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At least 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

STOCHASTIC OPTICS: A SCATTERING MITIGATION FRAMEWORK FOR RADIO INTERFEROMETRIC IMAGING

Just as turbulence in the Earth’s atmosphere can severely limit the angular resolution of optical telescopes, turbulence in the ionized interstellar medium fundamentally limits the resolution of radio telescopes. We present a scattering mitigation framework for radio imaging with very long baseline interferometry (VLBI) that partially overcomes this limitation. Our framework, “stochastic optics,” derives from a simplification of strong interstellar scattering to separate small-scale (“diffractive”) effects from large-scale (“refractive”) effects, thereby separating deterministic and random contributions to the scattering. Stochastic optics extends traditional synthesis imaging by simultaneously reconstructing an unscattered image and its refractive perturbations. Its advantages over direct imaging come from utilizing the many deterministic properties of the scattering—such as the time-averaged “blurring,” polarization independence, and the deterministic evolution in frequency and time—while still accounting for the stochastic image distortions on large scales. These distortions are identified in the image reconstructions through regularization by their time-averaged power spectrum. Using synthetic data, we show that this framework effectively removes the blurring from diffractive scattering while reducing the spurious image features from refractive scattering. Stochastic optics can provide significant improvements over existing scattering mitigation strategies and is especially promising for imaging the Galactic Center supermassive black hole, Sagittarius A*, with the Global mm-VLBI Array and with the Event Horizon Telescope.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Single-Objective Airy Light-Sheet Imaging

Despite its massive potential, standard light-sheet imaging (LSI) faces key challenges, such as the incompatibility with common sample mounting techniques and low-resolution imaging. Single-objective LSI attempts to address these issues but often suffers from limited fields-of-view and throughput rates, or requires multiple optics that increase costs, alignment complexity, and losses. To overcome these challenges of standard single-objective LSI, we introduce single-objective Airy light-sheet imaging (SoALSI). SoALSI leverages the extraordinary self-acceleration properties of the Airy beam, achieving 5× higher imaging rates and enhanced imaging efficiency than standard single-objective LSI. Here, we demonstrate SoALSI’s versatility through rigorous contrast and resolution characterizations and by high-resolution imaging of diverse biological specimens, including malaria parasite-infected red blood cells and plant root tissue. SoALSI seamlessly integrates with any standard inverted microscope frame, enabling broader accessibility for the bioimaging community to explore biological processes in a wide range of specimens with enhanced resolution and imaging contrast.

airy beam↗
DOE OSTI2016

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty-animal model.” Our species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91%–94% on out-of-sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Magnetic resonance imaging features of pubic symphysis urinary fistula with pubic bone osteomyelitis in the treated prostate cancer patient

Introduction: Pubic bone osteomyelitis with pubic symphysis urinary fistula represents a debilitating complication of radiation and ablative treatments for prostate cancer. The definitive radiographic diagnosis of this clinical entity is not described. In this study, we characterize the plain film and magnetic resonance imaging findings of pubic osteomyelitis. Materials and Methods: We reviewed a database of prostate cancer survivors with diagnosed pubic osteomyelitis from 2011 to 2015. These patients underwent pelvic plain radiographs and magnetic resonance imaging with T1-weighted and fat-suppressed T2-weighted fast spin echo sequences. Intravenous gadolinium was utilized. The diagnosis was verified with extirpative surgery. 16 patients with diagnosed pubic osteomyelitis from 2011 to 2015 underwent imaging at our institution. Results: All patients demonstrated increased signal on T2- weighted sequences and decreased signal on T1-weighted sequences along the pubic symphysis and the marrow of the involved pubic rami. Inflammatory myositis with diastasis of the pubic symphysis and cortical bone erosion were identified in the majority of patients. Fluid collections were identified in 75% of patients. 63% of conventional radiographs demonstrated no radiographic evidence of pubic osteomyelitis. Conclusion: Magnetic resonance imaging of pubic symphysis osteomyelitis in the prostate cancer survivor is characterized by high signal on T2-weighted images and low signal on T1-weighted images of the involved pubic rami, with the majority of patients demonstrating regional myositis. Imaging data combined with clinical assessment should prompt diagnosis and management of pubic osteomyelitis. Conventional radiography is generally insensitive to these findings. We consider magnetic resonance imaging to be the definitive diagnostic modality for this clinical entity.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Image Registration of Cone-Beam Computer Tomography and Preprocedural Computer Tomography Aids in Localization of Adrenal Veins and Decreasing Radiation Dose in Adrenal Vein Sampling

