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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 OSTI2020

Ultra-Fast Electron Microscopic Imaging of Single Molecules With a Direct Electron Detection Camera and Noise Reduction

Time-resolved imaging of molecules and materials made of light elements is an emerging field of transmission electron microscopy (TEM), and the recent development of direct electron detection cameras, capable of taking as many as 1,600 fps, has potentially broadened the scope of the time-resolved TEM imaging in chemistry and nanotechnology. However, such a high frame rate reduces electron dose per frame, lowers the signal-to-noise ratio (SNR), and renders the molecular images practically invisible. Here, we examined image noise reduction to take the best advantage of fast cameras and concluded that the Chambolle total variation denoising algorithm is the method of choice, as illustrated for imaging of a molecule in the 1D hollow space of a carbon nanotube with ~1 ms time resolution. Through the systematic comparison of the performance of multiple denoising algorithms, we found that the Chambolle algorithm improves the SNR by more than an order of magnitude when applied to TEM images taken at a low electron dose as required for imaging at around 1,000 fps. Open-source code and a standalone application to apply Chambolle denoising to TEM images and video frames are available for download.

47 OTHER INSTRUMENTATION↗
DOE OSTI2013

Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: A postmortem study

Purpose: Quantification of breast density based on three-dimensional breast MRI may provide useful information for the early detection of breast cancer. However, the field inhomogeneity can severely challenge the computerized image segmentation process. In this work, the effect of the bias field in breast density quantification has been investigated with a postmortem study. Methods: T1-weighted images of 20 pairs of postmortem breasts were acquired on a 1.5 T breast MRI scanner. Two computer-assisted algorithms were used to quantify the volumetric breast density. First, standard fuzzy c-means (FCM) clustering was used on raw images with the bias field present. Then, the coherent local intensity clustering (CLIC) method estimated and corrected the bias field during the iterative tissue segmentation process. Finally, FCM clustering was performed on the bias-field-corrected images produced by CLIC method. The left–right correlation for breasts in the same pair was studied for both segmentation algorithms to evaluate the precision of the tissue classification. Finally, the breast densities measured with the three methods were compared to the gold standard tissue compositions obtained from chemical analysis. The linear correlation coefficient, Pearson'sr, was used to evaluate the two image segmentation algorithms and the effect of bias field. Results: The CLIC method successfully corrected the intensity inhomogeneity induced by the bias field. In left–right comparisons, the CLIC method significantly improved the slope and the correlation coefficient of the linear fitting for the glandular volume estimation. The left–right breast density correlation was also increased from 0.93 to 0.98. When compared with the percent fibroglandular volume (%FGV) from chemical analysis, results after bias field correction from both the CLIC the FCM algorithms showed improved linear correlation. As a result, the Pearson'sr increased from 0.86 to 0.92 with the bias field correction. Conclusions: The investigated CLIC method significantly increased the precision and accuracy of breast density quantification using breast MRI images by effectively correcting the bias field. It is expected that a fully automated computerized algorithm for breast density quantification may have great potential in clinical MRI applications.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Accelerating Satellite Image Based Large-Scale Settlement Detection with GPU

Computer vision algorithms for image analysis are often computationally demanding. Application of such algorithms on large image databases\---- such as the high-resolution satellite imagery covering the entire land surface, can easily saturate the computational capabilities of conventional CPUs. There is a great demand for vision algorithms running on high performance computing (HPC) architecture capable of processing petascale image data. We exploit the parallel processing capability of GPUs to present a GPU-friendly algorithm for robust and efficient detection of settlements from large-scale high-resolution satellite imagery. Feature descriptor generation is an expensive, but a key step in automated scene analysis. To address this challenge, we present GPU implementations for three different feature descriptors\-- multiscale Historgram of Oriented Gradients (HOG), Gray Level Co-Occurrence Matrix (GLCM) Contrast and local pixel intensity statistics. We perform extensive experimental evaluations of our implementation using diverse and large image datasets. Our GPU implementation of the feature descriptor algorithms results in speedups of 220 times compared to the CPU version. We present an highly efficient settlement detection system running on a multiGPU architecture capable of extracting human settlement regions from a city-scale sub-meter spatial resolution aerial imagery spanning roughly 1200 sq. kilometers in just 56 seconds with detection accuracy close to 90\%. This remarkable speedup gained by our vision algorithm maintaining high detection accuracy clearly demonstrates that such computational advancements clearly hold the solution for petascale image analysis challenges.

