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DOE OSTI2014

Reconstruction of biofilm images: combining local and global structural parameters

Digitized images can be used for quantitative comparison of biofilms grown under different conditions. Using biofilm image reconstruction, it was previously found that biofilms with a completely different look can have nearly identical structural parameters and that the most commonly utilized global structural parameters were not sufficient to uniquely define these biofilms. Here, additional local and global parameters are introduced to show that these parameters considerably increase the reliability of the image reconstruction process. Assessment using human evaluators indicated that the correct identification rate of the reconstructed images increased from 50% to 72% with the introduction of the new parameters into the reconstruction procedure. An expanded set of parameters especially improved the identification of biofilm structures with internal orientational features and of structures in which colony sizes and spatial locations varied. Hence, the newly introduced structural parameter sets helped to better classify the biofilms by incorporating finer local structural details into the reconstruction process.

biofilm↗

DOE OSTI2023

System and method to fuse multiple sources of optical data to generate a high-resolution, frequent and cloud-/gap-free surface reflectance product

Aspects of the subject disclosure may include, for example, performing, by a processing system, image fusion using two or more groups of images to generate predicted images, wherein each group of the two or more groups has one of a different resolution, a different frequency temporal pattern or a combination thereof than another of the two or more groups. Gap filling can be performed by the processing system to correct images of the two or more groups. Additional embodiments are disclosed.

Guan, Kaiyu↗

DOE OSTI2016

SU-F-P-06: Moving From Computed Radiography to Digital Radiography: A Collaborative Approach to Improve Image Quality

Purpose: To bring together radiologists, technologists, and physicists to utilize post-processing techniques in digital radiography (DR) in order to optimize image acquisition and improve image quality. Methods: Sub-optimal images acquired on a new General Electric (GE) DR system were flagged for follow-up by radiologists and reviewed by technologists and medical physicists. Various exam types from adult musculoskeletal (n=35), adult chest (n=4), and pediatric (n=7) were chosen for review. 673 total images were reviewed. These images were processed using five customized algorithms provided by GE. An image score sheet was created allowing the radiologist to assign a numeric score to each of the processed images, this allowed for objective comparison to the original images. Each image was scored based on seven properties: 1) overall image look, 2) soft tissue contrast, 3) high contrast, 4) latitude, 5) tissue equalization, 6) edge enhancement, 7) visualization of structures. Additional space allowed for additional comments not captured in scoring categories. Radiologists scored the images from 1 – 10 with 1 being non-diagnostic quality and 10 being superior diagnostic quality. Scores for each custom algorithm for each image set were summed. The algorithm with the highest score for each image set was then set as the default processing. Results: Images placed into the PACS “QC folder” for image processing reasons decreased. Feedback from radiologists was, overall, that image quality for these studies had improved. All default processing for these image types was changed to the new algorithm. Conclusion: This work is an example of the collaboration between radiologists, technologists, and physicists at the University of New Mexico to add value to the radiology department. The significant amount of work required to prepare the processing algorithms, reprocessing and scoring of the images was eagerly taken on by all team members in order to produce better quality images and improve patient care.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

SU-E-J-256: Dual Energy Planar Image Based Localization in the Absence of On-Board CT Images

Purpose: To develop a tool enabling soft tissue based image guidance using dual energy radiographs for cases when on-board CT is not available. Method: Dual energy planar radiographs can be applied to image guidance for targeting lung lesions because the bone based alignment only may not be sufficient as the lesions move. We acquired images of an anthropomorphic thorax phantom at 120 and 60 KVp respectively. Using a weighted logarithmic subtraction of these dual energy images, a soft tissue enhanced and a bone enhanced image were generated and they could be used for the image guidance purpose. Similar processing was also applied to a dual energy image set acquired for a patient undergoing a proton therapy. Results: The soft tissue enhanced images suppressed bones (ribs and scapula) overlying on lung, thus enabling a better visualization of soft tissue and lesion, while the bone enhanced image suppressed the soft tissue. These enhanced effects were visually apparent without further processing for display enhancements, such as using histogram or edge enhancement technique. Conclusions: The phantom image processing was encouraging. The initial test on the patient image set showed that other post processing might still be able to add value in visualizing soft tissues in addition to the dual energy soft tissue enhancement. More evaluations are needed to determine the potential benefit of this technique in the clinic.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Profile Images and Annotations for Vehicle Re-identification Algorithms (PRIMAVERA)

