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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
DOE OSTI2026

HostSub_GP: Precise Galaxy Background Subtraction in Transient Long-slit Spectroscopy with Gaussian Processes

We present a novel host galaxy subtraction technique in long-slit spectroscopy for extragalactic transients. Unlike classic methods which generally estimate the background using simple interpolation of local galaxy flux in the 2D spectrum, our approach leverages multi-band archival images of the host galaxies to model the background emission from the galaxy in the 2D spectrum. Such imaging encodes the wavelength-dependent galaxy profile along the slit, and is readily accessible through wide-field imaging surveys. We construct a smooth prior for the 2D galaxy profile with a Gaussian process (GP) based on these reference images, and use another GP to model the correlated deviations from the prior in the observed spectrum. This enables accurate inference of the galaxy flux blended with the transient. On synthetic long-slit data of a spiral galaxy extracted from a Multi Unit Spectroscopic Explorer hyper-spectral cube, the GP method remains robust as long as the host galaxy is spatially resolved and consistently outperforms classic methods. We apply the method to archival Keck spectra of two real transients, SN 2019eix and AT 2019qiz, to further demonstrate how the method uniquely recovers weak spectral features amid strong galaxy contamination, enabling refined constraints on the properties of both transients. We have released the software implementation, HostSub_GP, a scalable toolkit that leverages JAX, with an MIT license.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

MO-F-CAMPUS-J-02: Automatic Recognition of Patient Treatment Site in Portal Images Using Machine Learning

Purpose: To investigate the method to automatically recognize the treatment site in the X-Ray portal images. It could be useful to detect potential treatment errors, and to provide guidance to sequential tasks, e.g. automatically verify the patient daily setup. Methods: The portal images were exported from MOSAIQ as DICOM files, and were 1) processed with a threshold based intensity transformation algorithm to enhance contrast, and 2) where then down-sampled (from 1024×768 to 128×96) by using bi-cubic interpolation algorithm. An appearance-based vector space model (VSM) was used to rearrange the images into vectors. A principal component analysis (PCA) method was used to reduce the vector dimensions. A multi-class support vector machine (SVM), with radial basis function kernel, was used to build the treatment site recognition models. These models were then used to recognize the treatment sites in the portal image. Portal images of 120 patients were included in the study. The images were selected to cover six treatment sites: brain, head and neck, breast, lung, abdomen and pelvis. Each site had images of the twenty patients. Cross-validation experiments were performed to evaluate the performance. Results: MATLAB image processing Toolbox and scikit-learn (a machine learning library in python) were used to implement the proposed method. The average accuracies using the AP and RT images separately were 95% and 94% respectively. The average accuracy using AP and RT images together was 98%. Computation time was ∼0.16 seconds per patient with AP or RT image, ∼0.33 seconds per patient with both of AP and RT images. Conclusion: The proposed method of treatment site recognition is efficient and accurate. It is not sensitive to the differences of image intensity, size and positions of patients in the portal images. It could be useful for the patient safety assurance. The work was partially supported by a research grant from Varian Medical System.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1985

Maximum likelihood classification of synthetic aperture radar imagery

Classification of synthetic aperture radar (SAR) images has important applications in geology, agriculture, and the military. A statistical model for SAR images is reviewed and a maximum likelihood classification algorithm developed for the classification of agricultural fields based on the model. It is first assumed that the target feature information is known a priori. The performance of the algorithm is then evaluated in terms of the probability of incorrect classification. A technique is also presented to extract the needed feature information from a SAR image; then both the feature extraction and the maximum likelihood classification algorithms are tested on a SEASAT-A SAR image.

Frost, V. S.↗
NASA NTRS1982

Hierarchical modeling for image classification

As part of the California Integrated Remote Sensing System's (CIRSS) San Bernardino County Project, the use of data layers from a geographic information system (GIS) as an integral part of the Landsat image classification process was investigated. Through a hierarchical modeling technique, elevation, aspect, land use, vegetation, and growth management data from the project's data base were used to guide class labeling decisions in a 1976 Landsat MSS land cover classification. A similar model, incorporating 1976-1979 Landsat spectral change data in addition to other data base elements, was used in the classification of a 1979 Landsat image. The resultant Landsat products were integrated as additional layers into the data base for use in growth management, fire hazard, and hydrological modeling.