PurposeWe assessed whether image registration of cone-beam computed tomography (CT) (CBCT) and contrast-enhanced CT (CE-CT) images indicating the locations of the adrenal veins can aid in increasing the success rate of first-attempts adrenal vein sampling (AVS) and therefore decreasing patient radiation dose.Materials and Methods CBCT scans were acquired in the interventional suite (Philips Allura Xper FD20) and rigidly registered to the vertebra in previously acquired CE-CT. Adrenal vein locations were marked on the CT image and superimposed with live fluoroscopy and digital-subtraction angiography (DSA) to guide the AVS. Seventeen first attempts at AVS were performed with image registration and retrospectively compared with 15 first attempts without image registration performed earlier by the same 2 interventional radiologists. First-attempt AVS was considered successful when both adrenal vein samples showed representative cortisol levels. Sampling time, dose-area product (DAP), number of DSA runs, fluoroscopy time, and skin dose were recorded.ResultsWithout image registration, the first attempt at sampling was successful in 8 of 15 procedures indicating a success rate of 53.3 %. This increased to 76.5 % (13 of 17) by adding CBCT and CE-CT image registration to AVS procedures (p = 0.266). DAP values (p = 0.001) and DSA runs (p = 0.026) decreased significantly by adding image registration guidance. Sampling and fluoroscopy times and skin dose showed no significant changes.ConclusionGuidance based on registration of CBCT and previously acquired diagnostic CE-CT can aid in enhancing localization of the adrenal veins thereby increasing the success rate of first-attempt AVS with a significant decrease in the number of used DSA runs and, consequently, radiation dose required.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Automated Segmentation and Quantification of Drusen in Fundus and Optical Coherence Tomography Images for Detection of ARMD

Age-related macular degeneration (ARMD) is one of the most common retinal syndromes that occurs in elderly people. Different eye testing techniques such as fundus photography and optical coherence tomography (OCT) are used to clinically examine the ARMD-affected patients. Many researchers have worked on detecting ARMD from fundus images, few of them also worked on detecting ARMD from OCT images. However, there are only few systems that establish the correspondence between fundus and OCT images to give an accurate prediction of ARMD pathology. In this paper, we present fully automated decision support system that can automatically detect ARMD by establishing correspondence between OCT and fundus imagery. The proposed system also distinguishes between early, suspect and confirmed ARMD by correlating OCT B-scans with respective region of the fundus image. In first phase, proposed system uses different B-scan based features along with support vector machine (SVM) to detect the presence of drusens and classify it as ARMD or normal case. In case input OCT scan is classified as ARMD, region of interest from corresponding fundus image is considered for further evaluation. The analysis of fundus image is performed using contrast enhancement and adaptive thresholding to detect possible drusens from fundus image and proposed system finally classified it as early stage ARMD or advance stage ARMD. The proposed system is tested on local data set of 100 patients with100 fundus images and 6800 OCT B-scans. Proposed system detects ARMD with the accuracy, sensitivity, and specificity ratings of 98.0, 100, and 97.14%, respectively.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Defect Detection of Industrial Radiography Images of Ammonia Pipes by a Sparse Coding Model

Pipeline transportation systems for liquid anhydrous ammonia require periodic inspections for pipe defects. Defects such as crack-like flaws, including those due to stress corrosion and fatigue crack growth and fatigue of welded joints, can be tested by in situ radiography testing (RT). It is required that the detection technique should reveal the defect region without any significant distortion. Imaging noise such as that due to radiation scattering reduces contrast over the whole or part of the image, causing a reduction in image quality. Various image processing methods can be utilized to improve the outcome of RT of pipes. The sparse coding model is a powerful contrast improvement algorithm, but it tends to eliminate imaging details, passing them off as noise. We present a novel implementation of the sparse coding model based on a probabilistic interpretation to improve defect detection in ammonia pipelines radiography images. The performance of this procedure was evaluated in radiographic images of defect and corrosion regions for different pipe types. For ammonia pipes, the technique was found to offer a significant improvement in defect detection while preserving imaging details.

