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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Removing imaging systematics from galaxy clustering measurements with Obiwan: application to the SDSS-IV extended Baryon Oscillation Spectroscopic Survey emission-line galaxy sample

This article presents the application of a new tool, Obiwan, which uses image simulations to determine the selection function of a galaxy redshift survey and calculate three-dimensional (3D) clustering statistics. Obiwan relies on a forward model of the process by which images of the night sky are transformed into a 3D large-scale structure catalogue, and offers several advantages over more traditional map-based techniques – such as operating on individual exposures and adopting a maximum likelihood approach. The photometric pipeline automatically detects and models galaxies and then generates a catalogue of such galaxies with detailed information for each one of them, including their location, redshift, and so on. Systematic biases in the imaging data are therefore imparted into the catalogues and must be accounted for in any scientific analysis of their information content. Obiwan simulates this process for samples selected from the Legacy Surveys imaging data. This imaging data will be used to select target samples for the next-generation Dark Energy Spectroscopic Instrument (DESI) experiment. Here, we apply Obiwan to a portion of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey emission-line galaxies (ELGs). Systematic biases in the data are clearly identified and removed. We compare the 3D clustering results to those obtained by the map-based approach applied to the complete eBOSS Data Release 16 (DR16) sample. We find the results are consistent, thereby validating the eBOSS DR16 ELG catalogues, which is used to obtain cosmological results.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Region-based convolutional neural network for wind turbine wake characterization from scanning lidars

A convolutional neural network is applied to lidar scan images from three experimental campaigns to identify and characterize wind turbine wakes. Initially developed as a proof-of-concept model and applied to a single data set in complex terrain, the model is now improved and generalized and applied to two other unique lidar data sets, one located near an escarpment and one located offshore. The model, initially developed using lidar scans collected in predominantly westerly flow, exhibits sensitivity to wind flow direction. The model is thus successfully generalized through implementing a standard rotation process to scan images before input into the convolutional neural network to ensure the flow is westerly. The sample size of lidar scans used to train the model is increased, and along with the generalization process, these changes to the model are shown to enhance accuracy and robustness when characterizing dissipating and asymmetric wakes. Applied to the offshore data set in which nearly 20 wind turbine wakes are included per scan, the improved model exhibits a 95% success rate in characterizing wakes and a 74% success rate in characterizing dissipating wake fragments. The improved model is shown to generalize well to the two new data sets, although an increase in wake characterization accuracy is offset by an increase in model sensitivity and false positive wake identifications.

17 WIND ENERGY↗
DOE OSTI2021

Dark energy survey year 3 results: weak lensing shape catalogue

ABSTRACT We present and characterize the galaxy shape catalogue from the first 3 yr of Dark Energy Survey (DES) observations, over an effective area of 4143 deg2 of the southern sky. We describe our data analysis process and our self-calibrating shear measurement pipeline metacalibration, which builds and improves upon the pipeline used in the DES Year 1 analysis in several aspects. The DES Year 3 weak-lensing shape catalogue consists of 100 204 026 galaxies, measured in the riz bands, resulting in a weighted source number density of neff = 5.59 gal arcmin−2 and corresponding shape noise σe = 0.261. We perform a battery of internal null tests on the catalogue, including tests on systematics related to the point spread function (PSF) modelling, spurious catalogue B-mode signals, catalogue contamination, and galaxy properties.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Adaptive Patching for High-resolution Image Segmentation with Transformers

Attention-based models are proliferating in the space of image analytics, including segmentation. The standard method of feeding images to transformer encoders is to divide the images into patches and then feed the patches to the model as a linear sequence of tokens. For high-resolution images, e.g. microscopic pathology images, the quadratic compute and memory cost prohibits the use of an attention-based model, if we are to use smaller patch sizes that are favorable in segmentation. The solution is to either use custom complex multi-resolution models or approximate attention schemes. We take inspiration from Adapative Mesh Refinement (AMR) methods in HPC by adaptively patching the images, as a pre-processing step, based on the image details to reduce the number of patches being fed to the model, by orders of magnitude. This method has a negligible overhead, and works seamlessly with any attention-based model, i.e. it is a pre-processing step that can be adopted by any attention-based model without friction. We demonstrate superior segmentation quality over SoTA segmentation models for realworld pathology datasets while gaining a geomean speedup of 6.9× for resolutions up to 64K2, on up to 2, 048 GPUs.