Likens, W.↗
DOE OSTI2016

“Refsdal” meets Popper: Comparing predictions of the re-appearance of the multiply imaged supernova behind MACSJ1149.5+2223

Supernova “Refsdal,” multiply imaged by cluster MACS1149.5+2223, represents a rare opportunity to make a true blind test of model predictions in extragalactic astronomy, on a timescale that is short compared to a human lifetime. In order to take advantage of this event, we produced seven gravitational lens models with five independent methods, based on Hubble Space Telescope (HST) Hubble Frontier Field images, along with extensive spectroscopic follow-up observations by HST, the Very Large and the Keck Telescopes. We compare the model predictions and show that they agree reasonably well with the measured time delays and magnification ratios between the known images, even though these quantities were not used as input. This agreement is encouraging, considering that the models only provide statistical uncertainties, and do not include additional sources of uncertainties such as structure along the line of sight, cosmology, and the mass sheet degeneracy. We then present the model predictions for the other appearances of supernova “Refsdal.” A future image will reach its peak in the first half of 2016, while another image appeared between 1994 and 2004. The past image would have been too faint to be detected in existing archival images. The future image should be approximately one-third as bright as the brightest known image (i.e., H{sub AB}≈25.7 mag at peak and H{sub AB}≈26.7 mag six months before peak), and thus detectable in single-orbit HST images. We will find out soon whether our predictions are correct.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Validation of columnar CsI x-ray detector responses obtained with hybridMANTIS, a CPU-GPU Monte Carlo code for coupled x-ray, electron, and optical transport

Purpose: hybridMANTIS is a Monte Carlo package for modeling indirect x-ray imagers using columnar geometry based on a hybrid concept that maximizes the utilization of available CPU and graphics processing unit processors in a workstation. Methods: The authors compare hybridMANTIS x-ray response simulations to previously published MANTIS and experimental data for four cesium iodide scintillator screens. These screens have a variety of reflective and absorptive surfaces with different thicknesses. The authors analyze hybridMANTIS results in terms of modulation transfer function and calculate the root mean square difference and Swank factors from simulated and experimental results. Results: The comparison suggests that hybridMANTIS better matches the experimental data as compared to MANTIS, especially at high spatial frequencies and for the thicker screens. hybridMANTIS simulations are much faster than MANTIS with speed-ups up to 5260. Conclusions: hybridMANTIS is a useful tool for improved description and optimization of image acquisition stages in medical imaging systems and for modeling the forward problem in iterative reconstruction algorithms.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

SU-D-9A-06: 3D Localization of Neurovascular Bundles Through MR-TRUS Registration in Prostate Radiotherapy

Purpose: Erectile dysfunction (ED) is the most common complication of prostate-cancer radiotherapy (RT) and the major mechanism is radiation-induced neurovascular bundle (NVB) damage. However, the localization of the NVB remains challenging. This study's purpose is to accurately localize 3D NVB by integrating MR and transrectal ultrasound (TRUS) images through MR-TRUS fusion. Methods: T1 and T2-weighted MR prostate images were acquired using a Philips 1.5T MR scanner and a pelvic phase-array coil. The 3D TRUS images were captured with a clinical scanner and a 7.5 MHz biplane probe. The TRUS probe was attached to a stepper; the B-mode images were captured from the prostate base to apex at a 1-mm step and the Doppler images were acquired in a 5-mm step. The registration method modeled the prostate tissue as an elastic material, and jointly estimated the boundary condition (surface deformation) and the volumetric deformations under elastic constraint. This technique was validated with a clinical study of 7 patients undergoing RT treatment for prostate cancer. The accuracy of our approach was assessed through the locations of landmarks, as well as previous ultrasound Doppler images of patients. Results: MR-TRUS registration was successfully performed for all patients. The mean displacement of the landmarks between the post-registration MR and TRUS images was 1.37±0.42 mm, which demonstrated the precision of the registration based on the biomechanical model; and the NVB volume Dice Overlap Coefficient was 92.1±3.2%, which demonstrated the accuracy of the NVB localization. Conclusion: We have developed a novel approach to improve 3D NVB localization through MR-TRUS fusion for prostate RT, demonstrated its clinical feasibility, and validated its accuracy with ultrasound Doppler data. This technique could be a useful tool as we try to spare the NVB in prostate RT, monitor NBV response to RT, and potentially improve post-RT potency outcomes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

