Results for “Image Processing” — Page 60

DOE OSTI2016

SU-G-IeP3-08: Image Reconstruction for Scanning Imaging System Based On Shape-Modulated Point Spreading Function

Purpose: Deconvolution is a widely used tool in the field of image reconstruction algorithm when the linear imaging system has been blurred by the imperfect system transfer function. However, due to the nature of Gaussian-liked distribution for point spread function (PSF), the components with coherent high frequency in the image are hard to restored in most of the previous scanning imaging system, even the relatively accurate PSF is acquired. We propose a novel method for deconvolution of images which are obtained by using shape-modulated PSF. Methods: We use two different types of PSF - Gaussian shape and donut shape - to convolute the original image in order to simulate the process of scanning imaging. By employing deconvolution of the two images with corresponding given priors, the image quality of the deblurred images are compared. Then we find the critical size of the donut shape compared with the Gaussian shape which has similar deconvolution results. Through calculation of tightened focusing process using radially polarized beam, such size of donut is achievable under same conditions. Results: The effects of different relative size of donut and Gaussian shapes are investigated. When the full width at half maximum (FWHM) ratio of donut and Gaussian shape is set about 1.83, similar resolution results are obtained through our deconvolution method. Decreasing the size of donut will favor the deconvolution method. A mask with both amplitude and phase modulation is used to create a donut-shaped PSF compared with the non-modulated Gaussian PSF. Donut with size smaller than our critical value is obtained. Conclusion: The utility of donutshaped PSF are proved useful and achievable in the imaging and deconvolution processing, which is expected to have potential practical applications in high resolution imaging for biological samples.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-G-303-00: Radiomics: Advances in the Use of Quantitative Imaging Used for Predictive Modeling

‘Radiomics’ refers to studies that extract a large amount of quantitative information from medical imaging studies as a basis for characterizing a specific aspect of patient health. Radiomics models can be built to address a wide range of outcome predictions, clinical decisions, basic cancer biology, etc. For example, radiomics models can be built to predict the aggressiveness of an imaged cancer, cancer gene expression characteristics (radiogenomics), radiation therapy treatment response, etc. Technically, radiomics brings together quantitative imaging, computer vision/image processing, and machine learning. In this symposium, speakers will discuss approaches to radiomics investigations, including: longitudinal radiomics, radiomics combined with other biomarkers (‘pan-omics’), radiomics for various imaging modalities (CT, MRI, and PET), and the use of registered multi-modality imaging datasets as a basis for radiomics. There are many challenges to the eventual use of radiomics-derived methods in clinical practice, including: standardization and robustness of selected metrics, accruing the data required, building and validating the resulting models, registering longitudinal data that often involve significant patient changes, reliable automated cancer segmentation tools, etc. Despite the hurdles, results achieved so far indicate the tremendous potential of this general approach to quantifying and using data from medical images. Specific applications of radiomics to be presented in this symposium will include: the longitudinal analysis of patients with low-grade gliomas; automatic detection and assessment of patients with metastatic bone lesions; image-based monitoring of patients with growing lymph nodes; predicting radiotherapy outcomes using multi-modality radiomics; and studies relating radiomics with genomics in lung cancer and glioblastoma. Learning Objectives: Understanding the basic image features that are often used in radiomic models. Understanding requirements for reliable radiomic models, including robustness of metrics, adequate predictive accuracy, and generalizability. Understanding the methodology behind radiomic-genomic (’radiogenomics’) correlations. Research supported by NIH (US), CIHR (Canada), and NSERC (Canada)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-G-303-01: Radiomics: Quantitative Imaging in the Service of Improved Treatment Decision Making