This dataset contains 636,246 profile images of vehicles representing 13,963 unique vehicles. The data was collected by a set of roadside sensors over the course of three years. Each time a vehicle passed by one of the sensors, a series of images was collected. The images were processed to detect and localize each vehicle, and a license plate reader collocated with the sensor was used to provide a unique ID for the vehicle. Actual license plate numbers have been obfuscated by replacing with an arbitrary numerical ID for each vehicle. After localizing the vehicle in each image, the original RGB image was rotated, scaled, and shifted to produce a new RGB image of size 234x234 pixels such that the outermost two wheels are located at predetermined pixel locations in the image. In this way, all vehicle images are aligned to one another. This registration process occasionally results in a portion of certain vehicles being cutoff at the edges of the image. The dataset has been partitioned into two sets called training and validation. The two partitions no common vehicles, i.e., a vehicle present in one partition is guaranteed not to be present in the other. In this way, an algorithm can be validated against a set of new vehicles that were not seen during the training process. The training set contains 543,926 images from 64,440 vehicle passes representing 11,918 unique vehicles, while the validation set contains 92,320 images from 10,991 vehicle passes representing 2,045 unique vehicles. Vehicle images are organized by directories corresponding to unique vehicles. The file naming scheme is as follows: veh_{vehID}_tr_{passID}_{frameID}_{elevation}_{timeofday}.jpg where {vehID} is the vehicle ID (unique across the entire dataset), {passID} is an identifier for each tracked vehicle pass (unique across the entire dataset), {frameID} is the index of the frame within the given vehicle pass starting at 0, {elevation} is a two-letter string indicating whether the sensor was elevated (el) or at ground-level (gl), and {timeofday} is a two-letter string indicating whether the image was captured during daytime (dt) or nighttime (nt).

image↗

DOE OSTI2020

Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling

We investigate the ability to reconstruct and derive spatial structure from sparsely sampled 3D piezoresponse force microcopy data, captured using the band-excitation (BE) technique, via Gaussian Process (GP) methods. Even for weakly informative priors, GP methods allow unambiguous determination of the characteristic length scales of the imaging process both in spatial and frequency domains. We further show that BE data set tends to be oversampled in the spatial domains, with ~30% of original data set sufficient for high-quality reconstruction, potentially enabling faster BE imaging. At the same time, reliable reconstruction along the frequency domain requires the resonance peak to be within the measured band. This behavior suggests the optimal strategy for the BE imaging on unknown samples. Finally, we discuss how GP can be used for automated experimentation in SPM, by combining GP regression with non-rectangular scans.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Image fusion for neutron tomography of nuclear fuel

Image fusion, the process of combining different images together, can be useful to create a more complete picture. In this work, image fusion is applied to neutron tomography of nuclear fuel with the goal of enhancing the information obtained about the fuel. Different reconstruction methods, such as Feldkamp, Davis and Kress filtered back projection and Simultaneous Reconstruction Technique, were combined to enhance image quality. This methodology was shown to reduce noise and ring artifacts without sacrificing sharp edges, allowing for a more accurate representation of sample geometry. Technique enhancements and future applications for the neutron imaging community are also discussed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2018

Simultaneous direct measurements of concentration and velocity in the Richtmyer–Meshkov instability