EXPLAINING THE COEXISTENCE OF LARGE-SCALE AND SMALL-SCALE MAGNETIC FIELDS IN FULLY CONVECTIVE STARS

Despite the lack of a shear-rich tachocline region, low-mass fully convective (FC) stars are capable of generating strong magnetic fields, indicating that a dynamo mechanism fundamentally different from the solar dynamo is at work in these objects. We present a self-consistent three-dimensional model of magnetic field generation in low-mass FC stars. The model utilizes the anelastic magnetohydrodynamic equations to simulate compressible convection in a rotating sphere. A distributed dynamo working in the model spontaneously produces a dipole-dominated surface magnetic field of the observed strength. The interaction of this field with the turbulent convection in outer layers shreds it, producing small-scale fields that carry most of the magnetic flux. The Zeeman–Doppler-Imaging technique applied to synthetic spectropolarimetric data based on our model recovers most of the large-scale field. Our model simultaneously reproduces the morphology and magnitude of the large-scale field as well as the magnitude of the small-scale field observed on low-mass FC stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

Megavoltage Image-Based Dynamic Multileaf Collimator Tracking of a NiTi Stent in Porcine Lungs on a Linear Accelerator

Purpose: To investigate the accuracy and potential limitations of MV image-based dynamic multileaf collimator (DMLC) tracking in a porcine model on a linear accelerator. Methods and Materials: A thermo-expandable NiTi stent designed for kilovoltage (kV) X-ray visualization of lung lesions was inserted into the bronchia of three anaesthetized Goettingen minipigs. A four-dimensional computed tomography scan was used for planning a five-field conformal treatment with circular multileaf collimator (MLC) apertures. A 22.5 Gy single fraction treatment was delivered to the pigs. The peak-to-peak stent motion was 3 to 8 mm, with breathing periods of 1.2 to 4 s. Before treatment, X-ray images were used for image-guided setup based on the stent. During treatment delivery, continuous megavoltage (MV) portal images were acquired at 7.5 Hz. The stent was segmented in the images and used for continuous adaptation of the MLC aperture. Offline, the tracking error in beam's eye view of the treatment beam was calculated for each MV image as the difference between the MLC aperture center and the segmented stent position. The standard deviations of the systematic error {Sigma} and the random error {sigma} were determined and compared with the would-be errors for a nontracking treatment with pretreatment image-guided setup. Results: Reliable stent segmentation was obtained for 11 of 15 fields. Segmentation failures occurred when image contrast was dominated by overlapping anatomical structures (ribs, diaphragm) rather than by the stent, which was designed for kV rather than MV X-ray visibility. For the 11 fields with reliable segmentation, {Sigma} was 0.5 mm/0.4 mm in the two imager directions, whereas {sigma} was 0.5 mm/1.1 mm. Without tracking, {Sigma} and {sigma} would have been 1.7 mm/1.4 mm and 0.8 mm/1.4 mm, respectively. Conclusion: For the first time, in vivo DMLC tracking has been demonstrated on a linear accelerator showing the potential for improved targeting accuracy. The study mimicked the envisioned patient workflow of future patient treatments. Clinical implementation of MV image-based tracking would require markers designed for MV visibility.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Adaptive nonlocal means filtering based on local noise level for CT denoising