Zhang, Enzhi↗
DOE OSTI2016

TU-AB-BRA-01: Abdominal Synthetic CT Generation in Support of Liver SBRT Dose Calculation

Purpose: To demonstrate and validate a technique for generating MRI-derived synthetic CT volumes (MRCTs) in support of adaptive liver SBRT. Methods: Under IRB approval, ten hepatocellular carcinoma patients were scanned using a single MR sequence (T1 Dixon-VIBE), yielding inherently-registered water, fat, and T1-weighted images. Air-containing voxels were identified by intensity thresholding. The envelope of the anterior vertebral bodies was segmented from the fat image by fitting a shape model to vertebral body candidate voxels, then using level sets to expand the contour outward. Fuzzy-C-Means (FCM) was then used to classify each non-air voxel in the image as fat, water, bone, or marrow. Bone and marrow only were classified within the vertebral body envelope. The MRCT was created by integrating the product of the FCM class probability with the assigned class density for each voxel. The resulting MRCTs were deformably aligned with planning CTs and 2-ARC SBRT VMAT plans were optimized on the MRCT density maps. Fluence was copied onto the CT density grids and dose recalculated. Results: The MRCTs faithfully reproduced most of the features visible in the corresponding CT image volumes, with exceptions of ribs and posterior spinous processes. The liver, vertebral bodies, kidneys, spleen and cord all had median HU differences of less than 75 between MRCT and CT images. PTV D99% values had an average 0.2% difference (standard deviation: 0.46%) between calculations on MRCT and CT density grids. The maximum difference in dose to 0.1cc of the PTV was 0.25% (std:0.49%). OAR dose differences were similarly small (mean:0.03Gy, std:0.26Gy). The largest normal tissue complication percentage (NTCP) difference was 1.48% (mean:0.06%, std:0.54%). Conclusions: MRCTs from a single abdominal imaging sequence are promising for use in SBRT dose calculation. Future work will focus on extending models to better define bones in the upper abdomen. Supported by NIHR01EB016079 and NIH1L30CA199594-01.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Objective assessment in digital images of skin erythema caused by radiotherapy

Purpose: Skin toxicity caused by radiotherapy has been visually classified into discrete grades. The present study proposes an objective and continuous assessment method of skin erythema in digital images taken under arbitrary lighting conditions, which is the case for most clinical environments. The purpose of this paper is to show the feasibility of the proposed method. Methods: Clinical data were gathered from six patients who received carbon beam therapy for lung cancer. Skin condition was recorded using an ordinary compact digital camera under unfixed lighting conditions; a laser Doppler flowmeter was used to measure blood flow in the skin. The photos and measurements were taken at 3 h, 30, and 90 days after irradiation. Images were decomposed into hemoglobin and melanin colors using independent component analysis. Pixel values in hemoglobin color images were compared with skin dose and skin blood flow. The uncertainty of the practical photographic method was also studied in nonclinical experiments. Results: The clinical data showed good linearity between skin dose, skin blood flow, and pixel value in the hemoglobin color images; their correlation coefficients were larger than 0.7. It was deduced from the nonclinical that the uncertainty due to the proposed method with photography was 15%; such an uncertainty was not critical for assessment of skin erythema in practical use. Conclusions: Feasibility of the proposed method for assessment of skin erythema using digital images was demonstrated. The numerical relationship obtained helped to predict skin erythema by artificial processing of skin images. Although the proposed method using photographs taken under unfixed lighting conditions increased the uncertainty of skin information in the images, it was shown to be powerful for the assessment of skin conditions because of its flexibility and adaptability.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Neurogenesis Deep Learning: Extending deep networks to accommodate new classes

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing – data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Multisource Mobile Transfer Learning Algorithm Based on Dynamic Model Compression

With the development of the Internet of Things, the application of computer vision on mobile phones is becoming more and more extensive and people have higher and higher requirements for the timeliness of the recognition results returned and the processing capabilities of the mobile phone for image recognition. However, the processing capability and storage capability of the user terminal equipment cannot meet the needs of identifying and storing a large number of pictures, and the data transmission process will cause high energy consumption of the terminal equipment. At the same time, multisource deep transfer learning has outstanding performance in computer vision and image classification. However, due to the huge amount of calculation of the deep network model, it is impossible to use the existing excellent network model to realize image recognition and classification on the mobile terminal. In order to solve the abovementioned problems, we propose a multisource mobile transfer learning algorithm based on dynamic model compression, this algorithm considers the realization of multisource transfer learning computing in the case of multiple mobile device computing source domains, and the method also guarantees data privacy and security for each device (origin domain). Meanwhile, extensive experiments show that our method can achieve remarkable results in popular image classification datasets.