The Richtmyer–Meshkov instability (RMI) is experimentally investigated in a vertical shock tube using a broadband initial condition imposed on an interface between a helium–acetone mixture and argon (Atwood number $A\approx 0.7$ ). In the present work, a shear layer is introduced at the interface to serve as a statistically repeatable, broadband initial condition to the RMI, and the density interface is accelerated by either an $M=1.6$ or $M=2.2$ planar shock wave. The development of the ensuing mixing layer is investigated using simultaneous planar laser-induced fluorescence (PLIF) and particle image velocimetry (PIV). PLIF images are processed to reveal the light-gas mole fraction, while PIV particle image pairs yield corresponding two-component planar velocity results. Field structure and distribution are explored through probability density functions (PDFs), and a decomposition is performed on concentration and velocity results to obtain a mean flow field and define fluctuations. Simultaneous concentration and velocity field measurements allow – for the first time in this regime – experimentally determined turbulence quantities such as Reynolds stresses, turbulent mass-flux velocities and turbulent kinetic energy to be obtained. We show that by the latest times the mixing layer has passed the turbulent threshold, and there is evidence of turbulent mixing occurring sooner for the higher Mach number case. Interface measurements show nonlinear growth with a power-law fit to the thickness data, and that integral measurements of mixing layer thickness are proportional to threshold measurements. Spectral analysis demonstrates the emergence of an inertial range with a slope ${\sim}k^{-5/3}$ when considering both density and velocity effects in planar turbulent kinetic energy (TKE) measurements.

Mechanics↗

DOE OSTI2013

EFFECTS OF DIFFUSE BACKGROUND EMISSION AND SOURCE CROWDING ON PHOTOMETRIC COMPLETENESS IN SPITZER SPACE TELESCOPE IRAC SURVEYS: THE GLIMPSE CATALOGS AND ARCHIVES

We characterize the completeness of point source lists from Spitzer Space Telescope surveys in the four Infrared Array Camera (IRAC) bandpasses, emphasizing the Galactic Legacy Infrared Mid-Plane Survey Extraordinaire (GLIMPSE) programs (GLIMPSE I, II, 3D, 360; Deep GLIMPSE) and their resulting point source Catalogs and Archives. The analysis separately addresses effects of incompleteness resulting from high diffuse background emission and incompleteness resulting from point source confusion (i.e., crowding). An artificial star addition and extraction analysis demonstrates that completeness is strongly dependent on local background brightness and structure, with high-surface-brightness regions suffering up to five magnitudes of reduced sensitivity to point sources. This effect is most pronounced at the IRAC 5.8 and 8.0 {mu}m bands where UV-excited polycyclic aromatic hydrocarbon emission produces bright, complex structures (photodissociation regions). With regard to diffuse background effects, we provide the completeness as a function of stellar magnitude and diffuse background level in graphical and tabular formats. These data are suitable for estimating completeness in the low-source-density limit in any of the four IRAC bands in GLIMPSE Catalogs and Archives and some other Spitzer IRAC programs that employ similar observational strategies and are processed by the GLIMPSE pipeline. By performing the same analysis on smoothed images we show that the point source incompleteness is primarily a consequence of structure in the diffuse background emission rather than photon noise. With regard to source confusion in the high-source-density regions of the Galactic Plane, we provide figures illustrating the 90% completeness levels as a function of point source density at each band. We caution that completeness of the GLIMPSE 360/Deep GLIMPSE Catalogs is suppressed relative to the corresponding Archives as a consequence of rejecting stars that lie in the point-spread function wings of saturated sources. This effect is minor in regions of low saturated star density, such as toward the Outer Galaxy; this effect is significant along sightlines having a high density of saturated sources, especially for Deep GLIMPSE and other programs observing closer to the Galactic center using 12 s or longer exposure times.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2013

Needle segmentation using 3D Hough transform in 3D TRUS guided prostate transperineal therapy

