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Iori G, Alzu’bi M, Abbadi A, Al Momani Y, Hasoneh AR, Van Vaerenbergh P, Cudin I, Marcos J, Ahmad A, Mohammad A, Matalgah S, Foudeh I, Al Najdawi M, Amro A, Ur Rehman A, Abugharbiyeh M, Khrais R, Aljadaa A, Nour M, Al Mohammad H, Al Omari F, Salama M, García Fusté MJ, Reyes-Herrera J, Morawe C, Attal M, Kasaei S, Chrysostomou C, Kołodziej T, Boruchowski M, Nowak P, Wiechecki J, Fatima A, Ghigo A, Wawrzyniak AI, Lorentz K, Paolucci G, Lehner F, Krisch M, Stampanoni M, Rack A, Kaprolat A, Lausi A. BEATS: BEAmline for synchrotron X-ray microTomography at SESAME. JOURNAL OF SYNCHROTRON RADIATION 2024; 31:1358-1372. [PMID: 39007825 PMCID: PMC11371053 DOI: 10.1107/s1600577524005277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 06/04/2024] [Indexed: 07/16/2024]
Abstract
The ID10 beamline of the SESAME (Synchrotron-light for Experimental Science and Applications in the Middle East) synchrotron light source in Jordan was inaugurated in June 2023 and is now open to scientific users. The beamline, which was designed and installed within the European Horizon 2020 project BEAmline for Tomography at SESAME (BEATS), provides full-field X-ray radiography and microtomography imaging with monochromatic or polychromatic X-rays up to photon energies of 100 keV. The photon source generated by a 2.9 T wavelength shifter with variable gap, and a double-multilayer monochromator system allow versatile application for experiments requiring either an X-ray beam with high intensity and flux, and/or a partially spatial coherent beam for phase-contrast applications. Sample manipulation and X-ray detection systems are designed to allow scanning samples with different size, weight and material, providing image voxel sizes from 13 µm down to 0.33 µm. A state-of-the-art computing infrastructure for data collection, three-dimensional (3D) image reconstruction and data analysis allows the visualization and exploration of results online within a few seconds from the completion of a scan. Insights from 3D X-ray imaging are key to the investigation of specimens from archaeology and cultural heritage, biology and health sciences, materials science and engineering, earth, environmental sciences and more. Microtomography scans and preliminary results obtained at the beamline demonstrate that the new beamline ID10-BEATS expands significantly the range of scientific applications that can be targeted at SESAME.
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Affiliation(s)
- Gianluca Iori
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | - Mustafa Alzu’bi
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | - Anas Abbadi
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | - Yazeed Al Momani
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | - Abdel Rahman Hasoneh
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | | | - Ivan Cudin
- Elettra-Sincrotrone Trieste SCpA, Basovizza, Trieste, Italy
| | | | - Abdalla Ahmad
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | - Anas Mohammad
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | - Salman Matalgah
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | - Ibrahim Foudeh
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | - Mohammad Al Najdawi
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | - Adel Amro
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | - Abid Ur Rehman
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | - Mohammad Abugharbiyeh
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | - Rami Khrais
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | - Amro Aljadaa
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | - Mohammad Nour
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | - Hussam Al Mohammad
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | - Farouq Al Omari
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | - Majeda Salama
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | | | | | | | - Maher Attal
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | - Samira Kasaei
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
| | | | - Tomasz Kołodziej
- Solaris National Synchrotron Radiation CentreJagiellonian UniversityKrakowPoland
| | - Mateusz Boruchowski
- Solaris National Synchrotron Radiation CentreJagiellonian UniversityKrakowPoland
| | - Paweł Nowak
- Solaris National Synchrotron Radiation CentreJagiellonian UniversityKrakowPoland
| | - Jarosław Wiechecki
- Solaris National Synchrotron Radiation CentreJagiellonian UniversityKrakowPoland
| | | | - Andrea Ghigo
- Laboratori Nazionali di Frascati dell’INFNINFNFrascatiRomeItaly
| | | | | | | | - Frank Lehner
- Deutsches Elektronen-Synchrotron DESYHamburgGermany
| | | | | | | | | | - Andrea Lausi
- SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan
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Welborn SS, Preefer MB, Nelson Weker J. TomoPyUI: a user-friendly tool for rapid tomography alignment and reconstruction. JOURNAL OF SYNCHROTRON RADIATION 2024; 31:979-986. [PMID: 38920267 PMCID: PMC11226142 DOI: 10.1107/s1600577524003989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 05/01/2024] [Indexed: 06/27/2024]
Abstract
The management and processing of synchrotron and neutron computed tomography data can be a complex, labor-intensive and unstructured process. Users devote substantial time to both manually processing their data (i.e. organizing data/metadata, applying image filters etc.) and waiting for the computation of iterative alignment and reconstruction algorithms to finish. In this work, we present a solution to these problems: TomoPyUI, a user interface for the well known tomography data processing package TomoPy. This highly visual Python software package guides the user through the tomography processing pipeline from data import, preprocessing, alignment and finally to 3D volume reconstruction. The TomoPyUI systematic intermediate data and metadata storage system improves organization, and the inspection and manipulation tools (built within the application) help to avoid interrupted workflows. Notably, TomoPyUI operates entirely within a Jupyter environment. Herein, we provide a summary of these key features of TomoPyUI, along with an overview of the tomography processing pipeline, a discussion of the landscape of existing tomography processing software and the purpose of TomoPyUI, and a demonstration of its capabilities for real tomography data collected at SSRL beamline 6-2c.