Gao, Peng↗
DOE OSTI2015

Vision 20/20: Positron emission tomography in radiation therapy planning, delivery, and monitoring

Positron emission tomography (PET) is increasingly considered as an effective imaging method to support several stages of radiation therapy. The combined usage of functional and morphological imaging in state-of-the-art PET/CT scanners is rapidly emerging to support the treatment planning process in terms of improved tumor delineation, and to assess the tumor response in follow-up investigations after or even during the course of fractionated therapy. Moreover, active research is being pursued on new tracers capable of providing different insights into tumor function, in order to identify areas of the planning volume which may require additional dosage for improved probability of tumor control. In this respect, major progresses in the next years will likely concern the development and clinical investigation of novel tracers and image processing techniques for reliable thresholding and segmentation, of treatment planning and beam delivery approaches integrating the PET imaging information, as well as improved multimodal clinical instrumentation such as PET/MR. But especially in the rapidly emerging case of ion beam therapy, the usage of PET is not only limited to the imaging of external tracers injected to the patient. In fact, a minor amount of positron emitters is formed in nuclear fragmentation reactions between the impinging ions and the tissue, bearing useful information for confirmation of the delivered treatment during or after therapeutic irradiation. Different implementations of unconventional PET imaging for therapy monitoring are currently being investigated clinically, and major ongoing research aims at new dedicated detector technologies and at challenging applications such as real-time imaging and time-resolved in vivo verification of motion compensated beam delivery. This paper provides an overview of the different areas of application of PET in radiation oncology and discusses the most promising perspectives in the years to come for radiation therapy planning, delivery, and monitoring.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-54: Towards Real-Time Volumetric Imaging Using the Treatment Beam and KV Beam

Purpose: Existing real-time imaging uses dual (orthogonal) kV beam fluoroscopies and may result in significant amount of extra radiation to patients, especially for prolonged treatment cases. In addition, kV projections only provide 2D information, which is insufficient for in vivo dose reconstruction. We propose real-time volumetric imaging using prior knowledge of pre-treatment 4D images and real-time 2D transit data of treatment beam and kV beam. Methods: The pre-treatment multi-snapshot volumetric images are used to simulate 2D projections of both the treatment beam and kV beam, respectively, for each treatment field defined by the control point. During radiation delivery, the transit signals acquired by the electronic portal image device (EPID) are processed for every projection and compared with pre-calculation by cross-correlation for phase matching and thus 3D snapshot identification or real-time volumetric imaging. The data processing involves taking logarithmic ratios of EPID signals with respect to the air scan to reduce modeling uncertainties in head scatter fluence and EPID response. Simulated 2D projections are also used to pre-calculate confidence levels in phase matching. Treatment beam projections that have a low confidence level either in pre-calculation or real-time acquisition will trigger kV beams so that complementary information can be exploited. In case both the treatment beam and kV beam return low confidence in phase matching, a predicted phase based on linear regression will be generated. Results: Simulation studies indicated treatment beams provide sufficient confidence in phase matching for most cases. At times of low confidence from treatment beams, kV imaging provides sufficient confidence in phase matching due to its complementary configuration. Conclusion: The proposed real-time volumetric imaging utilizes the treatment beam and triggers kV beams for complementary information when the treatment beam along does not provide sufficient confidence for phase matching. This strategy minimizes the use of extra radiation to patients. This project is partially supported by a Varian MRA grant.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-71: Improving CT Quality for Radiation Therapy Planning and Delivery Guidance Using a Non-Linear Contrast Enhancement Technique