Purpose: Prostate adenocarcinoma is the most common noncutaneous malignancy in American men with over 200 000 new cases diagnosed each year. Prostate interventional therapy, such as cryotherapy and brachytherapy, is an effective treatment for prostate cancer. Its success relies on the correct needle implant position. This paper proposes a robust and efficient needle segmentation method, which acts as an aid to localize the needle in three-dimensional (3D) transrectal ultrasound (TRUS) guided prostate therapy. Methods: The procedure of locating the needle in a 3D TRUS image is a three-step process. First, the original 3D ultrasound image containing a needle is cropped; the cropped image is then converted to a binary format based on its histogram. Second, a 3D Hough transform based needle segmentation method is applied to the 3D binary image in order to locate the needle axis. The position of the needle endpoint is finally determined by an optimal threshold based analysis of the intensity probability distribution. The overall efficiency is improved through implementing a coarse-fine searching strategy. The proposed method was validated in tissue-mimicking agar phantoms, chicken breast phantoms, and 3D TRUS patient images from prostate brachytherapy and cryotherapy procedures by comparison to the manual segmentation. The robustness of the proposed approach was tested by means of varying parameters such as needle insertion angle, needle insertion length, binarization threshold level, and cropping size. Results: The validation results indicate that the proposed Hough transform based method is accurate and robust, with an achieved endpoint localization accuracy of 0.5 mm for agar phantom images, 0.7 mm for chicken breast phantom images, and 1 mm for in vivo patient cryotherapy and brachytherapy images. The mean execution time of needle segmentation algorithm was 2 s for a 3D TRUS image with size of 264 Multiplication-Sign 376 Multiplication-Sign 630 voxels. Conclusions: The proposed needle segmentation algorithm is accurate, robust, and suitable for 3D TRUS guided prostate transperineal therapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TU-H-BRA-05: A System Design for Integration of An Interior MRI and a Linear Accelerator

Purpose: MRI is a highly desirable modality to guide radiation therapy but it is difficult to combine a conventional MRI scanner directly with a linear accelerator (linac). An interior MRI (iMRI) concept has been proposed to acquire MRI images within a small field of view only covering targets and immediate surrounding tissues. The objective of this project is to design an interior MRI system to work with a linac using a magnet to provide a field around 0.2T in a cube of 20cm per side, and perform image reconstruction with a slightly inhomogeneous static magnetic fields. Methods: All the results are simulated using a commercially available software package, FARADY. In our design, a ring structure holds the iMRI system and also imbeds a linac treatment head. The ring is synchronized to the linac gantry rotation. Half of the ring is made of steel and becomes a magnetic flux return path (yoke) so that a strong magnetic field will be limited inside the iron circuit and fringe fields will be very weak. In order to increase the static magnetic field homogeneity, special steel magnet boots or tips were simulated. Three curved boots were designed based on two-dimensional curves: arc, parabola and hyperbola. Results: Different boot surfaces modify magnetic field distributions differently. With the same pair of neodymium-iron-boron (NdFeB) magnets, the magnetic induction at the centers are 0.217T, 0.201T, 0.204T, and 0.212T for flat, arc, parabola and hyperbola boots, respectively. The hyperbola boots lead to the most homogeneous results, the static magnetic field deviations are within 0.5% in a cube of 20cm, and can be further improved using shimming techniques. Conclusion: This study supports the concept of an iMRI design. Successful development of iMRI will provide crucial information for tumor delineation in radiation therapy.

43 PARTICLE ACCELERATORS↗

DOE OSTI2016

Impact of compressed breast thickness and dose on lesion detectability in digital mammography: FROC study with simulated lesions in real mammograms