Purpose: To develop and evaluate an image-domain noise reduction method based on a modified nonlocal means (NLM) algorithm that is adaptive to local noise level of CT images and to implement this method in a time frame consistent with clinical workflow. Methods: A computationally efficient technique for local noise estimation directly from CT images was developed. A forward projection, based on a 2D fan-beam approximation, was used to generate the projection data, with a noise model incorporating the effects of the bowtie filter and automatic exposure control. The noise propagation from projection data to images was analytically derived. The analytical noise map was validated using repeated scans of a phantom. A 3D NLM denoising algorithm was modified to adapt its denoising strength locally based on this noise map. The performance of this adaptive NLM filter was evaluated in phantom studies in terms of in-plane and cross-plane high-contrast spatial resolution, noise power spectrum (NPS), subjective low-contrast spatial resolution using the American College of Radiology (ACR) accreditation phantom, and objective low-contrast spatial resolution using a channelized Hotelling model observer (CHO). Graphical processing units (GPU) implementation of this noise map calculation and the adaptive NLM filtering were developed to meet demands of clinical workflow. Adaptive NLM was piloted on lower dose scans in clinical practice. Results: The local noise level estimation matches the noise distribution determined from multiple repetitive scans of a phantom, demonstrated by small variations in the ratio map between the analytical noise map and the one calculated from repeated scans. The phantom studies demonstrated that the adaptive NLM filter can reduce noise substantially without degrading the high-contrast spatial resolution, as illustrated by modulation transfer function and slice sensitivity profile results. The NPS results show that adaptive NLM denoising preserves the shape and peak frequency of the noise power spectrum better than commercial smoothing kernels, and indicate that the spatial resolution at low contrast levels is not significantly degraded. Both the subjective evaluation using the ACR phantom and the objective evaluation on a low-contrast detection task using a CHO model observer demonstrate an improvement on low-contrast performance. The GPU implementation can process and transfer 300 slice images within 5 min. On patient data, the adaptive NLM algorithm provides more effective denoising of CT data throughout a volume than standard NLM, and may allow significant lowering of radiation dose. After a two week pilot study of lower dose CT urography and CT enterography exams, both GI and GU radiology groups elected to proceed with permanent implementation of adaptive NLM in their GI and GU CT practices. Conclusions: This work describes and validates a computationally efficient technique for noise map estimation directly from CT images, and an adaptive NLM filtering based on this noise map, on phantom and patient data. Both the noise map calculation and the adaptive NLM filtering can be performed in times that allow integration with clinical workflow. The adaptive NLM algorithm provides effective denoising of CT data throughout a volume, and may allow significant lowering of radiation dose.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska

Widespread changes in vegetation cover and composition are driving strong impacts on Arctic ecosystem functioning and global climate feedbacks. An accurate characterization of tundra vegetation composition is required to understand how the Arctic will respond to future climate change. However, quantifying tundra vegetation composition over large areas is challenging as commonly-used satellite observations are too coarse, spatially and spectrally, to differentiate low-lying tundra vegetation types. Recent airborne and spaceborne imaging spectroscopy platforms provide better data to characterize vegetation composition. Yet, our ability to characterize vegetation composition with imaging spectroscopy remains largely unexplored in the Arctic, particularly due to a lack of ground observations needed to train and test classification models. To address this problem, we collected very-high-resolution (VHR, ~5 cm) unoccupied aerial system (UAS) imagery at three low-Arctic tundra sites located on the Seward Peninsula, western Alaska. In this paper, we examine the feasibility of integrating imagery from the UAS and the hyperspectral Airborne Visible/Infrared Imaging Spectrometer, Next Generation (AVIRIS-NG) airborne instrument to map the fractional composition of 12 key Arctic plant functional types (PFTs). To this end, we first mapped the 12 PFTs from our VHR UAS imagery using random forest classification. We then used these UAS-derived PFT maps as ground truth to develop partial least squares regression (PLSR) models to predict the fractional cover (FCover) of each PFT from AVIRIS-NG imagery. Further, we evaluated the performance of our PLSR models using reserved UAS samples, as well as by mapping PFT FCover and dominant PFT for large tundra landscapes. Our results show that 1) Arctic PFTs can be effectively mapped using VHR UAS imagery, with overall accuracy between 86% and 92%, 2) when the UAS mapped PFTs were used to inform PLSR scaling models, the FCover of the 12 PFTs could be effectively estimated from AVIRIS-NG imagery with a mean absolute error (MAE) <0.13, and 3) our PLSR models outperformed traditional, fully constrained least-squares (FCLS) linear mixture analysis and produced high-quality, spatially contiguous PFT FCover and PFT maps that captured vegetation spatial patterns with similar accuracy to those developed from UAS imagery. The developed PLSR models have the potential to be broadly applied for quantifying vegetation composition with AVIRIS-NG images to help monitor tundra vegetation dynamics and improve process-based modeling of tundra ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Statistical iterative reconstruction to improve image quality for digital breast tomosynthesis