Purpose: Advanced image post-processing techniques which enhance soft-tissue contrast in CT have not been widely employed for RT planning or delivery guidance. The purpose of this work is to assess the soft-tissue contrast enhancement from non-linear contrast enhancing filters and its impact in RT. The contrast enhancement reduces patient alignment uncertainties. Methods: Non-linear contrast enhancing methods, such as Best Contrast (Siemens), amplify small differences in X-ray attenuation between two adjacent structure without significantly increasing noise. Best Contrast (BC) separates a CT into two frequency bands. The low frequency band is modified by a non-linear scaling function before recombination with the high frequency band. CT data collected using a CT-on-rails (Definition AS Open, Siemens) during daily CT-guided RT for 6 prostate cancer patients and an image quality phantom (The Phantom Laboratory) were analyzed. Images acquired with a standard protocol (120 kVp, 0.6 pitch, 18 mGy CTDIvol) were processed before comparison to the unaltered images. Contrast and noise were measured in the the phantom. Inter-observer variation was assessed by placing prostate contours on the 12 CT study sets, 6 enhanced and 6 unaltered, in a blinded study involving 8 observers. Results: The phantom data demonstrate that BC increased the contrast between the 1.0% supra-slice element and the background substrate by 46.5 HU while noise increased by only 2.3 HU. Thus the contrast to noise ratio increased from 1.28 to 6.71. Furthermore, the variation in centroid position of the prostate contours was decreased from 1.3±0.4 mm to 0.8±0.3 mm. Thus the CTV-to-PTV margin was reduced by 1.1 mm. The uncertainty in delineation of the prostate/rectum edge decreased by 0.5 mm. Conclusion: As demonstrated in phantom and patient scans the BC filter accentuates soft-tissue contrast. This enhancement leads to reduced inter-observer variation, which should improve RT planning and delivery. Supported by Siemens.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Advanced Real Time Optical Imaging: Part II

The first segment of this special topic, Advanced Real-time Optical Imaging: Part I (July 2018), featured a variety of studies based on techniques including hot thermocouple, particle image velocimetry, high-speed camera, infrared imaging, and environmental confocal scanning laser microscopy. Here, the papers contributed by experts in each field re-emphasized the significance of real-time optical imaging in materials science and processing. Emerging, traditional, and mixed imaging techniques were presented describing creative and innovative methods of real-time materials investigations. Part II follows the same goal as the first segment: that advanced real-time optical imaging techniques are assembled in a single venue to emphasize the diversity and strength of these approaches.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Optical droplet vaporization of nanoparticle-loaded stimuli-responsive microbubbles

A capillary co-flow focusing process is developed to generate stimuli-responsive microbubbles (SRMs) that comprise perfluorocarbon (PFC) suspension of silver nanoparticles (SNPs) in a lipid shell. Upon continuous laser irradiation at around their surface plasmon resonance band, the SNPs effectively absorb electromagnetic energy, induce heat accumulation in SRMs, trigger PFC vaporization, and eventually lead to thermal expansion and fragmentation of the SRMs. This optical droplet vaporization (ODV) process is further simulated by a theoretical model that combines heat generation of SNPs, phase change of PFC, and thermal expansion of SRMs. The model is validated by benchtop experiments, where the ODV process is monitored by microscopic imaging. The effects of primary process parameters on behaviors of ODV are predicted by the theoretical model, indicating the technical feasibility for process control and optimization in future drug delivery applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Automatic Image Point Matching

Sparse Image Point Matching (SIPM) is a foundational technology for photo triangulation, structure from motion (SfM), Simultaneous Location and Mapping (SLAM), and data fusion. The goal of the matching is to automatically generate sets of image coordinates that identify the same feature across images. Ideally, the process should be robust to lighting, scale, perspective, and modality changes. The scope of the image matching topic in the field of remote sensing (RS) is enormous because of the variety of collection platforms, modalities, sensor types, applications, and subjects. In this work, we report the history of and assess the state of the art of visible-spectrum (panchromatic and color) image matching of the Earth’s surface. Work specific to large-format images (LFI) (e.g., metric aerial cameras and Earth-observing satellites) will be highlighted. However, the state of the art in this century will mostly be traced through machine vision research and benchmarks because research specific to LFI is rare.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Automated selection of the optimal cardiac phase for single-beat coronary CT angiography reconstruction