Purpose: The aim of this work was twofold: (1) to examine whether, with standard automatic exposure control (AEC) settings that maintain pixel values in the detector constant, lesion detectability in clinical images decreases as a function of breast thickness and (2) to verify whether a new AEC setup can increase lesion detectability at larger breast thicknesses. Methods: Screening patient images, acquired on two identical digital mammography systems, were collected over a period of 2 yr. Mammograms were acquired under standard AEC conditions (part 1) and subsequently with a new AEC setup (part 2), programmed to use the standard AEC settings for compressed breast thicknesses ≤49 mm, while a relative dose increase was applied above this thickness. The images were divided into four thickness groups: T1 ≤ 29 mm, T2 = 30–49 mm, T3 = 50–69 mm, and T4 ≥ 70 mm, with each thickness group containing 130 randomly selected craniocaudal lesion-free images. Two measures of density were obtained for every image: a BI-RADS score and a map of volumetric breast density created with a software application (VolparaDensity, Matakina, NZ). This information was used to select subsets of four images, containing one image from each thickness group, matched to a (global) BI-RADS score and containing a region with the same (local) VOLPARA volumetric density value. One selected lesion (a microcalcification cluster or a mass) was simulated into each of the four images. This process was repeated so that, for a given thickness group, half the images contained a single lesion and half were lesion-free. The lesion templates created and inserted in groups T3 and T4 for the first part of the study were then inserted into the images of thickness groups T3 and T4 acquired with higher dose settings. Finally, all images were visualized using the ViewDEX software and scored by four radiologists performing a free search study. A statistical jackknife-alternative free-response receiver operating characteristic analysis was applied. Results: For part 1, the alternative free-response receiver operating characteristic curves for the four readers were 0.80, 0.65, 0.55 and 0.56 in going from T1 to T4, indicating a decrease in detectability with increasing breast thickness. P-values and the 95% confidence interval showed no significant difference for the T3-T4 comparison (p = 0.78) while all the other differences were significant (p < 0.05). Separate analysis of microcalcification clusters presented the same results while for mass detection, the only significant difference came when comparing T1 to the other thickness groups. Comparing the scores of part 1 and part 2, results for the T3 group acquired with the new AEC setup and T3 group at standard AEC doses were significantly different (p = 0.0004), indicating improved detection. For this group a subanalysis for microcalcification detection gave the same results while no significant difference was found for mass detection. Conclusions: These data using clinical images confirm results found in simple QA tests for many mammography systems that detectability falls as breast thickness increases. Results obtained with the AEC setup for constant detectability above 49 mm showed an increase in lesion detection with compressed breast thickness, bringing detectability of lesions to the same level.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

A Closed-Loop Proportional-Integral (PI) Control Software for Fully Mechanically Controlled Automated Electron Microscopic Tomography

A closed-loop proportional-integral (PI) control software is provided for fully mechanically controlled automated electron microscopic tomography. The software is developed based on Gatan DigitalMicrograph, and is compatible with Zeiss LIBRA 120 transmission electron microscope. However, it can be expanded to other TEM instrument with modification. The software consists of a graphical user interface, a digital PI controller, an image analyzing unit, and other drive units (i.e.: image acquire unit and goniometer drive unit). During a tomography data collection process, the image analyzing unit analyzes both the accumulated shift and defocus value of the latest acquired image, and provides the results to the digital PI controller. The digital PI control compares the results with the preset values and determines the optimum adjustments of the goniometer. The goniometer drive unit adjusts the spatial position of the specimen according to the instructions given by the digital PI controller for the next tilt angle and image acquisition. The goniometer drive unit achieves high precision positioning by using a backlash elimination method. The major benefits of the software are: 1) the goniometer drive unit keeps pre-aligned/optimized beam conditions unchanged and achieves position tracking solely through mechanical control; 2) the image analyzing unit relies on only historical data and therefore does not require additional images/exposures; 3) the PI controller enables the system to dynamically track the imaging target with extremely low system error.

Ren, Gang↗

DOE OSTI2021

Method and apparatus to obtain limited angle tomographic images from stationary gamma cameras

A nuclear imaging system and method for performing three-dimensional imaging of anatomical structures. The system and method includes two or more gamma ray detectors each used in combination with a variable-slant hole collimator. The detectors are positioned in close proximity to, or in contact with, the structure being imaged. The detectors remain in a stationary position during the data collection process. An imaging or reconstruction method is then used to reconstruct a three-dimensional image from the data derived from the detectors.