Patlolla, Dilip Reddy↗
DOE OSTI2021

Through a glass darkly: In-situ x-ray computed tomography imaging of feed melting in continuously fed laboratory-scale glass melter

his study describes the first direct in-situ 3-D observation of a steady-state melting process by imaging a laboratory-scale slurry-fed glass melter in operation by x-ray computed tomography. Features of the reacting glass-feed, the foam layer underneath, and cavities in the glass melt pool are reconstructed in three-dimensional images. A slurry pool formed in a deep central caldera of dense dried feed, which penetrated into the glass melt. Slurry overflow from the caldera led to fast-dried and highly porous feed structure. A thin layer of foam separated the caldera from the melt. Bubbles ?5-15 mm in diameter were seen to grow beneath the reacting feed and move through the melt to escape at the edge. Pore morphology is benchmarked against computed tomography scans of a pellet of reacting simulated waste glass feed, and evolved gas analysis describes the gases generated as a function of temperature. Cooling artifacts are imaged and compared to previous studies of quenched cold caps. Detailed understanding of processes occurring during the conversion process in and below the reacting feed layer is necessary for the development of representative models of the melting process.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships

We investigate methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on classification accuracy. A classification accuracy of 95.8% was achieved fordistinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions.We find that our newly developed microstructure representation describes image data well, and the traditional approachof utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relativelysmall, imbalanced original data set of 272 images. To explore the applicability of generative methods for supplementing such limited data sets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image data sets is also discussed. Our work has implications for quantitative microstructure analysis, and development of microstructure-processing relationships in limited data sets typical of metallurgical process design studies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

EXSCLAIM!: Harnessing materials science literature for self-labeled microscopy datasets

This work introduces the EXSCLAIM! toolkit for the automatic extraction, separation, and caption-based natural language annotation of images from scientific literature. EXSCLAIM! is used to show how rule-based natural language processing and image recognition can be leveraged to construct an electron microscopy data set containing thousands of keyword-annotated nanostructure images. Moreover, it is demonstrated how a combination of statistical topic modeling and semantic word similarity comparisons can be used to increase the number and variety of keyword annotations on top of the standard annotations from EXSCLAIM! With large-scale imaging datasets constructed from scientific literature, users are well positioned to train neural networks for classification and recognition tasks specific to microscopy-tasks often otherwise inhibited by a lack of sufficient annotated training data.

36 MATERIALS SCIENCE↗
DOE OSTI2015

SU-E-I-51: Quantitative Assessment of X-Ray Imaging Detector Performance in a Clinical Setting - a Simple Approach Using a Commercial Instrument