‘Radiomics’ refers to studies that extract a large amount of quantitative information from medical imaging studies as a basis for characterizing a specific aspect of patient health. Radiomics models can be built to address a wide range of outcome predictions, clinical decisions, basic cancer biology, etc. For example, radiomics models can be built to predict the aggressiveness of an imaged cancer, cancer gene expression characteristics (radiogenomics), radiation therapy treatment response, etc. Technically, radiomics brings together quantitative imaging, computer vision/image processing, and machine learning. In this symposium, speakers will discuss approaches to radiomics investigations, including: longitudinal radiomics, radiomics combined with other biomarkers (‘pan-omics’), radiomics for various imaging modalities (CT, MRI, and PET), and the use of registered multi-modality imaging datasets as a basis for radiomics. There are many challenges to the eventual use of radiomics-derived methods in clinical practice, including: standardization and robustness of selected metrics, accruing the data required, building and validating the resulting models, registering longitudinal data that often involve significant patient changes, reliable automated cancer segmentation tools, etc. Despite the hurdles, results achieved so far indicate the tremendous potential of this general approach to quantifying and using data from medical images. Specific applications of radiomics to be presented in this symposium will include: the longitudinal analysis of patients with low-grade gliomas; automatic detection and assessment of patients with metastatic bone lesions; image-based monitoring of patients with growing lymph nodes; predicting radiotherapy outcomes using multi-modality radiomics; and studies relating radiomics with genomics in lung cancer and glioblastoma. Learning Objectives: Understanding the basic image features that are often used in radiomic models. Understanding requirements for reliable radiomic models, including robustness of metrics, adequate predictive accuracy, and generalizability. Understanding the methodology behind radiomic-genomic (’radiogenomics’) correlations. Research supported by NIH (US), CIHR (Canada), and NSERC (Canada)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-G-303-02: Robust Radiomics Methods for PET and CT Imaging

‘Radiomics’ refers to studies that extract a large amount of quantitative information from medical imaging studies as a basis for characterizing a specific aspect of patient health. Radiomics models can be built to address a wide range of outcome predictions, clinical decisions, basic cancer biology, etc. For example, radiomics models can be built to predict the aggressiveness of an imaged cancer, cancer gene expression characteristics (radiogenomics), radiation therapy treatment response, etc. Technically, radiomics brings together quantitative imaging, computer vision/image processing, and machine learning. In this symposium, speakers will discuss approaches to radiomics investigations, including: longitudinal radiomics, radiomics combined with other biomarkers (‘pan-omics’), radiomics for various imaging modalities (CT, MRI, and PET), and the use of registered multi-modality imaging datasets as a basis for radiomics. There are many challenges to the eventual use of radiomics-derived methods in clinical practice, including: standardization and robustness of selected metrics, accruing the data required, building and validating the resulting models, registering longitudinal data that often involve significant patient changes, reliable automated cancer segmentation tools, etc. Despite the hurdles, results achieved so far indicate the tremendous potential of this general approach to quantifying and using data from medical images. Specific applications of radiomics to be presented in this symposium will include: the longitudinal analysis of patients with low-grade gliomas; automatic detection and assessment of patients with metastatic bone lesions; image-based monitoring of patients with growing lymph nodes; predicting radiotherapy outcomes using multi-modality radiomics; and studies relating radiomics with genomics in lung cancer and glioblastoma. Learning Objectives: Understanding the basic image features that are often used in radiomic models. Understanding requirements for reliable radiomic models, including robustness of metrics, adequate predictive accuracy, and generalizability. Understanding the methodology behind radiomic-genomic (’radiogenomics’) correlations. Research supported by NIH (US), CIHR (Canada), and NSERC (Canada)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-G-303-03: Machine Learning to Improve Human Learning From Longitudinal Image Sets

‘Radiomics’ refers to studies that extract a large amount of quantitative information from medical imaging studies as a basis for characterizing a specific aspect of patient health. Radiomics models can be built to address a wide range of outcome predictions, clinical decisions, basic cancer biology, etc. For example, radiomics models can be built to predict the aggressiveness of an imaged cancer, cancer gene expression characteristics (radiogenomics), radiation therapy treatment response, etc. Technically, radiomics brings together quantitative imaging, computer vision/image processing, and machine learning. In this symposium, speakers will discuss approaches to radiomics investigations, including: longitudinal radiomics, radiomics combined with other biomarkers (‘pan-omics’), radiomics for various imaging modalities (CT, MRI, and PET), and the use of registered multi-modality imaging datasets as a basis for radiomics. There are many challenges to the eventual use of radiomics-derived methods in clinical practice, including: standardization and robustness of selected metrics, accruing the data required, building and validating the resulting models, registering longitudinal data that often involve significant patient changes, reliable automated cancer segmentation tools, etc. Despite the hurdles, results achieved so far indicate the tremendous potential of this general approach to quantifying and using data from medical images. Specific applications of radiomics to be presented in this symposium will include: the longitudinal analysis of patients with low-grade gliomas; automatic detection and assessment of patients with metastatic bone lesions; image-based monitoring of patients with growing lymph nodes; predicting radiotherapy outcomes using multi-modality radiomics; and studies relating radiomics with genomics in lung cancer and glioblastoma. Learning Objectives: Understanding the basic image features that are often used in radiomic models. Understanding requirements for reliable radiomic models, including robustness of metrics, adequate predictive accuracy, and generalizability. Understanding the methodology behind radiomic-genomic (’radiogenomics’) correlations. Research supported by NIH (US), CIHR (Canada), and NSERC (Canada)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-G-303-04: Radiomics and the Coming Pan-Omics Revolution