Kross, Brian J.↗

DOE OSTI2022

Automatic Detection and Classification of Radio Galaxy Images by Deep Learning

Abstract Surveys conducted by radio astronomy observatories, such as SKA, MeerKAT, Very Large Array, and ASKAP, have generated massive astronomical images containing radio galaxies (RGs). This generation of massive RG images has imposed strict requirements on the detection and classification of RGs and makes manual classification and detection increasingly difficult, even impossible. Rapid classification and detection of images of different types of RGs help astronomers make full use of the observed astronomical image data for further processing and analysis. The classification of FRI and FRII is relatively easy, and there are more studies and literature on them at present, but FR0 and FRI are similar, so it is difficult to distinguish them. It poses a greater challenge to image processing. At present, deep learning has made breakthrough progress in the field of image analysis and processing and has preliminary applications in astronomical data processing. Compared with classification algorithms that can only classify galaxies, object detection algorithms that can locate and classify RGs simultaneously are preferred. In target detection algorithms, YOLOv5 has outstanding advantages in the classification and positioning of small targets. Therefore, we propose a deep-learning method based on an improved YOLOv5 object detection model that makes full use of multisource data, combining FIRST radio with SDSS optical image data, and realizes the automatic detection of FR0, FRI, and FRII RGs. The innovation of our work is that on the basis of the original YOLOv5 object detection model, we introduce the SE Net attention mechanism, increase the number of preset anchors, adjust the network structure of the feature pyramid, and modify the network structure, thereby allowing our model to demonstrate galaxy classification and position detection effects. Our improved model produces satisfactory results, as evidenced by experiments. Overall, the mean average precision (mAP@0.5) of our improved model on the test set reaches 89.4%, which can determine the position (R.A. and decl.) and automatically detect and classify FR0s, FRIs, and FRIIs. Our work contributes to astronomy because it allows astronomers to locate FR0, FRI, and FRII galaxies in a relatively short time and can be further combined with other astronomically generated data to study the properties of these galaxies. The target detection model can also help astronomers find FR0s, FRIs, and FRIIs in future surveys and build a large-scale star RG catalog. Moreover, our work is also useful for the detection of other types of galaxies.

Astronomy & Astrophysics↗

DOE OSTI2022

Star–Galaxy Image Separation with Computationally Efficient Gaussian Process Classification

Abstract We introduce a novel method for discerning optical telescope images of stars from those of galaxies using Gaussian processes (GPs). Although applications of GPs often struggle in high-dimensional data modalities such as optical image classification, we show that a low-dimensional embedding of images into a metric space defined by the principal components of the data suffices to produce high-quality predictions from real large-scale survey data. We develop a novel method of GP classification hyperparameter training that scales approximately linearly in the number of image observations, which allows for application of GP models to large-size Hyper Suprime-Cam Subaru Strategic Program data. In our experiments, we evaluate the performance of a principal component analysis embedded GP predictive model against other machine-learning algorithms, including a convolutional neural network and an image photometric morphology discriminator. Our analysis shows that our methods compare favorably with current methods in optical image classification while producing posterior distributions from the GP regression that can be used to quantify object classification uncertainty. We further describe how classification uncertainty can be used to efficiently parse large-scale survey imaging data to produce high-confidence object catalogs.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Simultaneous concentration and velocity field measurements in a shock-accelerated mixing layer

Here, a novel technique to obtain simultaneous velocity and concentration measurements is applied to the Richtmyer–Meshkov instability. After acceleration by a Mach 2.2 shock wave, the interface between the two gases develops into a turbulent mixing layer. A time-separated pair of acetone planar laser-induced fluorescence images are processed to yield concentration and, through application of the Advection-Corrected Correlation Image Velocimetry technique, velocity fields. This is the first application of this technique to shock-accelerated flows. We show that when applied to numerical simulations, this technique reproduces the velocity field to a similar quality as particle image velocimetry. When applied to the turbulent mixing layer of the experiments, information about the Reynolds number and anisotropy of the flow is obtained.

42 ENGINEERING↗

DOE OSTI2017

Mitigating Effects of Missing Data for SAR Coherent Images

Missing samples within synthetic aperture radar data result in image distortions. For coherent data products, such as coherent change detection and interferometric processing, the image distortion can be devastating to these second order products, resulting in missed detections and inaccurate height maps. Earlier approaches to repair the coherent data products focus upon reconstructing the missing data samples. This study demonstrates that reconstruction is not necessary to restore the quality of the coherent data products.

47 OTHER INSTRUMENTATION↗