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Affiliation(s)
- Samuel S. Welborn
- Department of Materials Science and EngineeringUniversity of PennsylvaniaPhiladelphiaPA19104USA
- Stanford Synchrotron Radiation LightsourceSLAC National Accelerator LaboratoryMenlo ParkCA94025USA
| | - Molleigh B. Preefer
- Stanford Synchrotron Radiation LightsourceSLAC National Accelerator LaboratoryMenlo ParkCA94025USA
| | - Johanna Nelson Weker
- Stanford Synchrotron Radiation LightsourceSLAC National Accelerator LaboratoryMenlo ParkCA94025USA
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Iori G, Foudeh I, Alzu’bi M, Al Mohammad M, Matalgah S. Alrecon: computed tomography reconstruction web application based on Solara. OPEN RESEARCH EUROPE 2024; 4:54. [PMID: 38779342 PMCID: PMC11109687 DOI: 10.12688/openreseurope.16863.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/21/2024] [Indexed: 05/25/2024]
Abstract
Synchrotron X-ray computed tomography is a non-destructive 3D imaging technique that offers the possibility to study the internal microstructure of samples with high spatial and temporal resolution. Given its unmatched image quality and acquisition speed, and the possibility to preserve the specimens, there is an increasing demand for this technique, from scientific users from innumerable disciplines. Computed tomography reconstruction is the computational process by which experimental radiographs are converted to a meaningful 3-dimensional image after the scan. The procedure involves pre-processing steps for image background and artifact correction on raw data, a reconstruction step approximating the inverse Radon-transform, and writing of the reconstructed volume image to disk. Several open-source Python packages exist to help scientists in the process of tomography reconstruction, by offering efficient implementations of reconstruction algorithms exploiting central or graphics processing unit (CPU and GPU, respectively), and by automating significant portions of the data processing pipeline. A further increase in productivity is attained by scheduling and parallelizing demanding reconstructions on high performance computing (HPC) clusters. Nevertheless, visual inspection and interactive selection of optimal reconstruction parameters remain crucial steps that are often performed in close interaction with the end-user of the data. As a result, the reconstruction task involves more than one software. Graphical user interfaces are provided to the user for fast inspection and optimization of reconstructions, while HPC resources are often accessed through scripts and command line interface. We propose Alrecon, a pure Python web application for tomographic reconstruction built using Solara. Alrecon offers users an intuitive and reactive environment for exploring data and customizing reconstruction pipelines. By leveraging upon popular 3D image visualization tools, and by providing a user-friendly interface for reconstruction scheduling on HPC resources, Alrecon guarantees productivity and efficient use of resources for any type of beamline user.
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Affiliation(s)
- Gianluca Iori
- SESAME - Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, 19252, Jordan
| | - Ibrahim Foudeh
- SESAME - Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, 19252, Jordan
| | - Mustafa Alzu’bi
- SESAME - Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, 19252, Jordan
| | - Malik Al Mohammad
- SESAME - Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, 19252, Jordan
| | - Salman Matalgah
- SESAME - Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, 19252, Jordan
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Li D, Zhang Z. MetaQA: Enhancing human-centered data search using Generative Pre-trained Transformer (GPT) language model and artificial intelligence. PLoS One 2023; 18:e0293034. [PMID: 37956160 PMCID: PMC10642800 DOI: 10.1371/journal.pone.0293034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 10/03/2023] [Indexed: 11/15/2023] Open
Abstract
Accessing and utilizing geospatial data from various sources is essential for developing scientific research to address complex scientific and societal challenges that require interdisciplinary knowledge. The traditional keyword-based geosearch approach is insufficient due to the uncertainty inherent within spatial information and how it is presented in the data-sharing platform. For instance, the Gulf of Mexico Coastal Ocean Observing System (GCOOS) data search platform stores geoinformation and metadata in a complex tabular. Users can search for data by entering keywords or selecting data from a drop-down manual from the user interface. However, the search results provide limited information about the data product, where detailed descriptions, potential use, and relationship with other data products are still missing. Language models (LMs) have demonstrated great potential in tasks like question answering, sentiment analysis, text classification, and machine translation. However, they struggle when dealing with metadata represented in tabular format. To overcome these challenges, we developed Meta Question Answering System (MetaQA), a novel spatial data search model. MetaQA integrates end-to-end AI models with a generative pre-trained transformer (GPT) to enhance geosearch services. Using GCOOS metadata as a case study, we tested the effectiveness of MetaQA. The results revealed that MetaQA outperforms state-of-the-art question-answering models in handling tabular metadata, underlining its potential for user-inspired geosearch services.
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Affiliation(s)
- Diya Li
- Department of Geography, Texas A&M University, College Station, Texas, United States of America
| | - Zhe Zhang
- Department of Geography, Texas A&M University, College Station, Texas, United States of America
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, United States of America
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Quenum J, Zenyuk IV, Ushizima D. Lithium Metal Battery Quality Control via Transformer-CNN Segmentation. J Imaging 2023; 9:111. [PMID: 37367459 DOI: 10.3390/jimaging9060111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/27/2023] [Accepted: 05/29/2023] [Indexed: 06/28/2023] Open
Abstract
Lithium metal battery (LMB) has the potential to be the next-generation battery system because of its high theoretical energy density. However, defects known as dendrites are formed by heterogeneous lithium (Li) plating, which hinders the development and utilization of LMBs. Non-destructive techniques to observe the dendrite morphology often use X-ray computed tomography (XCT) to provide cross-sectional views. To retrieve three-dimensional structures inside a battery, image segmentation becomes essential to quantitatively analyze XCT images. This work proposes a new semantic segmentation approach using a transformer-based neural network called TransforCNN that is capable of segmenting out dendrites from XCT data. In addition, we compare the performance of the proposed TransforCNN with three other algorithms, U-Net, Y-Net, and E-Net, consisting of an ensemble network model for XCT analysis. Our results show the advantages of using TransforCNN when evaluating over-segmentation metrics, such as mean intersection over union (mIoU) and mean Dice similarity coefficient (mDSC), as well as through several qualitatively comparative visualizations.
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Affiliation(s)
- Jerome Quenum
- Department of Electrical Engineering and Computer Science, Berkeley College of Engineering, University of California, Berkeley, CA 94720, USA
- Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Iryna V Zenyuk
- Department of Chemical & Biomolecular Engineering, National Fuel Cell Research Center, University of California Irvine, Irvine, CA 92697, USA
| | - Daniela Ushizima
- Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Berkeley Institute for Data Science, University of California Berkeley, Berkeley, CA 94720, USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA 94143, USA
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6
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Yuan K, Starchenko V, Rampal N, Yang F, Xiao X, Stack AG. Assessing an aqueous flow cell designed for in situ crystal growth under X-ray nanotomography and effects of radiolysis products. JOURNAL OF SYNCHROTRON RADIATION 2023; 30:634-642. [PMID: 37067259 PMCID: PMC10161885 DOI: 10.1107/s1600577523002783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 03/23/2023] [Indexed: 05/06/2023]
Abstract
Nucleation and growth of minerals has broad implications in the geological, environmental and materials sciences. Recent developments in fast X-ray nanotomography have enabled imaging of crystal growth in solutions in situ with a resolution of tens of nanometres, far surpassing optical microscopy. Here, a low-cost, custom-designed aqueous flow cell dedicated to the study of heterogeneous nucleation and growth of minerals in aqueous environments is shown. To gauge the effects of radiation damage from the imaging process on growth reactions, radiation-induced morphological changes of barite crystals (hundreds of nanometres to ∼1 µm) that were pre-deposited on the wall of the flow cell were investigated. Under flowing solution, minor to major crystal dissolution was observed when the tomography scan frequency was increased from every 30 min to every 5 min (with a 1 min scan duration). The production of reactive radicals from X-ray induced water radiolysis and decrease of pH close to the surface of barite are likely responsible for the observed dissolution. The flow cell shown here can possibly be adopted to study a wide range of other chemical reactions in solutions beyond crystal nucleation and growth where the combination of fast flow and fast scan can be used to mitigate the radiation effects.