‘Radiomics’ refers to studies that extract a large amount of quantitative information from medical imaging studies as a basis for characterizing a specific aspect of patient health. Radiomics models can be built to address a wide range of outcome predictions, clinical decisions, basic cancer biology, etc. For example, radiomics models can be built to predict the aggressiveness of an imaged cancer, cancer gene expression characteristics (radiogenomics), radiation therapy treatment response, etc. Technically, radiomics brings together quantitative imaging, computer vision/image processing, and machine learning. In this symposium, speakers will discuss approaches to radiomics investigations, including: longitudinal radiomics, radiomics combined with other biomarkers (‘pan-omics’), radiomics for various imaging modalities (CT, MRI, and PET), and the use of registered multi-modality imaging datasets as a basis for radiomics. There are many challenges to the eventual use of radiomics-derived methods in clinical practice, including: standardization and robustness of selected metrics, accruing the data required, building and validating the resulting models, registering longitudinal data that often involve significant patient changes, reliable automated cancer segmentation tools, etc. Despite the hurdles, results achieved so far indicate the tremendous potential of this general approach to quantifying and using data from medical images. Specific applications of radiomics to be presented in this symposium will include: the longitudinal analysis of patients with low-grade gliomas; automatic detection and assessment of patients with metastatic bone lesions; image-based monitoring of patients with growing lymph nodes; predicting radiotherapy outcomes using multi-modality radiomics; and studies relating radiomics with genomics in lung cancer and glioblastoma. Learning Objectives: Understanding the basic image features that are often used in radiomic models. Understanding requirements for reliable radiomic models, including robustness of metrics, adequate predictive accuracy, and generalizability. Understanding the methodology behind radiomic-genomic (’radiogenomics’) correlations. Research supported by NIH (US), CIHR (Canada), and NSERC (Canada)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Quantitative microstructural investigation of 3D-printed and cast cement pastes using micro-computed tomography and image analysis

Highlights: • Processing alters the microstructure in 3D-printed lamellar cubic element. • A unique patterned and connected pore microstructure network exists. • Micro-channels are connected through micro-pores present at interfaces regions. • Three distinct pore volume domains exists in hardened microstructure. • Volume and frequency of pore, hydrated, and unhydrated clusters are dissimilar. Microstructural phases and mechanical properties of lamellar 3D-printed and cast hardened cement paste (hcp) elements were investigated using a lab-based X-ray microscope at two levels of magnification (0.4× and 4×). K-means clustering was used for quantitative image analysis. The entire volume of intact 3-days-old 3D-printed and cast hcp elements was characterized at 0.4× magnification. Three microstructural features (macro-pores, micro-channels, and interfacial micro-pores) were found to reside in three distinct pore size domains. The largest pores of the 3D-printed element were larger than the largest pores of the reference cast hcp element. Moreover, the smallest pore sizes of the 3D-printed element were found to be smaller than those present in the cast counterparts. Micro-channels were found to be connected to one another through the micro-pores present at interfacial regions, indicating the presence of a uniquely patterned and interconnected pore network. The role of locally weak and porous interfaces on mechanical response and fracture properties is discussed.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Direct Imaging of Antiferromagnet-Ferromagnet Phase Transition in van der Waals Antiferromagnet CrSBr