Purpose: To measure and compare the performance of X-ray imaging detectors in a clinical setting using a dedicated instrument for the quantitative determination of detector performance. Methods: The DQEPro (DQE Instruments Inc., London, Ontario Canada) was used to determine the MTF, NPS and DQE using an IEC compliant methodology for three different imaging modalities: conventional radiography (CsI-based detector), general-purpose radioscopy (CsI-based detector), and mammography (a-Se based detector). The radiation qualities (IEC) RQA-5 and RQA-M-2 were used for the CsI-based and a-Se-based detectors, respectively. The DQEPro alleviates some of the difficulties associated with DQE measurements by automatically positioning test devices over the detector, guiding the user through the image acquisition process and providing software for calculations. Results: A comparison of the NPS showed that the image noise of the a-Se detector was less correlated than the CsI detectors. A consistently higher performance was observed for the a-Se detector at all spatial frequencies (MTF: 0.97@0.25 cy/mm, DQE: 0.72@0.25 cy/mm) and the DQE drops off slower than for the CsI detectors. The CsI detector used for conventional radiography displayed a higher performance at low spatial frequencies compared to the CsI detector used for radioscopy (DQE: 0.65 vs 0.60@0.25 cy/mm). However, at spatial frequencies above 1.3 cy/mm, the radioscopy detector displayed better performance than the conventional radiography detector (DQE: 0.35 vs 0.24@2.00 cy/mm). Conclusion: The difference in the MTF, NPS and DQE that was observed for the two different CsI detectors and the a-Se detector reflect the imaging tasks that the different detector types are intended for. The DQEPro has made the determination and calculation of quantitative metrics of X-ray imaging detector performance substantially more convenient and accessible to undertake in a clinical setting.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Radiation image reconstruction and uncertainty quantification using a Gaussian process prior

We propose a complete framework for Bayesian image reconstruction and uncertainty quantification based on a Gaussian process prior (GPP) to overcome limitations of maximum likelihood expectation maximization (ML-EM) image reconstruction algorithm. The prior distribution is constructed with a zero-mean Gaussian process (GP) with a choice of a covariance function, and a link function is used to map the Gaussian process to an image. Unlike many other maximum a posteriori approaches, our method offers highly interpretable hyperparamters that are selected automatically with the empirical Bayes method. Furthermore, the GP covariance function can be modified to incorporate a priori structural priors, enabling multi-modality imaging or contextual data fusion. Lastly, we illustrate that our approach lends itself to Bayesian uncertainty quantification techniques, such as the preconditioned Crank–Nicolson method and the Laplace approximation. The proposed framework is general and can be employed in most radiation image reconstruction problems, and we demonstrate it with simulated free-moving single detector radiation source imaging scenarios. We compare the reconstruction results from GPP and ML-EM, and show that the proposed method can significantly improve the image quality over ML-EM, all the while providing greater understanding of the source distribution via the uncertainty quantification capability. Furthermore, significant improvement of the image quality by incorporating a structural prior is illustrated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