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Affiliation(s)
- Ke Yuan
- Chemical Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Vitalii Starchenko
- Chemical Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Nikhil Rampal
- Chemical Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Department of Chemical Engineering, Columbia University, NY 10027, USA
| | - Fengchang Yang
- Chemical Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Xianghui Xiao
- National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Andrew G. Stack
- Chemical Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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7
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Khedekar K, Satjaritanun P, Stewart S, Braaten J, Atanassov P, Tamura N, Cheng L, Johnston CM, Zenyuk IV. Effect of Commercial Gas Diffusion Layers on Catalyst Durability of Polymer Electrolyte Fuel Cells in Varied Cathode Gas Environment. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2201750. [PMID: 35871500 DOI: 10.1002/smll.202201750] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 07/04/2022] [Indexed: 06/15/2023]
Abstract
Gas diffusion layers (GDLs) play a crucial role in heat transfer and water management of cathode catalyst layers in polymer electrolyte fuel cells (PEFCs). Thermal and water gradients can accelerate electrocatalyst degradation and therefore the selection of GDLs can have a major influence on PEFC durability. Currently, the role of GDLs in electrocatalyst degradation is poorly studied. In this study, electrocatalyst accelerated stress test studies are performed on membrane electrode assemblies (MEAs) prepared using three most commonly used GDLs. The effect of GDLs on electrocatalyst degradation is evaluated in both nitrogen (non-reactive) and air (reactive) gas environments at 100% relative humidity. In situ electrochemical characterization and extensive physical characterization is performed to understand the subtle differences in electrocatalyst degradation and correlated to the use of different GDLs. Overall, no difference is observed in the electrocatalyst degradation due to GDLs based on polarization curves at the end of life. But interestingly, MEA with a cracked microporous layer (MPL) in the GDL exhibited a higher electrocatalyst loading loss, which resulted in a lower and more heterogeneous increase in the average electrocatalyst nanoparticle size.
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Affiliation(s)
- Kaustubh Khedekar
- Department of Material Science and Engineering; National Fuel Cell Research Center, University of California, Irvine, CA, 92697, USA
| | - Pongsarun Satjaritanun
- Department of Chemical and Biomolecular Engineering; National Fuel Cell Research Center, University of California, Irvine, CA, 92697, USA
| | - Sarah Stewart
- Bosch Research and Technology Center North America, Sunnyvale, CA, 94085, USA
| | - Jonathan Braaten
- Bosch Research and Technology Center North America, Sunnyvale, CA, 94085, USA
| | - Plamen Atanassov
- Department of Material Science and Engineering; National Fuel Cell Research Center, University of California, Irvine, CA, 92697, USA
- Department of Chemical and Biomolecular Engineering; National Fuel Cell Research Center, University of California, Irvine, CA, 92697, USA
| | - Nobumichi Tamura
- Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Lei Cheng
- Bosch Research and Technology Center North America, Sunnyvale, CA, 94085, USA
| | | | - Iryna V Zenyuk
- Department of Material Science and Engineering; National Fuel Cell Research Center, University of California, Irvine, CA, 92697, USA
- Department of Chemical and Biomolecular Engineering; National Fuel Cell Research Center, University of California, Irvine, CA, 92697, USA
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8
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Chu M, Li J, Zhang Q, Jiang Z, Dufresne EM, Sandy A, Narayanan S, Schwarz N. pyXPCSviewer: an open-source interactive tool for X-ray photon correlation spectroscopy visualization and analysis. JOURNAL OF SYNCHROTRON RADIATION 2022; 29:1122-1129. [PMID: 35787580 PMCID: PMC9255579 DOI: 10.1107/s1600577522004830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 05/05/2022] [Indexed: 06/15/2023]
Abstract
pyXPCSviewer, a Python-based graphical user interface that is deployed at beamline 8-ID-I of the Advanced Photon Source for interactive visualization of XPCS results, is introduced. pyXPCSviewer parses rich X-ray photon correlation spectroscopy (XPCS) results into independent PyQt widgets that are both interactive and easy to maintain. pyXPCSviewer is open-source and is open to customization by the XPCS community for ingestion of diversified data structures and inclusion of novel XPCS techniques, both of which are growing demands particularly with the dawn of near-diffraction-limited synchrotron sources and their dedicated XPCS beamlines.