The advent of van der Waals (vdW) ferromagnetic (FM) and antiferromagnetic (AFM) materials offers unprecedented opportunities for spintronics and magneto-optic devices. Combining magnetic Kerr microscopy and density functional theory calculations, the AFM-FM transition is investigated and a surprising abnormal magneto-optic anisotropy in vdW CrSBr associated with different magnetic phases (FM, AFM, or paramagnetic state) is discovered. This unique magneto-optic property leads to different anisotropic optical reflectivity from different magnetic states, permitting direct imaging of the AFM Néel vector orientation and the dynamic process of the AFM-FM transition within a magnetic field. Using Kerr microscopy, not only the domain nucleation and propagation process is imaged but also the intermediate spin-flop state in the AFM-FM transition is identified. In conclusion, the unique magneto-optic property and clear identification of the dynamics process of the AFM-FM phase transition in CrSBr demonstrate the promise of vdW magnetic materials for future spintronic technology.

36 MATERIALS SCIENCE↗

DOE OSTI2021

The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts

Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Methods and apparatuses for detection of radiation with semiconductor image sensors

A semiconductor image sensor is repeatedly exposed to high-energy photons while a visible light obstructer is in place to block visible light from impinging on the sensor to generate a set of images from the exposures. A composite image is generated from the set of images with common noise substantially removed so the composite image includes image information corresponding to radiated pixels that absorbed at least some energy from the high-energy photons. The composite image is processed to determine a set of bright points in the composite image, each bright point being above a first threshold. The set of bright points is processed to identify lines with two or more bright points that include pixels therebetween that are above a second threshold and identify a presence of the high-energy particles responsive to a number of lines.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

SNIa detection in the SNLS photometric analysis using Morphological Component Analysis

Detection of supernovae (SNe) and, more generally, of transient events in large surveys can provide numerous false detections. In the case of a deferred processing of survey images, this implies reconstructing complete light curves for all detections, requiring sizable processing time and resources. Optimizing the detection of transient events is thus an important issue for both present and future surveys. We present here the optimization done in the SuperNova Legacy Survey (SNLS) for the 5-year data deferred photometric analysis. In this analysis, detections are derived from stacks of subtracted images with one stack per lunation. The 3-year analysis provided 300,000 detections dominated by signals of bright objects that were not perfectly subtracted. Allowing these artifacts to be detected leads not only to a waste of resources but also to possible signal coordinate contamination. We developed a subtracted image stack treatment to reduce the number of non SN-like events using morphological component analysis. This technique exploits the morphological diversity of objects to be detected to extract the signal of interest. At the level of our subtraction stacks, SN-like events are rather circular objects while most spurious detections exhibit different shapes. A two-step procedure was necessary to have a proper evaluation of the noise in the subtracted image stacks and thus a reliable signal extraction. We also set up a new detection strategy to obtain coordinates with good resolution for the extracted signal. SNIa Monte-Carlo (MC) generated images were used to study detection efficiency and coordinate resolution. When tested on SNLS 3-year data this procedure decreases the number of detections by a factor of two, while losing only 10% of SN-like events, almost all faint ones. MC results show that SNIa detection efficiency is equivalent to that of the original method for bright events, while the coordinate resolution is improved.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Minimising damage in high resolution scanning transmission electron microscope images of nanoscale structures and processes

Determining the optimum electron dose distribution for damage mitigated scanning transmission electron microscopy imaging using subsampling and image inpainting.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Windowed Fourier transform and cross-correlation algorithms for molecular tagging velocimetry