42 ENGINEERING↗
DOE OSTI2019

The Role of Multimodality Imaging in Transcatheter Aortic Valve Replacement

Purpose of Review: Multimodality imaging is integral for diagnosis, procedural guidance, and follow-up of patients undergoing transcatheter aortic valve replacement (TAVR). In this review, we provide an overview of the role of each imaging modality and highlight technical considerations and pitfalls. We also address current controversies and new developments in the field. Recent Findings: Echocardiography remains the primary imaging modality for diagnosis of aortic stenosis and intraprocedural guidance for TAVR, but computed tomography (CT) imaging has supplanted echocardiography for annular sizing and access site evaluation. Magnetic resonance imaging (MRI) shows promise in targeted patient populations. Refined parameters and guidelines for valve sizing and paravalvular regurgitation have sought to standardize these complex assessments. Summary: Multimodality imaging remains critical to the success of TAVR, but its role has evolved over time. Understanding the applications, strengths, and limitations of each imaging modality is a crucial skill for the modern structural imager.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Automated bubble analysis of high-speed subcooled flow boiling images using U-net transfer learning and global optical flow

Capturing and analyzing the bubble dynamics is crucial to improving the understanding of boiling heat transfer mechanisms and predicting boiling heat transfer coefficient and boiling crisis. High speed video (HSV) imaging has been used for decades towards this end. Still, there is no universal approach to quantitatively analyze bubble dynamics from HSV images. In this study, we propose a data-driven post-processing approach to segment, track, and identify wall-attached vapor bubbles from HSV images of the boiling process in subcooled flow conditions. Firstly, we employ a transfer learning framework with a U-Net-based convolution neural network (CNN) architecture to detect and segment bubbles in HSV images of diverse contrast and surface texture using very little data (e.g., 10 images) for training. Then, we evaluate the trained CNN model with 100 ground-truth images, and the validation results show that the model accuracy and precision in detecting the optical footprint of bubbles are higher than 90%. Finally, we suggest a criterion to identify a condensing bubble based on the divergence of the bubble displacement, which is calculated from sequential segmented bubble images using a global optical flow code. Using this combination of machine learning and optical flow, we can identify nucleation sites and track the growth of bubbles nucleating at each site to quantify nucleation site density, nucleation frequency, and other fundamental boiling parameters. The proposed system is validated using results obtained on a special heater, which enables both infrared (IR) thermometry and HSV imaging on a metallic surface. We compare the fundamental boiling parameters obtained by the two different diagnostics. The results show good agreement. In conclusion, the difference between the measurements of nucleation site density, averaged nucleation frequency, and averaged growth time performed with the two techniques is always within ± 20% and mostly ± 10% of the values measured with IR thermometry.

42 ENGINEERING↗
DOE OSTI2015

Combined Ventilation and Perfusion Imaging Correlates With the Dosimetric Parameters of Radiation Pneumonitis in Radiation Therapy Planning for Lung Cancer

Purpose: The purpose of this study was to prospectively investigate clinical correlations between dosimetric parameters associated with radiation pneumonitis (RP) and functional lung imaging. Methods and Materials: Functional lung imaging was performed using four-dimensional computed tomography (4D-CT) for ventilation imaging, single-photon emission computed tomography (SPECT) for perfusion imaging, or both (V/Q-matched region). Using 4D-CT, ventilation imaging was derived from a low attenuation area according to CT numbers below different thresholds (vent-860 and -910). Perfusion imaging at the 10th, 30th, 50th, and 70th percentile perfusion levels (F10-F70) were defined as the top 10%, 30%, 50%, and 70% hyperperfused normal lung, respectively. All imaging data were incorporated into a 3D planning system to evaluate correlations between RP dosimetric parameters (where fV20 is the percentage of functional lung volume irradiated with >20 Gy, or fMLD, the mean dose administered to functional lung) and the percentage of functional lung volume. Radiation pneumonitis was evaluated using Common Terminology Criteria for Adverse Events version 4.0. Statistical significance was defined as a P value of <.05. Results: Sixty patients who underwent curative radiation therapy were enrolled (48 patients for non-small cell lung cancer, and 12 patients for small cell lung cancer). Grades 1, 2, and ≥3 RP were observed in 16, 44, and 6 patients, respectively. Significant correlations were observed between the percentage of functional lung volume and fV20 (r=0.4475 in vent-860 and 0.3508 in F30) or fMLD (r=0.4701 in vent-860 and 0.3128 in F30) in patients with grade ≥2 RP. F30∩vent-860 results exhibited stronger correlations with fV20 and fMLD in patients with grade ≥2 (r=0.5509 in fV20 and 0.5320 in fMLD) and grade ≥3 RP (r=0.8770 in fV20 and 0.8518 in fMLD). Conclusions: RP dosimetric parameters correlated significantly with functional lung imaging.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Imaging and Data Acquisition in Clinical Trials for Radiation Therapy