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Affiliation(s)
- Miaoqi Chu
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
| | - Jeffrey Li
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
| | - Qingteng Zhang
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
| | - Zhang Jiang
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
| | - Eric M. Dufresne
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
| | - Alec Sandy
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
| | - Suresh Narayanan
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
| | - Nicholas Schwarz
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
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9
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Thies M, Wagner F, Huang Y, Gu M, Kling L, Pechmann S, Aust O, Grüneboom A, Schett G, Christiansen S, Maier A. Calibration by differentiation - Self-supervised calibration for X-ray microscopy using a differentiable cone-beam reconstruction operator. J Microsc 2022; 287:81-92. [PMID: 35638174 DOI: 10.1111/jmi.13125] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 04/20/2022] [Accepted: 05/22/2022] [Indexed: 11/28/2022]
Abstract
High-resolution X-ray microscopy (XRM) is gaining interest for biological investigations of extremely small-scale structures. XRM imaging of bones in living mice could provide new insights into the emergence and treatment of osteoporosis by observing osteocyte lacunae, which are holes in the bone of few micrometers in size. Imaging living animals at that resolution, however, is extremely challenging and requires very sophisticated data processing converting the raw XRM detector output into reconstructed images. This paper presents an open-source, differentiable reconstruction pipeline for XRM data which analytically computes the final image from the raw measurements. In contrast to most proprietary reconstruction software, it offers the user full control over each processing step and, additionally, makes the entire pipeline deep learning compatible by ensuring differentiability. This allows fitting trainable modules both before and after the actual reconstruction step in a purely data-driven way using the gradient-based optimizers of common deep learning frameworks. The value of such differentiability is demonstrated by calibrating the parameters of a simple cupping correction module operating on the raw projection images using only a self-supervisory quality metric based on the reconstructed volume and no further calibration measurements. The retrospective calibration directly improves image quality as it avoids cupping artifacts and decreases the difference in gray values between outer and inner bone by 68% to 94%. Furthermore, it makes the reconstruction process entirely independent of the XRM manufacturer and paves the way to explore modern deep learning reconstruction methods for arbitrary XRM and, potentially, other flat-panel CT systems. This exemplifies how differentiable reconstruction can be leveraged in the context of XRM and, hence, is an important step toward the goal of reducing the resolution limit of in-vivo bone imaging to the single micrometer domain. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Mareike Thies
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Fabian Wagner
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Yixing Huang
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Mingxuan Gu
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Lasse Kling
- Institute for Nanotechnology and Correlative Microscopy e.V. INAM, Forchheim, Germany
| | - Sabrina Pechmann
- Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Forchheim, Germany
| | - Oliver Aust
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Anika Grüneboom
- Leibniz Institute for Analytical Sciences ISAS, Dortmund, Germany
| | - Georg Schett
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany.,Deutsches Zentrum für Immuntherapie, Friedrich-Alexander-Universität Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Silke Christiansen
- Institute for Nanotechnology and Correlative Microscopy e.V. INAM, Forchheim, Germany.,Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Forchheim, Germany.,Physics Department, Freie Universität Berlin, Berlin, Germany
| | - Andreas Maier
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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10
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Nikitin V, Tekawade A, Duchkov A, Shevchenko P, De Carlo F. Real-time streaming tomographic reconstruction with on-demand data capturing and 3D zooming to regions of interest. JOURNAL OF SYNCHROTRON RADIATION 2022; 29:816-828. [PMID: 35511014 PMCID: PMC9070713 DOI: 10.1107/s1600577522003095] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 03/22/2022] [Indexed: 06/14/2023]
Abstract
Complex dynamic tomographic experiments at brilliant X-ray light sources require real-time feedback on the sample changes with respect to environmental conditions, selecting representative regions of interest for high-resolution scanning, and on-demand data saving mechanisms for storing only relevant projections acquired by fast area detectors and reducing data volumes. Here the implementation details of a 3D real-time imaging monitoring instrument, with zooming to a volume of interest with easy-to-use visualization via ImageJ, a tool familiar to most beamline users, is presented. The instrument relies on optimized data flow between the detector and processing machines and is implemented on commodity computers. The instrument has been developed at beamline 2-BM of the Advanced Photon Source, where the automatic lens changing mechanism for zooming is implemented with an Optique Peter microscope. Performance tests demonstrate the ability to process more than 3 GB of projection data per second and generate real-time 3D zooming with different magnification. These new capabilities are essential for new APS Upgrade instruments such as the projection microscope under development at beamline 32-ID. The efficacy of the proposed instrument was demonstrated during an in situ tomographic experiment on ice and gas hydrate formation in porous samples.
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Affiliation(s)
- Viktor Nikitin
- Advanced Photon Source, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Aniket Tekawade
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Anton Duchkov
- Institute of Petroleum Geology and Geophysics SB RAS, 630090 Novosibirsk, Russia
- Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Pavel Shevchenko
- Advanced Photon Source, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Francesco De Carlo
- Advanced Photon Source, Argonne National Laboratory, Lemont, IL 60439, USA
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11
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Jørgensen JS, Ametova E, Burca G, Fardell G, Papoutsellis E, Pasca E, Thielemans K, Turner M, Warr R, Lionheart WRB, Withers PJ. Core Imaging Library - Part I: a versatile Python framework for tomographic imaging. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200192. [PMID: 34218673 PMCID: PMC8255949 DOI: 10.1098/rsta.2020.0192] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
We present the Core Imaging Library (CIL), an open-source Python framework for tomographic imaging with particular emphasis on reconstruction of challenging datasets. Conventional filtered back-projection reconstruction tends to be insufficient for highly noisy, incomplete, non-standard or multi-channel data arising for example in dynamic, spectral and in situ tomography. CIL provides an extensive modular optimization framework for prototyping reconstruction methods including sparsity and total variation regularization, as well as tools for loading, preprocessing and visualizing tomographic data. The capabilities of CIL are demonstrated on a synchrotron example dataset and three challenging cases spanning golden-ratio neutron tomography, cone-beam X-ray laminography and positron emission tomography. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.
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Affiliation(s)
- J. S. Jørgensen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
- Department of Mathematics, The University of Manchester, Manchester, UK
| | - E. Ametova
- Laboratory for Applications of Synchrotron Radiation, Karlsruhe Institute of Technology, Karlsruhe, Germany
- Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK
| | - G. Burca
- ISIS Neutron and Muon Source, STFC, UKRI, Rutherford Appleton Laboratory, Didcot, UK
- Department of Mathematics, The University of Manchester, Manchester, UK
| | - G. Fardell
- Scientific Computing Department, STFC, UKRI, Rutherford Appleton Laboratory, Didcot, UK
| | - E. Papoutsellis
- Scientific Computing Department, STFC, UKRI, Rutherford Appleton Laboratory, Didcot, UK
- Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK
| | - E. Pasca
- Scientific Computing Department, STFC, UKRI, Rutherford Appleton Laboratory, Didcot, UK
| | - K. Thielemans
- Institute of Nuclear Medicine and Centre for Medical Image Computing, University College London, London, UK
| | - M. Turner
- Research IT Services, The University of Manchester, Manchester, UK
| | - R. Warr
- Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK
| | | | - P. J. Withers
- Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK
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12
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Jørgensen JS, Ametova E, Burca G, Fardell G, Papoutsellis E, Pasca E, Thielemans K, Turner M, Warr R, Lionheart WRB, Withers PJ. Core Imaging Library - Part I: a versatile Python framework for tomographic imaging. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021. [PMID: 34218673 DOI: 10.5281/zenodo.4744394] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
We present the Core Imaging Library (CIL), an open-source Python framework for tomographic imaging with particular emphasis on reconstruction of challenging datasets. Conventional filtered back-projection reconstruction tends to be insufficient for highly noisy, incomplete, non-standard or multi-channel data arising for example in dynamic, spectral and in situ tomography. CIL provides an extensive modular optimization framework for prototyping reconstruction methods including sparsity and total variation regularization, as well as tools for loading, preprocessing and visualizing tomographic data. The capabilities of CIL are demonstrated on a synchrotron example dataset and three challenging cases spanning golden-ratio neutron tomography, cone-beam X-ray laminography and positron emission tomography. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.