Purpose: Digital breast tomosynthesis (DBT) is a novel modality with the potential to improve early detection of breast cancer by providing three-dimensional (3D) imaging with a low radiation dose. 3D image reconstruction presents some challenges: cone-beam and flat-panel geometry, and highly incomplete sampling. A promising means to overcome these challenges is statistical iterative reconstruction (IR), since it provides the flexibility of accurate physics modeling and a general description of system geometry. The authors’ goal was to develop techniques for applying statistical IR to tomosynthesis imaging data. Methods: These techniques include the following: a physics model with a local voxel-pair based prior with flexible parameters to fine-tune image quality; a precomputed parameter λ in the prior, to remove data dependence and to achieve a uniform resolution property; an effective ray-driven technique to compute the forward and backprojection; and an oversampled, ray-driven method to perform high resolution reconstruction with a practical region-of-interest technique. To assess the performance of these techniques, the authors acquired phantom data on the stationary DBT prototype system. To solve the estimation problem, the authors proposed an optimization-transfer based algorithm framework that potentially allows fewer iterations to achieve an acceptably converged reconstruction. Results: IR improved the detectability of low-contrast and small microcalcifications, reduced cross-plane artifacts, improved spatial resolution, and lowered noise in reconstructed images. Conclusions: Although the computational load remains a significant challenge for practical development, the superior image quality provided by statistical IR, combined with advancing computational techniques, may bring benefits to screening, diagnostics, and intraoperative imaging in clinical applications.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations

We propose InSituNet, a deep learning based surrogate model to support parameter space exploration for ensemble simulations that are visualized in situ. In situ visualization, generating visualizations at simulation time, is becoming prevalent in handling large-scale simulations because of the I/O and storage constraints. However, in situ visualization approaches limit the flexibility of post-hoc exploration because the raw simulation data are no longer available. Although multiple image-based approaches have been proposed to mitigate this limitation, those approaches lack the ability to explore the simulation parameters. Our approach allows flexible exploration of parameter space for large-scale ensemble simulations by taking advantage of the recent advances in deep learning. Specifically, we design InSituNet as a convolutional regression model to learn the mapping from the simulation and visualization parameters to the visualization results. With the trained model, users can generate new images for different simulation parameters under various visualization settings, which enables in-depth analysis of the underlying ensemble simulations. We demonstrate the effectiveness of InSituNet in combustion, cosmology, and ocean simulations through quantitative and qualitative evaluations.

deep learning↗
DOE OSTI2014

A new background distribution-based active contour model for three-dimensional lesion segmentation in breast DCE-MRI