WE-G-16A-01: Evolution of Radiation Treatment Planning

Welcome and Introduction - Lawrence N. Rothenberg This symposium is one a continuing series of presentations at AAPM Annual Meetings on the historical aspects of medical physics, radiology, and radiation oncology that have been organized by the AAPM History Committee. Information on previous presentations including “Early Developments in Teletherapy” (Indianapolis 2013), “Historical Aspects of Cross-Sectional Imaging” (Charlotte 2012), “Historical Aspects of Brachytherapy” (Vancouver 2011), “50 Years of Women in Medical Physics” (Houston 2008), and “Roentgen's Early Investigations” (Minneapolis 2007) can be found in the Education Section of the AAPM Website. The Austin 2014 History Symposium will be on “Evolution of Radiation Treatment Planning.” Overview - Radhe Mohan Treatment planning is one of the most critical components in the chain of radiation therapy of cancers. Treatment plans of today contain a wide variety of sophisticated information conveying the potential clinical effectiveness of the designed treatment to practitioners. Examples of such information include dose distributions superimposed on three- or even four-dimensional anatomic images; dose volume histograms, dose, dose-volume and dose-response indices for anatomic structures of interest; etc. These data are used for evaluating treatment plans and for making treatment decisions. The current state-of-the-art has evolved from the 1940s era when the dose to the tumor and normal tissues was estimated approximately by manual means. However, the symposium will cover the history of the field from the late-1950's, when computers were first introduced for treatment planning, to the present state involving the use of high performance computing and advanced multi-dimensional anatomic, functional and biological imaging, focusing only on external beam treatment planning. The symposium will start with a general overview of the treatment planning process including imaging, structure delineation, assignment of dose requirements, consideration of uncertainties, selection of beam configurations and shaping of beams, and calculations, optimization and evaluation of dose distributions. This will be followed by three presentations covering the evolution of treatment planning, which parallels the evolution of computers, availability of advanced volumetric imaging and the development of novel technologies such as dynamic multi-leaf collimators and online image guidance. This evolution will be divided over three distinct periods - prior to 1970's, the 2D era; from 1980 to the mid-1990's, the 3D era; and from the mid 1990's to today, the IMRT era. When the World was Flat: The Two-Dimensional Radiation Therapy Era” - Jacob Van Dyk In the 2D era, anatomy was defined with the aid of solder wires, special contouring devices and projection x-rays. Dose distributions were calculated manually from single field, flat surface isodoses on transparencies. Precalculated atlases of generic dose distributions were produced by the International Atomic Energy Agency. Massive time-shared main frames and mini-computers were used to compute doses at individual points or dose distributions in a single plane. Beam shapes were generally rectangular, with wedges, missing tissue compensators and occasional blocks to shield critical structures. Dose calculations were measurement-based or they used primary and scatter calculations based on scatter-air ratio methodologies. Dose distributions were displayed on line printers as alpha-numeric character maps or isodose patterns made with pen plotters. More than Pretty Pictures: 3D Treatment Planning and Conformal Therapy - Benedick A. Fraass The introduction of computed tomography allowed the delineation of anatomy three-dimensionally and, supported partly by contracts from the National Cancer Institute, made possible the introduction and clinical use of 3D treatment planning, leading to development and use of 3D conformal therapy in the 1980's. 3D computer graphics and 3D anatomical structure definitions made possible Beam's Eye View (BEV) displays, making conformal beam shaping and much more sophisticated beam arrangements possible. These conformal plans significantly improved target dose coverage as well as normal tissue sparing. The use of dose volume histograms, gross/clinical/planning target volumes, MRI and PET imaging, multileaf collimators, and computer-controlled treatment delivery made sophisticated planning approaches practical. The significant improvements in dose distributions and analysis achievable with 3D conformal therapy made possible formal dose escalation and normal tissue tolerance clinical studies that set new and improved expectations for improved local control and decreasing complications in many clinical sites. From the Art to the State of the Art: Inverse Planning and IMRT - Thomas R. Bortfeld While the potential of intensity modulation was recognized in the mid- 1980's, intensity-modulated radiotherapy (IMRT) did not become a reality until the mid-1990's. Broad beams of photons could be sub-divided into narrow beamlets whose intensities could be determined using sophisticated optimization algorithms to appropriately balance tumor dose with normal tissue sparing. The development of dynamic multi-leaf collimators (on conventional linear accelerators as well as in helical delivery devices) enabled the efficient delivery of IMRT. The evolution of IMRT planning is continuing in the form of Volumetric Modulated Arc Therapy (VMAT) and through advanced optimization tools, such as multi-criteria optimization, automated IMRT planning, and robust optimization to protect dose distributions against uncertainties. IMRT also facilitates “dose painting” in which different sub-volumes of the target are prescribed different doses. Clearly, these advancements are being made possible by the increasing power and lower cost of computers and developments in other fields such as imaging and operations research. Summary - Radhe Mohan The history does not end here. The advancement of treatment planning is expected to continue, leading to further automation and improvements in conformality and robustness of dose distributions, particularly in the area of particle therapy. Radiobiological modeling will gain emphasis as part of the planning process. Learning Objectives: The scope of changes in technology and the capabilities of radiation treatment planning The impact of these changes in the quality of treatment plans and optimality of dose distributions The impact of development in other fields (imaging, computers, operations research, etc.) on the evolution of radiation treatment planning.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-F-18C-15: Model-Based Multiscale Noise Reduction On Low Dose Cone Beam Projection