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Affiliation(s)
- J S Jørgensen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
- Department of Mathematics, The University of Manchester, Manchester, UK
| | - E Ametova
- Laboratory for Applications of Synchrotron Radiation, Karlsruhe Institute of Technology, Karlsruhe, Germany
- Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK
| | - G Burca
- ISIS Neutron and Muon Source, STFC, UKRI, Rutherford Appleton Laboratory, Didcot, UK
- Department of Mathematics, The University of Manchester, Manchester, UK
| | - G Fardell
- Scientific Computing Department, STFC, UKRI, Rutherford Appleton Laboratory, Didcot, UK
| | - E Papoutsellis
- Scientific Computing Department, STFC, UKRI, Rutherford Appleton Laboratory, Didcot, UK
- Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK
| | - E Pasca
- Scientific Computing Department, STFC, UKRI, Rutherford Appleton Laboratory, Didcot, UK
| | - K Thielemans
- Institute of Nuclear Medicine and Centre for Medical Image Computing, University College London, London, UK
| | - M Turner
- Research IT Services, The University of Manchester, Manchester, UK
| | - R Warr
- Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK
| | - W R B Lionheart
- Department of Mathematics, The University of Manchester, Manchester, UK
| | - P J Withers
- Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK
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13
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Langer M, Zhang Y, Figueirinhas D, Forien JB, Mom K, Mouton C, Mokso R, Villanueva-Perez P. PyPhase - a Python package for X-ray phase imaging. JOURNAL OF SYNCHROTRON RADIATION 2021; 28:1261-1266. [PMID: 34212892 PMCID: PMC8284402 DOI: 10.1107/s1600577521004951] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 05/11/2021] [Indexed: 06/01/2023]
Abstract
X-ray propagation-based imaging techniques are well established at synchrotron radiation and laboratory sources. However, most reconstruction algorithms for such image modalities, also known as phase-retrieval algorithms, have been developed specifically for one instrument by and for experts, making the development and diffusion of such techniques difficult. Here, PyPhase, a free and open-source package for propagation-based near-field phase reconstructions, which is distributed under the CeCILL license, is presented. PyPhase implements some of the most popular phase-retrieval algorithms in a highly modular framework supporting its deployment on large-scale computing facilities. This makes the integration, the development of new phase-retrieval algorithms, and the deployment on different computing infrastructures straightforward. Its capabilities and simplicity are presented by application to data acquired at the synchrotron source MAX IV (Lund, Sweden).
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Affiliation(s)
- Max Langer
- Univ Lyon, INSA Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621 Villeurbanne, France
| | - Yuhe Zhang
- Division of Synchrotron Radiation Research and NanoLund, Department of Physics, Lund University, SE-221 00 Lund, Sweden
| | - Diogo Figueirinhas
- Division of Packaging Logistics, Faculty of Engineering, Lund University, SE-22100 Lund, Sweden
- MAX IV Laboratory, Lund University, SE-22100 Lund, Sweden
| | | | - Kannara Mom
- Univ Lyon, INSA Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621 Villeurbanne, France
| | - Claire Mouton
- Univ Lyon, INSA Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621 Villeurbanne, France
| | - Rajmund Mokso
- Division of Solid Mechanics, Faculty of Engineering, Lund University, SE-22100 Lund, Sweden
| | - Pablo Villanueva-Perez
- Division of Synchrotron Radiation Research and NanoLund, Department of Physics, Lund University, SE-221 00 Lund, Sweden
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14
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Zhang Q, Dufresne EM, Nakaye Y, Jemian PR, Sakumura T, Sakuma Y, Ferrara JD, Maj P, Hassan A, Bahadur D, Ramakrishnan S, Khan F, Veseli S, Sandy AR, Schwarz N, Narayanan S. 20 µs-resolved high-throughput X-ray photon correlation spectroscopy on a 500k pixel detector enabled by data-management workflow. JOURNAL OF SYNCHROTRON RADIATION 2021; 28:259-265. [PMID: 33399576 DOI: 10.1107/s1600577520014319] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 10/28/2020] [Indexed: 06/12/2023]
Abstract
The performance of the new 52 kHz frame rate Rigaku XSPA-500k detector was characterized on beamline 8-ID-I at the Advanced Photon Source at Argonne for X-ray photon correlation spectroscopy (XPCS) applications. Due to the large data flow produced by this detector (0.2 PB of data per 24 h of continuous operation), a workflow system was deployed that uses the Advanced Photon Source data-management (DM) system and high-performance software to rapidly reduce area-detector data to multi-tau and two-time correlation functions in near real time, providing human-in-the-loop feedback to experimenters. The utility and performance of the workflow system are demonstrated via its application to a variety of small-angle XPCS measurements acquired from different detectors in different XPCS measurement modalities. The XSPA-500k detector, the software and the DM workflow system allow for the efficient acquisition and reduction of up to ∼109 area-detector data frames per day, facilitating the application of XPCS to measuring samples with weak scattering and fast dynamics.
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Affiliation(s)
- Qingteng Zhang
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA
| | - Eric M Dufresne
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA
| | - Yasukazu Nakaye
- XRD Design and Engineering Department, Rigaku Corporation, 3-9-12 Matsubara-cho, Akishima-shi, Tokyo, Japan
| | - Pete R Jemian
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA
| | - Takuto Sakumura
- XRD Design and Engineering Department, Rigaku Corporation, 3-9-12 Matsubara-cho, Akishima-shi, Tokyo, Japan
| | - Yasutaka Sakuma
- XRD Design and Engineering Department, Rigaku Corporation, 3-9-12 Matsubara-cho, Akishima-shi, Tokyo, Japan
| | - Joseph D Ferrara
- XRD Design and Engineering Department, Rigaku Corporation, 3-9-12 Matsubara-cho, Akishima-shi, Tokyo, Japan
| | - Piotr Maj
- AGH University of Science and Technology, av. Mickiewicza 30, Krakow 30-059, Poland
| | - Asra Hassan
- Chemical and Biomedical Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA
| | - Divya Bahadur
- Chemical and Biomedical Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA
| | - Subramanian Ramakrishnan
- Chemical and Biomedical Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA
| | - Faisal Khan
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA
| | - Sinisa Veseli
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA
| | - Alec R Sandy
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA
| | - Nicholas Schwarz
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA
| | - Suresh Narayanan
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA
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15
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Prasad JA, Balwani AH, Johnson EC, Miano JD, Sampathkumar V, De Andrade V, Fezzaa K, Du M, Vescovi R, Jacobsen C, Kording KP, Gürsoy D, Gray Roncal W, Kasthuri N, Dyer EL. A three-dimensional thalamocortical dataset for characterizing brain heterogeneity. Sci Data 2020; 7:358. [PMID: 33082340 PMCID: PMC7576781 DOI: 10.1038/s41597-020-00692-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 09/17/2020] [Indexed: 02/07/2023] Open
Abstract
Neural microarchitecture is heterogeneous, varying both across and within brain regions. The consistent identification of regions of interest is one of the most critical aspects in examining neurocircuitry, as these structures serve as the vital landmarks with which to map brain pathways. Access to continuous, three-dimensional volumes that span multiple brain areas not only provides richer context for identifying such landmarks, but also enables a deeper probing of the microstructures within. Here, we describe a three-dimensional X-ray microtomography imaging dataset of a well-known and validated thalamocortical sample, encompassing a range of cortical and subcortical structures from the mouse brain . In doing so, we provide the field with access to a micron-scale anatomical imaging dataset ideal for studying heterogeneity of neural structure.