Cancer treatment evolves through oncology clinical trials. Cancer trials are multimodal and complex. Assuring high-quality data are available to answer not only study objectives but also questions not anticipated at study initiation is the role of quality assurance. The National Cancer Institute reorganized its cancer clinical trials program in 2014. The National Clinical Trials Network (NCTN) was formed and within it was established a Diagnostic Imaging and Radiation Therapy Quality Assurance Organization. This organization is Imaging and Radiation Oncology Core, the Imaging and Radiation Oncology Core Group, consisting of 6 quality assurance centers that provide imaging and radiation therapy quality assurance for the NCTN. Sophisticated imaging is used for cancer diagnosis, treatment, and management as well as for image-driven technologies to plan and execute radiation treatment. Integration of imaging and radiation oncology data acquisition, review, management, and archive strategies are essential for trial compliance and future research. Lessons learned from previous trials are and provide evidence to support diagnostic imaging and radiation therapy data acquisition in NCTN trials.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Strategy of Using Intratreatment Hypoxia Imaging to Selectively and Safely Guide Radiation Dose De-escalation Concurrent With Chemotherapy for Locoregionally Advanced Human Papillomavirus–Related Oropharyngeal Carcinoma

Purpose: To report a small substudy of an ongoing large, multi-arm study using functional imaging to assess pre-/intratreatment hypoxia for all head and neck cancer, in which we hypothesized that pre- and early-treatment hypoxia assessment using functional positron emission tomography (PET) imaging may help select which human papillomavirus (HPV)-positive (HPV{sup +}) oropharyngeal cancer (OPC) patients can safely receive radiation de-escalation without jeopardizing treatment outcomes. Methods and Materials: Patients with HPV{sup +} oropharyngeal carcinoma were enrolled on an institutional review board–approved prospective study of which de-escalation based on imaging response was done for node(s) only. Pretreatment {sup 18}F-fluorodeoxyglucose and dynamic {sup 18}F-FMISO (fluoromisonidazole) positron emission tomography (PET) scans were performed. For patients with pretreatment hypoxia on{sup 18}F-FMISO PET (defined as a >1.2 tumor to muscle standard uptake value ratio), a repeat scan was done 1 week after chemoradiation. Patients without pretreatment hypoxia or with resolution of hypoxia on repeat scan received a 10-Gy dose reduction to metastatic lymph node(s). The 2-year local, regional, distant metastasis–free, and overall survival rates were estimated using the Kaplan-Meier product-limit method. A subset of patients had biopsy of a hypoxic node done under image guidance. Results: Thirty-three HPV{sup +} OPC patients were enrolled in this pilot study. One hundred percent showed pretreatment hypoxia (at primary site and/or node[s]), and among these, 48% resolved (at primary site and/or node[s]); 30% met criteria and received 10-Gy reduction to the lymph node(s). At the median follow-up of 32 months (range, 21-61 months), the 2-year locoregional control rate was 100%. One patient failed distantly with persistence of hypoxia on {sup 18}F-FMISO PET. The 2-year distant metastasis–free rate was 97%. The 2-year OS rate was 100%. Hypoxia on imaging was confirmed pathologically. Conclusions: Hypoxia is present in HPV{sup +} tumors but resolves within 1 week of treatment in 48% of cases either at the primary site and/or lymph node(s). Our 100% locoregional control rate suggests that intratreatment functional imaging used to selectively de-escalate node(s) to 60 Gy was confirmed safe using our stringent imaging criteria. Intratreatment functional imaging warrants further study to determine its ultimate role in de-escalation treatment strategies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction

Micrograph quantification is an essential component of several materials science studies. Machine learning methods, in particular convolutional neural networks, have previously demonstrated performance in image recognition tasks across several disciplines (e.g. materials science, medical imaging, facial recognition). Here, we apply these well-established methods to develop an approach to microstructure quantification for kinetic modeling of a discontinuous precipitation reaction in a case study on the uranium-molybdenum system. Prediction of material processing history based on image data (classification), calculation of area fraction of phases present in the micrographs (segmentation), and kinetic modeling from segmentation results were performed. Results indicate that convolutional neural networks represent microstructure image data well, and segmentation using the k-means clustering algorithm yields results that agree well with manually annotated images. Classification accuracies of original and segmented images are both 94% for a 5-class classification problem. Kinetic modeling results agree well with previously reported data using manual thresholding. The image quantification and kinetic modeling approach developed and presented here aims to reduce researcher bias introduced into the characterization process, and allows for leveraging information in limited image data sets.