Purpose: To develop and evaluate a computerized semiautomatic segmentation method for accurate extraction of three-dimensional lesions from dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) of the breast. Methods: The authors propose a new background distribution-based active contour model using level set (BDACMLS) to segment lesions in breast DCE-MRIs. The method starts with manual selection of a region of interest (ROI) that contains the entire lesion in a single slice where the lesion is enhanced. Then the lesion volume from the volume data of interest, which is captured automatically, is separated. The core idea of BDACMLS is a new signed pressure function which is based solely on the intensity distribution combined with pathophysiological basis. To compare the algorithm results, two experienced radiologists delineated all lesions jointly to obtain the ground truth. In addition, results generated by other different methods based on level set (LS) are also compared with the authors’ method. Finally, the performance of the proposed method is evaluated by several region-based metrics such as the overlap ratio. Results: Forty-two studies with 46 lesions that contain 29 benign and 17 malignant lesions are evaluated. The dataset includes various typical pathologies of the breast such as invasive ductal carcinoma, ductal carcinomain situ, scar carcinoma, phyllodes tumor, breast cysts, fibroadenoma, etc. The overlap ratio for BDACMLS with respect to manual segmentation is 79.55% ± 12.60% (mean ± s.d.). Conclusions: A new active contour model method has been developed and shown to successfully segment breast DCE-MRI three-dimensional lesions. The results from this model correspond more closely to manual segmentation, solve the weak-edge-passed problem, and improve the robustness in segmenting different lesions.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

Development and Application of NASA SPoRT’s DustTracker-AI Model for Real-Time Identification and Tracking of Dust in Geostationary Satellite Imagery

The NASA Short-term Prediction Research and Transition (SPoRT) Center developed the DustTracker-AI model for identifying and tracking dust in NASA/NOAA Geostationary Operational Environmental Satellite (GOES) imagery in a real-time framework. A training dataset consisting of day and night dust cases was gathered over the southwestern consisting of 115 distinct images and over a million dust pixels and 256 million no dust pixels. The dataset was separated into training (60%), testing (20%), and validation (20%). A simple random forest machine learning model was developed originally to overcome the problem of night-time dust detection and has been expanded to a comprehensive day/night model for dust identification and tracking. This physically-based machine-learning approach uses NASA/NOAA GOES-16 Advanced Baseline Imager infrared imagery as inputs to the model. The model probability of dust output achieves an Area-Under-Curve (AUC) of 0.97 with a standard deviation of 0.04 for dust cases. For images with dust present, the model correctly labels 85% of dust pixels for all dust images in the validation data set. In conjunction with developing the machine-learning model, the NASA Short-term Prediction Research and Transition Center (SPoRT) partnered with NOAA National Weather Service forecast offices to evaluate the model for utility in weather forecasting operations during the 2021 and 2023 late winter-spring seasons. Preliminary evaluation has indicated the majority of forecasters described the DustTracker-AI probabilities as having added confidence to interpreting the Dust RGB and other satellite products to objectively assess the dust extent and trends and increased the amount of time the dust plume could be tracked into the night as compared to use of the Dust RGB. More recently, SPoRT tested small scale events associated with thunderstorm outflow and burn scars to determine the model’s ability to capture local events. This presentation highlights design of the model, validation/evaluation of model performance, and example cases collected during end user product assessments.

Connor H Welch↗
DOE OSTI2014

3D/3D registration of coronary CTA and biplane XA reconstructions for improved image guidance

Purpose: The authors aim to improve image guidance during percutaneous coronary interventions of chronic total occlusions (CTO) by providing information obtained from computed tomography angiography (CTA) to the cardiac interventionist. To this end, the authors investigate a method to register a 3D CTA model to biplane reconstructions. Methods: The authors developed a method for registering preoperative coronary CTA with intraoperative biplane x-ray angiography (XA) images via 3D models of the coronary arteries. The models are extracted from the CTA and biplane XA images, and are temporally aligned based on CTA reconstruction phase and XA ECG signals. Rigid spatial alignment is achieved with a robust probabilistic point set registration approach using Gaussian mixture models (GMMs). This approach is extended by including orientation in the Gaussian mixtures and by weighting bifurcation points. The method is evaluated on retrospectively acquired coronary CTA datasets of 23 CTO patients for which biplane XA images are available. Results: The Gaussian mixture model approach achieved a median registration accuracy of 1.7 mm. The extended GMM approach including orientation was not significantly different (P > 0.1) but did improve robustness with regards to the initialization of the 3D models. Conclusions: The authors demonstrated that the GMM approach can effectively be applied to register CTA to biplane XA images for the purpose of improving image guidance in percutaneous coronary interventions.

60 APPLIED LIFE SCIENCES↗