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Affiliation(s)
- Judy A Prasad
- Department of Neurobiology, University of Chicago, Chicago, IL, USA
| | - Aishwarya H Balwani
- School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Erik C Johnson
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA
| | - Joseph D Miano
- College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
| | | | | | - Kamel Fezzaa
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL, USA
| | - Ming Du
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL, USA
| | - Rafael Vescovi
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL, USA
| | - Chris Jacobsen
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL, USA
- Department of Physics & Astronomy, Northwestern University, Evanston, IL, USA
| | - Konrad P Kording
- Department of Biomedical Engineering, University of Pennsylvania, Philadelpha, PA, USA
| | - Doga Gürsoy
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL, USA
| | | | | | - Eva L Dyer
- School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
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16
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Peng X, Kulkarni D, Huang Y, Omasta TJ, Ng B, Zheng Y, Wang L, LaManna JM, Hussey DS, Varcoe JR, Zenyuk IV, Mustain WE. Using operando techniques to understand and design high performance and stable alkaline membrane fuel cells. Nat Commun 2020; 11:3561. [PMID: 32678101 PMCID: PMC7366663 DOI: 10.1038/s41467-020-17370-7] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 06/26/2020] [Indexed: 11/16/2022] Open
Abstract
There is a need to understand the water dynamics of alkaline membrane fuel cells under various operating conditions to create electrodes that enable high performance and stable, long-term operation. Here we show, via operando neutron imaging and operando micro X-ray computed tomography, visualizations of the spatial and temporal distribution of liquid water in operating cells. We provide direct evidence for liquid water accumulation at the anode, which causes severe ionomer swelling and performance loss, as well as cell dryout from undesirably low water content in the cathode. We observe that the operating conditions leading to the highest power density during polarization are not generally the conditions that allow for long-term stable operation. This observation leads to new catalyst layer designs and gas diffusion layers. This study reports alkaline membrane fuel cells that can be operated continuously for over 1000 h at 600 mA cm-2 with voltage decay rate of only 32-μV h-1 - the best-reported durability to date.
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Affiliation(s)
- Xiong Peng
- Department of Chemical Engineering, University of South Carolina, Columbia, SC, 29208, USA
| | - Devashish Kulkarni
- Department of Materials Science and Engineering; National Fuel Cell Research Center, University of California Irvine, Irvine, CA, 92697-2700, USA
| | - Ying Huang
- Department of Materials Science and Engineering; National Fuel Cell Research Center, University of California Irvine, Irvine, CA, 92697-2700, USA
| | - Travis J Omasta
- Department of Chemical Engineering, University of South Carolina, Columbia, SC, 29208, USA
| | - Benjamin Ng
- Department of Chemical Engineering, University of South Carolina, Columbia, SC, 29208, USA
| | - Yiwei Zheng
- Department of Chemical Engineering, University of South Carolina, Columbia, SC, 29208, USA
| | - Lianqin Wang
- Department of Chemistry, University of Surrey, Guildford, GU2 7XH, UK
| | - Jacob M LaManna
- National Institute for Standards and Technology, Gaithersburg, MD, 20899, USA
| | - Daniel S Hussey
- National Institute for Standards and Technology, Gaithersburg, MD, 20899, USA
| | - John R Varcoe
- Department of Chemistry, University of Surrey, Guildford, GU2 7XH, UK
| | - Iryna V Zenyuk
- Department of Materials Science and Engineering; National Fuel Cell Research Center, University of California Irvine, Irvine, CA, 92697-2700, USA
- Department of Chemical and Biomolecular Engineering, University of California Irvine, Irvine, CA, 92697-2700, USA
| | - William E Mustain
- Department of Chemical Engineering, University of South Carolina, Columbia, SC, 29208, USA.
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17
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Micrometer-resolution X-ray tomographic full-volume reconstruction of an intact post-mortem juvenile rat lung. Histochem Cell Biol 2020; 155:215-226. [PMID: 32189111 PMCID: PMC7910225 DOI: 10.1007/s00418-020-01868-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/06/2020] [Indexed: 01/30/2023]
Abstract
In this article, we present an X-ray tomographic imaging method that is well suited for pulmonary disease studies in animal models to resolve the full pathway from gas intake to gas exchange. Current state-of-the-art synchrotron-based tomographic phase-contrast imaging methods allow for three-dimensional microscopic imaging data to be acquired non-destructively in scan times of the order of seconds with good soft tissue contrast. However, when studying multi-scale hierarchically structured objects, such as the mammalian lung, the overall sample size typically exceeds the field of view illuminated by the X-rays in a single scan and the necessity for achieving a high spatial resolution conflicts with the need to image the whole sample. Several image stitching and calibration techniques to achieve extended high-resolution fields of view have been reported, but those approaches tend to fail when imaging non-stable samples, thus precluding tomographic measurements of large biological samples, which are prone to degradation and motion during extended scan times. In this work, we demonstrate a full-volume three-dimensional reconstruction of an intact rat lung under immediate post-mortem conditions and at an isotropic voxel size of (2.75 µm)3. We present the methodology for collecting multiple local tomographies with 360° extended field of view scans followed by locally non-rigid volumetric stitching. Applied to the lung, it allows to resolve the entire pulmonary structure from the trachea down to the parenchyma in a single dataset. The complete dataset is available online (https://doi.org/10.16907/7eb141d3-11f1-47a6-9d0e-76f8832ed1b2).