Purpose: To improve image quality of low dose cone beam CT for patient positioning in radiation therapy. Methods: In low dose cone beam CT (CBCT) imaging systems, Poisson process governs the randomness of photon fluence at x-ray source and the detector because of the independent binomial process of photon absorption in medium. On a CBCT projection, the variance of fluence consists of the variance of noiseless imaging structure and that of Poisson noise, which is proportional to the mean (noiseless) of the fluence at the detector. This requires multiscale filters to smoothen noise while keeping the structure information of the imaged object. We used a mathematical model of Poisson process to design multiscale filters and established the balance of noise correction and structure blurring. The algorithm was checked with low dose kilo-voltage CBCT projections acquired from a Varian OBI system. Results: From the investigation of low dose CBCT of a Catphan phantom and patients, it showed that our model-based multiscale technique could efficiently reduce noise and meanwhile keep the fine structure of the imaged object. After the image processing, the number of visible line pairs in Catphan phantom scanned with 4 ms pulse time was similar to that scanned with 32 ms, and soft tissue structure from simulated 4 ms patient head-and-neck images was also comparable with scanned 20 ms ones. Compared with fixed-scale technique, the image quality from multiscale one was improved. Conclusion: Use of projection-specific multiscale filters can reach better balance on noise reduction and structure information loss. The image quality of low dose CBCT can be improved by using multiscale filters.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TakeTwo : an indexing algorithm suited to still images with known crystal parameters

The indexing methods currently used for serial femtosecond crystallography were originally developed for experiments in which crystals are rotated in the X-ray beam, providing significant three-dimensional information. On the other hand, shots from both X-ray free-electron lasers and serial synchrotron crystallography experiments are still images, in which the few three-dimensional data available arise only from the curvature of the Ewald sphere. Traditional synchrotron crystallography methods are thus less well suited to still image data processing. Here, a new indexing method is presented with the aim of maximizing information use from a still image given the known unit-cell dimensions and space group. Efficacy for cubic, hexagonal and orthorhombic space groups is shown, and for those showing some evidence of diffraction the indexing rate ranged from 90% (hexagonal space group) to 151% (cubic space group). Here, the indexing rate refers to the number of lattices indexed per image.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

An approach to contouring the dorsal vagal complex for radiotherapy planning

Multiple studies suggest that radiation dose to the area of the brainstem called the “dorsal vagal complex (DVC)” influences the frequency of nausea and vomiting during radiotherapy. The purpose of this didactic article is to describe the step-by-step process that we use to contour the general area of the DVC on axial computed tomography (CT) images as would be done for radiotherapy planning. The contouring procedure that we describe for contouring the area of the DVC is useful to medical dosimetrists and radiation oncologists.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2017

Identification of uncommon objects in containers

A system for identifying in an image an object that is commonly found in a collection of images and for identifying a portion of an image that represents an object based on a consensus analysis of segmentations of the image. The system collects images of containers that contain objects for generating a collection of common objects within the containers. To process the images, the system generates a segmentation of each image. The image analysis system may also generate multiple segmentations for each image by introducing variations in the selection of voxels to be merged into a segment. The system then generates clusters of the segments based on similarity among the segments. Each cluster represents a common object found in the containers. Once the clustering is complete, the system may be used to identify common objects in images of new containers based on similarity between segments of images and the clusters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Non-contact rapid reader system for reflective particle tags

A reflective particle tag reader system includes a read head assembly having a camera, illuminators, and a rigid frame portion for supporting the camera and the illuminators. The illuminators illuminate a focal point located opposite the camera where a reflective particle tag is placed. A computer in data communication with the camera receives and store images of the reflective particle tag that are acquired by the camera. The computer is programmed to process video images and to quantify a positional alignment parameter and an angular alignment parameter of the reader with respect to the reflective particle tag. A rapid burst of image frames is obtained in response to the positional alignment and the angular alignment parameters being within a predetermined tolerance and identity of the reflective tag is established between a first image set and a second image set.