In this study, simulated and experimental molecular tagging velocimetry (MTV) images have been analyzed with a technique commonly used to process grid images on surfaces, the windowed Fourier transform with local spectrum analysis (WFT-LSA). A systematic synthetic image study of the modulation transfer function (MTF) and error tendencies of the WFT-LSA was performed and compared with a PIV-style cross-correlation algorithm to see if advanced strategies such as iterative image deformation can improve analysis of gridded images with high noise levels. Testing of single-pass algorithms showed that in typical MTV images, the WFT-LSA yields significantly lower bias errors than cross-correlation (CC) at displacements greater than 1 pixel but slightly higher random error at all displacements and image conditions. Analysis of the MTF shows that CC provided better resolution of spatial fluctuations than the WFT-LSA in many combinations of grid size and interrogation window. Tests of image deformation algorithms showed that the gap in performance between CC and WFT-LSA is maintained even as both methods improve. Additionally, WFT-LSA and CC methods are applied to real MTV experiments in high-speed gas jet flows. A preliminary analysis of phosphorescence lifetime provided by acetone vapor excited at 266 nm is used for assessing the required gas speeds for making MTV application feasible. The application of WFT-LSA to real MTV images demonstrates the ability of the algorithm to handle further real-world effects that could not be considered in the synthetic image analysis, like reduced signal-to-noise ratio and non-uniform intensity of the tagging grid across the image introduced by the actual laser beam energy distribution. With experimental images, CC is more accurate with shear flows but less robust to high noise levels than WFT-LSA, as predicted by the synthetic image analysis.

42 ENGINEERING↗

DOE OSTI2015

SU-E-J-204: Can a Commercial System for MR-IGRT Be Used to Treat Patients Without Acquiring a CT Scan?

Patients treated using a magnetic-resonance image guided radiation therapy (MR-IGRT) system received both CT and MR simulations. During planning, the CT is used to determine relative electron density (RED) using a calibration table. This study aims to investigate the feasibility of MR-only treatments by comparing CT-computed dose distributions to those computed with combinations of water (1.0), lung (0.26), tissue (1.02), and bone (1.12) bulk RED overrides, and to identify the effects of the magnetic field on the RED-overridden doses. Methods: Four patients who received treatment using a commercial MR-IGRT system were analyzed (1 lung, 2 abdomen, and 1 pelvis). The clinical plans were computed using the first fraction MRI as primary, and the simulation CT as secondary for REDs. Plans were reoptimized using default bulk RED overrides (water/lung and tissue/lung for the lung patient, water/bone, tissue/bone, water only, and tissue only for the abdomen and pelvis patients). Additionally, each plan was re-optimized to include the static magnetic field. All plans were normalized to the same PTV coverage as the clinical plan. Dose-difference volumes and DVHs were computed for bulk density override plans, and 3D gamma analyses between each plan and its accompanying magnetic field plan were performed using 3%/3 mm dose difference and distance-to-agreement criteria using the PTV and Skin as masking structures. Results: The average differences in PTV and organs-at-risk mean dose for all RED combinations tested were −0.19 Gy (−0.62 – 0.06 Gy) and −0.34 Gy (−1.76 – 0.33 Gy), respectively. The average PTV and Skin gamma pass rates for all RED combinations tested were 99.88% (99.5% – 100%) and 98. 35% (96.3% – 99.6%). No systematic differences in DVHs or isodoses were observed. Conclusions: It is likely that that a commercial MR-IGRT system may produce high quality treatment plans without the need for CT scans. Authors of this abstract are members of the Washington University Radiation Oncology department, which has a research agreement with ViewRay, Inc.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

A 1.5 T transverse magnetic field in radiotherapy of rectal cancer: Impact on the dose distribution