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18
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Vescovi R, Du M, de Andrade V, Scullin W, Gürsoy D, Jacobsen C. Tomosaic: efficient acquisition and reconstruction of teravoxel tomography data using limited-size synchrotron X-ray beams. JOURNAL OF SYNCHROTRON RADIATION 2018; 25:1478-1489. [PMID: 30179188 PMCID: PMC6140399 DOI: 10.1107/s1600577518010093] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Accepted: 06/24/2018] [Indexed: 05/13/2023]
Abstract
X-rays offer high penetration with the potential for tomography of centimetre-sized specimens, but synchrotron beamlines often provide illumination that is only millimetres wide. Here an approach is demonstrated termed Tomosaic for tomographic imaging of large samples that extend beyond the illumination field of view of an X-ray imaging system. This includes software modules for image stitching and calibration, while making use of existing modules available in other packages for alignment and reconstruction. The approach is compatible with conventional beamline hardware, while providing a dose-efficient method of data acquisition. By using parallelization on a distributed computing system, it provides a solution for handling teravoxel-sized or larger datasets that cannot be processed on a single workstation in a reasonable time. Using experimental data, the package is shown to provide good quality three-dimensional reconstruction for centimetre-sized samples with sub-micrometre pixel size.
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Affiliation(s)
- Rafael Vescovi
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Ming Du
- Department of Materials Science, Northwestern University, Evanston, IL 60208, USA
| | - Vincent de Andrade
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA
| | - William Scullin
- Argonne Leadership Computing Facility, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Doǧa Gürsoy
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA
- Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA
| | - Chris Jacobsen
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA
- Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, USA
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL 60208, USA
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19
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Pandolfi RJ, Allan DB, Arenholz E, Barroso-Luque L, Campbell SI, Caswell TA, Blair A, De Carlo F, Fackler S, Fournier AP, Freychet G, Fukuto M, Gürsoy D, Jiang Z, Krishnan H, Kumar D, Kline RJ, Li R, Liman C, Marchesini S, Mehta A, N’Diaye AT, Parkinson DY, Parks H, Pellouchoud LA, Perciano T, Ren F, Sahoo S, Strzalka J, Sunday D, Tassone CJ, Ushizima D, Venkatakrishnan S, Yager KG, Zwart P, Sethian JA, Hexemer A. Xi-cam: a versatile interface for data visualization and analysis. JOURNAL OF SYNCHROTRON RADIATION 2018; 25:1261-1270. [PMID: 29979189 PMCID: PMC6691515 DOI: 10.1107/s1600577518005787] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 04/13/2018] [Indexed: 05/22/2023]
Abstract
Xi-cam is an extensible platform for data management, analysis and visualization. Xi-cam aims to provide a flexible and extensible approach to synchrotron data treatment as a solution to rising demands for high-volume/high-throughput processing pipelines. The core of Xi-cam is an extensible plugin-based graphical user interface platform which provides users with an interactive interface to processing algorithms. Plugins are available for SAXS/WAXS/GISAXS/GIWAXS, tomography and NEXAFS data. With Xi-cam's `advanced' mode, data processing steps are designed as a graph-based workflow, which can be executed live, locally or remotely. Remote execution utilizes high-performance computing or de-localized resources, allowing for the effective reduction of high-throughput data. Xi-cam's plugin-based architecture targets cross-facility and cross-technique collaborative development, in support of multi-modal analysis. Xi-cam is open-source and cross-platform, and available for download on GitHub.
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Affiliation(s)
- Ronald J. Pandolfi
- Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, USA
| | - Daniel B. Allan
- National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY, USA
| | - Elke Arenholz
- Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, USA
| | - Luis Barroso-Luque
- Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, USA
| | - Stuart I. Campbell
- National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY, USA
| | - Thomas A. Caswell
- National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY, USA
| | - Austin Blair
- Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, USA
| | - Francesco De Carlo
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL, USA
| | - Sean Fackler
- Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, USA
| | - Amanda P. Fournier
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA, USA
| | - Guillaume Freychet
- Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, USA
| | - Masafumi Fukuto
- National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY, USA
| | - Doǧa Gürsoy
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL, USA
- Department of Electrical Engineering and Computer Science, Northwestern University, 2145 Sheridan Road, Evanston, IL, USA
| | - Zhang Jiang
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL, USA
| | | | - Dinesh Kumar
- Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, USA
| | - R. Joseph Kline
- National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD, USA
| | - Ruipeng Li
- National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY, USA
| | - Christopher Liman
- National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD, USA
| | - Stefano Marchesini
- Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, USA
| | - Apurva Mehta
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA, USA
| | - Alpha T. N’Diaye
- Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, USA
| | | | - Holden Parks
- Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, USA
| | | | - Talita Perciano
- Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, USA
| | - Fang Ren
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA, USA
| | - Shreya Sahoo
- Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, USA
| | - Joseph Strzalka
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL, USA
| | - Daniel Sunday
- National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD, USA
| | | | - Daniela Ushizima
- Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, USA
| | | | - Kevin G. Yager
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY, USA
| | - Peter Zwart
- Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, USA
| | - James A. Sethian
- Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, USA
| | - Alexander Hexemer
- Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, USA
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20
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Operando X-ray tomography and sub-second radiography for characterizing transport in polymer electrolyte membrane electrolyzer. Electrochim Acta 2018. [DOI: 10.1016/j.electacta.2018.04.144] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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21
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Yang X, De Andrade V, Scullin W, Dyer EL, Kasthuri N, De Carlo F, Gürsoy D. Low-dose x-ray tomography through a deep convolutional neural network. Sci Rep 2018; 8:2575. [PMID: 29416047 PMCID: PMC5803233 DOI: 10.1038/s41598-018-19426-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Accepted: 12/27/2017] [Indexed: 02/07/2023] Open
Abstract
Synchrotron-based X-ray tomography offers the potential for rapid large-scale reconstructions of the interiors of materials and biological tissue at fine resolution. However, for radiation sensitive samples, there remain fundamental trade-offs between damaging samples during longer acquisition times and reducing signals with shorter acquisition times. We present a deep convolutional neural network (CNN) method that increases the acquired X-ray tomographic signal by at least a factor of 10 during low-dose fast acquisition by improving the quality of recorded projections. Short-exposure-time projections enhanced with CNNs show signal-to-noise ratios similar to long-exposure-time projections. They also show lower noise and more structural information than low-dose short-exposure acquisitions post-processed by other techniques. We evaluated this approach using simulated samples and further validated it with experimental data from radiation sensitive mouse brains acquired in a tomographic setting with transmission X-ray microscopy. We demonstrate that automated algorithms can reliably trace brain structures in low-dose datasets enhanced with CNN. This method can be applied to other tomographic or scanning based X-ray imaging techniques and has great potential for studying faster dynamics in specimens.