Purpose: Reconstructing a low-motion cardiac phase is expected to improve coronary artery visualization in coronary computed tomography angiography (CCTA) exams. This study developed an automated algorithm for selecting the optimal cardiac phase for CCTA reconstruction. The algorithm uses prospectively gated, single-beat, multiphase data made possible by wide cone-beam imaging. The proposed algorithm differs from previous approaches because the optimal phase is identified based on vessel image quality (IQ) directly, compared to previous approaches that included motion estimation and interphase processing. Because there is no processing of interphase information, the algorithm can be applied to any sampling of image phases, making it suited for prospectively gated studies where only a subset of phases are available. Methods: An automated algorithm was developed to select the optimal phase based on quantitative IQ metrics. For each reconstructed slice at each reconstructed phase, an image quality metric was calculated based on measures of circularity and edge strength of through-plane vessels. The image quality metric was aggregated across slices, while a metric of vessel-location consistency was used to ignore slices that did not contain through-plane vessels. The algorithm performance was evaluated using two observer studies. Fourteen single-beat cardiac CT exams (Revolution CT, GE Healthcare, Chalfont St. Giles, UK) reconstructed at 2% intervals were evaluated for best systolic (1), diastolic (6), or systolic and diastolic phases (7) by three readers and the algorithm. Pairwise inter-reader and reader-algorithm agreement was evaluated using the mean absolute difference (MAD) and concordance correlation coefficient (CCC) between the reader and algorithm-selected phases. A reader-consensus best phase was determined and compared to the algorithm selected phase. In cases where the algorithm and consensus best phases differed by more than 2%, IQ was scored by three readers using a five point Likert scale. Results: There was no statistically significant difference between inter-reader and reader-algorithm agreement for either MAD or CCC metrics (p > 0.1). The algorithm phase was within 2% of the consensus phase in 15/21 of cases. The average absolute difference between consensus and algorithm best phases was 2.29% ± 2.47%, with a maximum difference of 8%. Average image quality scores for the algorithm chosen best phase were 4.01 ± 0.65 overall, 3.33 ± 1.27 for right coronary artery (RCA), 4.50 ± 0.35 for left anterior descending (LAD) artery, and 4.50 ± 0.35 for left circumflex artery (LCX). Average image quality scores for the consensus best phase were 4.11 ± 0.54 overall, 3.44 ± 1.03 for RCA, 4.39 ± 0.39 for LAD, and 4.50 ± 0.18 for LCX. There was no statistically significant difference (p > 0.1) between the image quality scores of the algorithm phase and the consensus phase. Conclusions: The proposed algorithm was statistically equivalent to a reader in selecting an optimal cardiac phase for CCTA exams. When reader and algorithm phases differed by >2%, image quality as rated by blinded readers was statistically equivalent. By detecting the optimal phase for CCTA reconstruction, the proposed algorithm is expected to improve coronary artery visualization in CCTA exams.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

MO-D-PinS Room/Hall E-02: Multiparametric Fuctional Imaging in Radiation Therapy

MRI, with its excellent soft tissue contrast and its ability to provide physiological as well as anatomical information, is becoming increasingly used in radiation therapy for treatment planning, image-guided radiation therapy, and treatment evaluation. This session will explore solutions to integrating MRI into the simulation process. Obstacles for using MRI for simulation include distortions and artifacts, image acquisition speed, complexity of imaging techniques, and lack of electron density information. Partners in Solutions presents vendor representatives who will present their approaches to meeting these challenges and others. An increased awareness of how MRI simulation works will allow physicists to better understand and use this powerful technique. The speakers are all employees who are presenting information about their company’s products.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

An innovative digital image correlation technique for in-situ process monitoring of composite structures in large scale additive manufacturing

As additive manufacturing (AM) continues to develop and become a standardized manufacturing method, there will be a continued need to provide in-situ monitoring during the manufacturing of polymer composite printed components. Thermal residual stress is a primary cause of failures such as interlayer disbonds or delamination, micro cracking, and dimensional instability, which can occur during or after the build. Here, we report a novel digital image correlation (DIC) adaptation to monitor thermal residual stresses during the entire print process for large-scale AM. In this work, DIC has been investigated (a) by the natural speckle produced by the polymer surface for correlation, (b) to monitor AM build, and (c) to evaluate the effect of thermal residual stress on warpage of the printed component. The natural speckle pattern of the AM material resulted in a respectable 3.57% error compared to the traditional painted speckle pattern of 3.05% error. DIC measured a 190% increase in vertical displacement at the edge of the wall compared to the center, indicating warpage during AM. This work is a step towards a non-intrusive residual stress measuring technique using DIC for large-scale AM.

36 MATERIALS SCIENCE↗