Purpose: MRI guidance during radiotherapy has the potential to enable more accurate dose delivery, optimizing the balance between local control and treatment related toxicity. However, the presence of a permanent magnetic field influences the dose delivery, especially around air cavities. Here, electrons are able to return to the surface through which they entered the air cavity (electron return effect, ERE) locally resulting in dose hot- and cold-spots. Where RT of rectal cancer patients might benefit from MRI guidance for margin reduction, air cavities in and around the target volume are frequently present. The purpose of this research is to evaluate the impact of the presence of a 1.5 T transverse magnetic field on dose delivery in patients with rectal cancer. Methods: Ten patients treated with 5 × 5 Gy RT having large changes in pelvic air content were selected out of a cohort of 33 patients. On the planning CT, a 1.5 T, 6 MV, 7-field intensity modulated radiotherapy (IMRT) plan was created. This plan was subsequently recalculated on daily CT scans. For each daily CT, the CTV V{sub 95%} and V{sub 107%} and bowel area V{sub 5Gy}, V{sub 10Gy}, V{sub 15Gy}, V{sub 20Gy}, and V{sub 25Gy} were calculated to evaluate the changes in dose distribution from fraction to fraction. For comparison, the authors repeated this procedure for the 0 T situation. To study the effect of changing air cavities separate from other anatomical changes, the authors also generated artificial air cavities in the CTV of one patient (2 and 5 cm diameter), in the high dose gradient region (2 cm), and in the low dose area (2 cm). Treatment plans were optimized without and with each simulated air cavity. For appearing and disappearing air cavities, the CTV V{sub 95%} and V{sub 107%} were evaluated. The authors also evaluated the ERE separate from attenuation changes locally around appearing gas pockets. Results: For the ten patients, at 1.5 T, the V{sub 95%} was influenced by both appearing and disappearing air, and dropped to <98% in 2 out of 50 fractions due a disappearing air cavity of 150 cm{sup 3}. V{sub 95%} differences between 0 and 1.5 T were all within 2%. The V{sub 107%} was below 1% in 46 out of 50 fractions, and increased to 3% in the remaining fractions due to appearing air of around 120 cm{sup 3}. For comparison, V{sub 107%} was <1% at 0 T for all fractions. In the bowel area, the V{sub 15Gy} varied strongest from fraction to fraction, but differences between 1.5 and 0 T were minimal with an average difference of 2.3 cm{sup 3} (SD = 18.7 cm{sup 3}, p = 0.38). For the simulated air cavities, the ERE resulted in cold-spots maximally 5% lower than prescribed and hot-spots maximally 6% higher than prescribed. Conclusions: The presence of a 1.5 T magnetic field has an impact on the dose distribution when the air content changes of within a few percent in these selected rectal cancer patients. The authors consider this influence of the transverse magnetic field on the dose distribution in IMRT for rectal cancer patients clinically acceptable.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Atomic resolution scanning transmission electron microscopy at liquid helium temperatures for quantum materials

Fundamental quantum phenomena in condensed matter, ranging from correlated electron systems to quantum information processors, manifest their emergent characteristics and behaviors predominantly at low temperatures. This necessitates the use of liquid helium (LHe) cooling for experimental observation. Atomic resolution scanning transmission electron microscopy combined with LHe cooling (cryo-STEM) provides a powerful characterization technique to probe local atomic structural modulations and their coupling with charge, spin and orbital degrees-of-freedom in quantum materials. However, achieving atomic resolution in cryo-STEM is exceptionally challenging, primarily due to sample drifts arising from temperature changes and noises associated with LHe bubbling, turbulent gas flow, etc. In this work, we demonstrate atomic resolution cryo-STEM imaging at LHe temperatures using a commercial side-entry LHe cooling holder. Firstly, we examine STEM imaging performance as a function of He gas flow rate, identifying two primary noise sources: He-gas pulsing and He-gas bubbling. Secondly, we propose two strategies to achieve low noise conditions for atomic resolution STEM imaging: either by temporarily suppressing He gas flow rate using the needle valve or by acquiring images during the natural warming process. Lastly, we show the applications of image acquisition methods and image processing techniques in investigating structural phase transitions in Cr 2 Ge 2 Te 6 , CuIr 2 S 4 , and CrCl 3 . In conclusion, our findings represent an advance in the field of atomic resolution electron microscopy imaging for quantum materials and devices at LHe temperatures, which can be applied to other commercial side-entry LHe cooling TEM holders.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specifc contextual clues and requires no training. This approach generalizes across diferent modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal refectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fdelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufcient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively.

47 OTHER INSTRUMENTATION↗

DOE OSTI2018

Ultrafast X-ray imaging of laser–metal additive manufacturing processes

The high-speed synchrotron X-ray imaging technique was synchronized with a custom-built laser-melting setup to capture the dynamics of laser powder-bed fusion processes in situ . Various significant phenomena, including vapor-depression and melt-pool dynamics and powder-spatter ejection, were captured with high spatial and temporal resolution. Imaging frame rates of up to 10 MHz were used to capture the rapid changes in these highly dynamic phenomena. At the same time, relatively slow frame rates were employed to capture large-scale changes during the process. This experimental platform will be vital in the further understanding of laser additive manufacturing processes and will be particularly helpful in guiding efforts to reduce or eliminate microstructural defects in additively manufactured parts.

laser powder bed fusion↗