42 ENGINEERING↗
DOE OSTI2020

Identification of uncommon objects in containers

A system for identifying in an image an object that is commonly found in a collection of images and for identifying a portion of an image that represents an object based on a consensus analysis of segmentations of the image. The system collects images of containers that contain objects for generating a collection of common objects within the containers. To process the images, the system generates a segmentation of each image. The image analysis system may also generate multiple segmentations for each image by introducing variations in the selection of voxels to be merged into a segment. The system then generates clusters of the segments based on similarity among the segments. Each cluster represents a common object found in the containers. Once the clustering is complete, the system may be used to identify common objects in images of new containers based on similarity between segments of images and the clusters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Statistical analysis of AFM topographic images of self-assembled quantum dots

To obtain statistical data on quantum-dot sizes, AFM topographic images of the substrate on which the dots under study are grown are analyzed. Due to the nonideality of the substrate containing height differences on the order of the size of nanoparticles at distances of 1-10 {mu}m and the insufficient resolution of closely arranged dots due to the finite curvature radius of the AFM probe, automation of the statistical analysis of their large dot array requires special techniques for processing topographic images to eliminate the loss of a particle fraction during conventional processing. As such a technique, convolution of the initial matrix of the AFM image with a specially selected matrix is used. This makes it possible to determine the position of each nanoparticle and, using the initial matrix, to measure their geometrical parameters. The results of statistical analysis by this method of self-assembled InAs quantum dots formed on the surface of an AlGaAs epitaxial layer are presented. It is shown that their concentration, average size, and half-width of height distribution depend strongly on the In flow and total amount of deposited InAs which are varied within insignificant limits.

36 MATERIALS SCIENCE↗
DOE OSTI2012

CT, MR, and ultrasound image artifacts from prostate brachytherapy seed implants: The impact of seed size

Purpose: To investigate the effects of brachytherapy seed size on the quality of x-ray computed tomography (CT), ultrasound (US), and magnetic resonance (MR) images and seed localization through comparison of the 6711 and 9011 {sup 125}I sources. Methods: For CT images, an acrylic phantom mimicking a clinical implantation plan and embedded with low contrast regions of interest (ROIs) was designed for both the 0.774 mm diameter 6711 (standard) and the 0.508 mm diameter 9011 (thin) seed models (Oncura, Inc., and GE Healthcare, Arlington Heights, IL). Image quality metrics were assessed using the standard deviation of ROIs between the seeds and the contrast to noise ratio (CNR) within the low contrast ROIs. For US images, water phantoms with both single and multiseed arrangements were constructed for both seed sizes. For MR images, both seeds were implanted into a porcine gel and imaged with pelvic imaging protocols. The standard deviation of ROIs and CNR values were used as metrics of artifact quantification. Seed localization within the CT images was assessed using the automated seed finder in a commercial brachytherapy treatment planning system. The number of erroneous seed placements and the average and maximum error in seed placements were recorded as metrics of the localization accuracy. Results: With the thin seeds, CT image noise was reduced from 48.5 {+-} 0.2 to 32.0 {+-} 0.2 HU and CNR improved by a median value of 74% when compared with the standard seeds. Ultrasound image noise was measured at 50.3 {+-} 17.1 dB for the thin seed images and 50.0 {+-} 19.8 dB for the standard seed images, and artifacts directly behind the seeds were smaller and less prominent with the thin seed model. For MR images, CNR of the standard seeds reduced on average 17% when using the thin seeds for all different imaging sequences and seed orientations, but these differences are not appreciable. Automated seed localization required an average ({+-}SD) of 7.0 {+-} 3.5 manual corrections in seed positions for the thin seed scans and 3.0 {+-} 1.2 manual corrections in seed positions for the standard seed scans. The average error in seed placement was 1.2 mm for both seed types and the maximum error in seed placement was 2.1 mm for the thin seed scans and 1.8 mm for the standard seed scans. Conclusions: The 9011 thin seeds yielded significantly improved image quality for CT and US images but no significant differences in MR image quality.

62 RADIOLOGY AND NUCLEAR MEDICINE↗