. We also performed simulations to confirm our understanding of the transmitted photon levels through metal objects with different size and composition. Results: The reconstructed images at 175 kVp still have significant dark shading artifacts, as expected since no special scatter correction or beam hardening was performed but show substantially lower noise and photon starvation than at lower kVp due to better beam penetration. Analysis of the raw data shows that the photon starved data is reduced from over 4% at 140 kVp to below 0.2% at 175 kVp. The simulations indicate that for clinically relevant titanium and stainless objects a 175 kVp tube voltage effectively avoids photon starvation. Conclusion: The use of exceptionally high tube voltage on a clinical CT system is a practical and effective solution to avoid photon starvation caused by certain metal implants. Sparse and hybrid high-voltage protocols are being considered to maintain low patient dose. This opens the door to algorithmic physics-based corrections rather than treating the data as missing and relying on missing data algorithms. Some of the authors are employees of General Electric.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-08: CT Image Ring Artifact Reduction Based On Prior Image

Purpose: In computed tomography (CT) system, CT images with ring artifacts will be reconstructed when some adjacent bins of detector don’t work. The ring artifacts severely degrade CT image quality. We present a useful CT ring artifacts reduction based on projection data correction, aiming at estimating the missing data of projection data accurately, thus removing the ring artifacts of CT images. Methods: The method consists of ten steps: 1) Identification of abnormal pixel line in projection sinogram; 2) Linear interpolation within the pixel line of projection sinogram; 3) FBP reconstruction using interpolated projection data; 4) Filtering FBP image using mean filter; 5) Forwarding projection of filtered FBP image; 6) Subtraction forwarded projection from original projection; 7) Linear interpolation of abnormal pixel line area in the subtraction projection; 8) Adding the interpolated subtraction projection on the forwarded projection; 9) FBP reconstruction using corrected projection data; 10) Return to step 4 until the pre-set iteration number is reached. The method is validated on simulated and real data to restore missing projection data and reconstruct ring artifact-free CT images. Results: We have studied impact of amount of dead bins of CT detector on the accuracy of missing data estimation in projection sinogram. For the simulated case with a resolution of 256 by 256 Shepp-Logan phantom, three iterations are sufficient to restore projection data and reconstruct ring artifact-free images when the dead bins rating is under 30%. The dead-bin-induced artifacts are substantially reduced. More iteration number is needed to reconstruct satisfactory images while the rating of dead bins increases. Similar results were found for a real head phantom case. Conclusion: A practical CT image ring artifact correction scheme based on projection data is developed. This method can produce ring artifact-free CT images feasibly and effectively.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2013

MISSE in the Materials and Processes Technical Information System (MAPTIS )

Materials International Space Station Experiment (MISSE) data is now being collected and distributed through the Materials and Processes Technical Information System (MAPTIS) at Marshall Space Flight Center in Huntsville, Alabama. MISSE data has been instrumental in many programs and continues to be an important source of data for the space community. To facilitate great access to the MISSE data the International Space Station (ISS) program office and MAPTIS are working to gather this data into a central location. The MISSE database contains information about materials, samples, and flights along with pictures, pdfs, excel files, word documents, and other files types. Major capabilities of the system are: access control, browsing, searching, reports, and record comparison. The search capabilities will search within any searchable files so even if the desired meta-data has not been associated data can still be retrieved. Other functionality will continue to be added to the MISSE database as the Athena Platform is expanded

Burns, DeWitt↗
DOE OSTI2026

BSEC flux towers: CSAT3B and TRH

The data were collected as part of the BSEC project, during the period from June 2025 to May 2026. Directory "broadway" contains data collected on a multi-level flux tower (US-BWf) in the Broadway East neighborhood (1808 North Patterson Park Ave., Baltimore City, MD 21213; LAT: 39o18'40.31'' N; LONG: 76o35'12.43'' W). At each of the four measurement heights (8.5 m, 11.1 m, 13.4 m, 15.9 m), a Campbell Scientific CSAT3B sonic anemometer was operated at 50 Hz to measure virtual temperature (tc) and three velocity components (u: 270 degrees; v: 180 degrees; w: vertical), and a RM Young temperature sensor (model 41382VC) was operated at 1 Hz inside a compact aspirated radiation shield (model 43502) to measure absolute temperature (T) and relative humidity (RH). Inside directory "broadway", directory "netcdf" contains data collected each day in 5-minute chunks that have been converted to NetCDF format (before quality checking), while "4hr" contains data arranged into 4-hour chunks (also in NetCDF format) that have been through basic quality checking steps (treating data points with nonzero diagnostic codes as missing data; fixing six or fewer consecutive missing data points using linear interpolation). Users are recommended to start with data in directory "4hr", while data in directory "netcdf" can be used for reference purposes.