36 MATERIALS SCIENCE↗
DOE OSTI2014

The iQID Camera: An Ionizing-Radiation Quantum Imaging Detector

We have developed and tested a novel, ionizing-radiation Quantum Imaging Detector (iQID). This scintillation-based detector was originally developed as a high-resolution gamma-ray imager, called BazookaSPECT, for use in single-photon emission computed tomography (SPECT). Recently, we have investigated the detectors response and imaging potential with other forms of ionizing radiation including alpha, neutron, beta, and fission fragment particles. The detector’s response to a broad range of ionizing radiation has prompted its new title. The principle operation of the iQID camera involves coupling a scintillator to an image intensifier. The scintillation light generated particle interactions is optically amplified by the intensifier and then re-imaged onto a CCD/CMOS camera sensor. The intensifier provides sufficient optical gain that practically any CCD/CMOS camera can be used to image ionizing radiation. Individual particles are identified and their spatial position (to sub-pixel accuracy) and energy are estimated on an event-by-event basis in real time using image analysis algorithms on high-performance graphics processing hardware. Distinguishing features of the iQID camera include portability, large active areas, high sensitivity, and high spatial resolution (tens of microns). Although modest, iQID has energy resolution that is sufficient to discrimate between particles. Additionally, spatial features of individual events can be used for particle discrimination. An important iQID imaging application that has recently been developed is single-particle, real-time digital autoradiography. In conclusion, we present the latest results and discuss potential applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Automated defect identification in electroluminescence images of solar modules

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. Here, we develop a rapid automatic computer vision pipeline (~0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

14 SOLAR ENERGY↗
DOE OSTI2017

Quantitative comparison of bright field and annular bright field imaging modes for characterization of oxygen octahedral tilts

Octahedral tilt behavior is increasingly recognized as an important contributing factor to the physical behavior of perovskite oxide materials and especially their interfaces, necessitating the development of high-resolution methods of tilt mapping. There are currently two major approaches for quantitative imaging of tilts in scanning transmission electron microscopy (STEM), bright field (BF) and annular bright field (ABF). In this study, we show that BF STEM can be reliably used for measurements of oxygen octahedral tilts. While optimal conditions for BF imaging are more restricted with respect to sample thickness and defocus, we find that BF imaging with an aberration-corrected microscope with the accelerating voltage of 300 kV gives us the most accurate quantitative measurement of the oxygen column positions. Using the tilted perovskite structure of BiFeO 3 (BFO) as our test sample, we simulate BF and ABF images in a wide range of conditions, identifying the optimal imaging conditions for each mode. Finally, we show that unlike ABF imaging, BF imaging remains directly quantitatively interpretable for a wide range of the specimen mistilt, suggesting that it should be preferable to the ABF STEM imaging for quantitative structure determination.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Constant-Distance Mode Nanospray Desorption Electrospray Ionization Mass Spectrometry Imaging of Biological Samples with Complex Topography

A new approach for constant distance mode mass spectrometry imaging of biological samples using nanospray desorption electrospray ionization (nano-DESI MSI) was developed by integrating a shear-force probe with nano-DESI probe. The technical concept and basic instrumental setup as well as general operation of the system are described. Mechanical dampening of resonant oscillations due to the presence of shear forces between the probe and the sample surface enables constant-distance imaging mode via a computer controlled closed feedback loop. The capability of simultaneous chemical and topographic imaging of complex biological samples is demonstrated using living Bacillus Subtilis ATCC 49760 colonies on agar plates. The constant-distance mode nano-DESI MSI enabled imaging of many metabolites including non-ribosomal peptides (surfactin, plipastatin and iturin) and iron-bound heme on the surface of living bacterial colonies ranging in diameter from 10 mm to 13 mm with height variations of up to 0.8 mm above the agar plate. Co-registration of ion images to topographic images provided higher-contrast images. Constant-mode nano-DESI MSI is ideally suited for imaging biological samples of complex topography in their native state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