Baltimore↗
DOE OSTI2017

Practical Weak-lensing Shear Measurement with Metacalibration

Metacalibration is a recently introduced method to accurately measure weak gravitational lensing shear using only the available imaging data, without need for prior information about galaxy properties or calibration from simulations. The method involves distorting the image with a small known shear, and calculating the response of a shear estimator to that applied shear. The method was shown to be accurate in moderate-sized simulations with galaxy images that had relatively high signal-to-noise ratios, and without significant selection effects. In this work we introduce a formalism to correct for both shear response and selection biases. We also observe that for images with relatively low signal-to-noise ratios, the correlated noise that arises during the metacalibration process results in significant bias, for which we develop a simple empirical correction. To test this formalism, we created large image simulations based on both parametric models and real galaxy images, including tests with realistic point-spread functions. We varied the point-spread function ellipticity at the five-percent level. In each simulation we applied a small few-percent shear to the galaxy images. We introduced additional challenges that arise in real data, such as detection thresholds, stellar contamination, and missing data. We applied cuts on the measured galaxy properties to induce significant selection effects. Using our formalism, we recovered the input shear with an accuracy better than a part in a thousand in all cases.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

LANL Meteorological Program: 2020 Data Completeness/Quality Report

Los Alamos National Laboratory (LANL) operates four mesa-top meteorology towers: Technical Area (TA) 06, TA-49, TA-53, and TA-54. An additional tower is located in Mortandad Canyon (TA-5 MDCN), and a rain gauge at North Community (NCOM). A description of the meteorology monitoring network is found in Dewart and Boggs (2014). Mesa-top towers are instrumented at 1.2 meters (m), 11.5 m, 23 m, and 46 m. In addition, TA-06 is instrumented at 92 m. The TA-5 MDCN tower is 10 m in height and is instrumented at 1.2 m and 10 m. Data are collected every 15 minutes. Range checking is done on each measurement every 15 minutes; data that are beyond normal ranges are eliminated from the data set and replaced by a code for missing data. In addition, data are reviewed weekly by meteorologists to identify bad data not identified by range checking. The data steward eliminates these data from the data set and replaces them with a code for missing data. The instrument technicians also review that data and schedule instrument replacement as required. Data completeness is determined by the number of total 15-minute records available versus the total number of possible measurements for the entire year. As a rule, the meteorologists do not attempt to estimate data that are eliminated as bad data. Original datalogger records, containing bad data, can be recalled from program archival storage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

LANL Meteorological Program: 2022 Data Completeness/Quality Report

Los Alamos National Laboratory (LANL) operates seven mesa-top instrumented meteorology towers: Technical Area (TA) 6, TA-49, TA-53, TA-54, TA-63, TA-54B, and TA-16B. An additional instrumented tower is located in Mortandad Canyon (TA-5 MDCN), and there is a rain gauge at North Community (NCOM), located within the town of Los Alamos. The 10 meter (m) towers at TA-63, TA- 54B, and TA-16B were installed in 2021, and will be included in the 2023 data completeness/quality report. A description of the meteorology monitoring network, prior to the installation of the TA-63, TA-54B, and TA-16B is found in Dewart and Boggs (2014). Four of the mesa-top towers (e.g., TA-6, TA-49, TA-53, and TA-54) are instrumented at the 1.2 m, 11.5 m, 23 m, and 46 m levels. In addition, the TA-6 tower is instrumented at the 92 m level. The TA-5 MDCN tower is 10 m in height and is instrumented at 1.2 m and 10 m. Data are collected and averaged every 15 minutes. Range checking is performed on each measurement every 15 minutes; data that are beyond normal ranges are eliminated from the data set and replaced by a code for missing data. In addition, data are reviewed weekly by qualified meteorologists to identify bad data not identified by the range checking technique. The data steward eliminates these data from the data set and replaces them with a code for missing data. The instrument technicians also review that data and schedule instrument replacement, as required. All instruments are calibrated at a frequency that meets the criteria identified in ANSI/ANS-3.11-2015. Data completeness is determined by the number of total 15-minute records available versus the number of possible measurements for the entire year. As a rule, the meteorologists do not attempt to estimate data that are eliminated as bad data. Original datalogger records, including bad data, can be recalled from program archival storage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

LANL Meteorological Program: 2023 Data Completeness/Quality Report

Los Alamos National Laboratory (LANL) operates seven mesa-top instrumented meteorology towers: Technical Area (TA) 6, TA-49, TA-53, TA-54, TA-63, TA-54B, and TA-16B. An additional instrumented tower is located in Mortandad Canyon (TA-5 MDCN), and there is a rain gauge at North Community (NCOM), located within the town of Los Alamos. The 10 meter (m) towers at TA-63, TA-54B, and TA-16B have been in testing since they were installed in 2021, and will be included in a future data completeness report. A description of the meteorology monitoring network, prior to the installation of the TA-63, TA-54B, and TA-16B is found in Dewart and Boggs (2014). Four of the mesa-top towers (e.g., TA-6, TA-49, TA-53, and TA-54) are instrumented at the 1.2 m, 11.5 m, 23 m, and 46 m levels. In addition, the TA-6 tower is instrumented at the 92 m level. The TA-5 MDCN tower is 10 m in height and is instrumented at 1.2 m and 10 m. Data are collected and averaged every 15 minutes. Range checking is performed on each measurement every 15 minutes; data that are beyond normal ranges are eliminated from the data set and replaced by a code for missing data. In addition, data are reviewed weekly by qualified meteorologists to identify bad data not identified by the range checking technique. The data steward eliminates these data from the data set and replaces them with a code for missing data. The instrument technicians also review that data and schedule instrument replacement, as required. All instruments are calibrated at a frequency that meets the criteria identified in ANSI/ANS-3.11-2015. Data completeness is determined by the number of total 15-minute records available versus the number of possible measurements for the entire year. As a rule, the meteorologists do not attempt to estimate data that are eliminated as bad data. Original datalogger records, including bad data, can be recalled from program archival storage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning

Data I/O poses a significant bottleneck in large-scale CFD simulations; thus, practitioners would like to significantly reduce the number of times the solution is saved to disk, yet retain the ability to recover any field quantity (at any time instance) a posteriori . The objective of this work is therefore to accurately recover missing CFD data a posteriori at any time instance, given that the solution has been written to disk at only a relatively small number of time instances. We consider in particular high-order discretizations (e.g., discontinuous Galerkin), as such techniques are becoming increasingly popular for the simulation of highly separated flows. To satisfy this objective, this work proposes a methodology consisting of two stages: 1) dimensionality reduction and 2) dynamics learning. For dimensionality reduction, we propose a novel hierarchical approach. First, the method reduces the number of degrees of freedom within each element of the high-order discretization by applying autoencoders from deep learning. Second, the methodology applies principal component analysis to compress the global vector of encodings. This leads to a low-dimensional state, which associates with a nonlinear embedding of the original CFD data. For dynamics learning, we propose to apply regression techniques (e.g., kernel methods) to learn the discrete-time velocity characterizing the time evolution of this low-dimensional state. In conclusion, a numerical example on a large-scale CFD example characterized by nearly 13 million degrees of freedom illustrates the suitability of the proposed method in an industrial setting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

WE-G-18A-03: Cone Artifacts Correction in Iterative Cone Beam CT Reconstruction

Purpose: For iterative reconstruction (IR) in cone-beam CT (CBCT) imaging, data truncation along the superior-inferior (SI) direction causes severe cone artifacts in the reconstructed CBCT volume images. Not only does it reduce the effective SI coverage of the reconstructed volume, it also hinders the IR algorithm convergence. This is particular a problem for regularization based IR, where smoothing type regularization operations tend to propagate the artifacts to a large area. It is our purpose to develop a practical cone artifacts correction solution. Methods: We found it is the missing data residing in the truncated cone area that leads to inconsistency between the calculated forward projections and measured projections. We overcome this problem by using FDK type reconstruction to estimate the missing data and design weighting factors to compensate the inconsistency caused by the missing data. We validate the proposed methods in our multi-GPU low-dose CBCT reconstruction system on multiple patients' datasets. Results: Compared to the FDK reconstruction with full datasets, while IR is able to reconstruct CBCT images using a subset of projection data, the severe cone artifacts degrade overall image quality. For head-neck case under a full-fan mode, 13 out of 80 slices are contaminated. It is even more severe in pelvis case under half-fan mode, where 36 out of 80 slices are affected, leading to inferior soft-tissue delineation. By applying the proposed method, the cone artifacts are effectively corrected, with a mean intensity difference decreased from ∼497 HU to ∼39HU for those contaminated slices. Conclusion: A practical and effective solution for cone artifacts correction is proposed and validated in CBCT IR algorithm. This study is supported in part by NIH (1R01CA154747-01)

60 APPLIED LIFE SCIENCES↗