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Affiliation(s)
- Xiaogang Yang
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL, 60439, USA.
| | - Vincent De Andrade
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL, 60439, USA
| | - William Scullin
- Argonne Leadership Computing Facility (ALCF), Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL, 60439, USA
| | - Eva L Dyer
- Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, 313 Ferst Dr NW, Atlanta, GA, 30332, USA
| | - Narayanan Kasthuri
- Biology Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL, 60439, USA
- Department of Neurobiology, University of Chicago, 947 East 58th Street, Chicago, IL, 60637, USA
| | - Francesco De Carlo
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL, 60439, USA
| | - Doğa Gürsoy
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL, 60439, USA
- Department of Electrical Engineering and Computer Science, Northwestern University, 2145 Sheridan Road, Evanston, IL, 60208, USA
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22
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Kaira CS, De Andrade V, Singh SS, Kantzos C, Kirubanandham A, De Carlo F, Chawla N. Probing Novel Microstructural Evolution Mechanisms in Aluminum Alloys Using 4D Nanoscale Characterization. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2017; 29:1703482. [PMID: 28906570 DOI: 10.1002/adma.201703482] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Revised: 07/24/2017] [Indexed: 06/07/2023]
Abstract
Dispersions of nanoscale precipitates in metallic alloys have been known to play a key role in strengthening, by increasing their strain hardenability and providing resistance to deformation. Although these phenomena have been extensively investigated in the last century, the traditional approaches employed in the past have not rendered an authoritative microstructural understanding in such materials. The effect of the precipitates' inherent complex morphology and their 3D spatial distribution on evolution and deformation behavior have often been precluded. This study reports, for the first time, implementation of synchrotron-based hard X-ray nanotomography in Al-Cu alloys to measure kinetics of different nanoscale phases in 3D, and reveals insights behind some of the observed novel phase transformation reactions. The experimental results of the present study reconcile with coarsening models from the Lifshitz-Slyozov-Wagner theory to an unprecedented extent, thereby establishing a new paradigm for thermodynamic analysis of precipitate assemblies. Finally, this study sheds light on the possibilities for establishing new theories for dislocation-particle interactions, based on the limitations of using the Orowan equation in estimating precipitation strengthening.
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Affiliation(s)
- C Shashank Kaira
- Materials Science and Engineering, Arizona State University, Tempe, AZ, 85287, USA
| | - V De Andrade
- Advanced Photon Source, Argonne National Laboratory, Building 401, 9700 S. Cass Avenue, Argonne, IL, 60439, USA
| | - Sudhanshu S Singh
- Materials Science and Engineering, Arizona State University, Tempe, AZ, 85287, USA
- Department of Materials Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, 208016, India
| | - C Kantzos
- Materials Science and Engineering, Arizona State University, Tempe, AZ, 85287, USA
| | - Antony Kirubanandham
- Materials Science and Engineering, Arizona State University, Tempe, AZ, 85287, USA
| | - F De Carlo
- Advanced Photon Source, Argonne National Laboratory, Building 401, 9700 S. Cass Avenue, Argonne, IL, 60439, USA
| | - Nikhilesh Chawla
- Materials Science and Engineering, Arizona State University, Tempe, AZ, 85287, USA
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23
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Mokso R, Schlepütz CM, Theidel G, Billich H, Schmid E, Celcer T, Mikuljan G, Sala L, Marone F, Schlumpf N, Stampanoni M. GigaFRoST: the gigabit fast readout system for tomography. JOURNAL OF SYNCHROTRON RADIATION 2017; 24:1250-1259. [PMID: 29091068 PMCID: PMC5665295 DOI: 10.1107/s1600577517013522] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 09/20/2017] [Indexed: 05/22/2023]
Abstract
Owing to recent developments in CMOS technology, it is now possible to exploit tomographic microscopy at third-generation synchrotron facilities with unprecedented speeds. Despite this rapid technical progress, one crucial limitation for the investigation of realistic dynamic systems has remained: a generally short total acquisition time at high frame rates due to the limited internal memory of available detectors. To address and solve this shortcoming, a new detection and readout system, coined GigaFRoST, has been developed based on a commercial CMOS sensor, acquiring and streaming data continuously at 7.7 GB s-1 directly to a dedicated backend server. This architecture allows for dynamic data pre-processing as well as data reduction, an increasingly indispensable step considering the vast amounts of data acquired in typical fast tomographic experiments at synchrotron beamlines (up to several tens of TByte per day of raw data).
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Affiliation(s)
- Rajmund Mokso
- Swiss Light Source, Paul Scherrer Institute, Villigen, Switzerland
| | | | - Gerd Theidel
- Electronics for Measuring Systems, Paul Scherrer Institute, Villigen, Switzerland
| | - Heiner Billich
- Information Technology Division, Paul Scherrer Institute, Villigen, Switzerland
| | - Elmar Schmid
- Electronics for Measuring Systems, Paul Scherrer Institute, Villigen, Switzerland
| | - Tine Celcer
- Controls Section, GFA, Paul Scherrer Institute, Villigen, Switzerland
| | - Gordan Mikuljan
- Swiss Light Source, Paul Scherrer Institute, Villigen, Switzerland
| | - Leonardo Sala
- Information Technology Division, Paul Scherrer Institute, Villigen, Switzerland
| | - Federica Marone
- Swiss Light Source, Paul Scherrer Institute, Villigen, Switzerland
| | - Nick Schlumpf
- Electronics for Measuring Systems, Paul Scherrer Institute, Villigen, Switzerland
| | - Marco Stampanoni
- Swiss Light Source, Paul Scherrer Institute, Villigen, Switzerland
- Institute for Biomedical Engineering, ETH Zurich, Switzerland
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24
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Zhao C, Wada T, De Andrade V, Williams GJ, Gelb J, Li L, Thieme J, Kato H, Chen-Wiegart YCK. Three-Dimensional Morphological and Chemical Evolution of Nanoporous Stainless Steel by Liquid Metal Dealloying. ACS APPLIED MATERIALS & INTERFACES 2017; 9:34172-34184. [PMID: 28869380 DOI: 10.1021/acsami.7b04659] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Nanoporous materials, especially those fabricated by liquid metal dealloying processes, possess great potential in a wide range of applications due to their high surface area, bicontinuous structure with both open pores for transport and solid phase for conductivity or support, and low material cost. Here, we used X-ray nanotomography and X-ray fluorescence microscopy to reveal the three-dimensional (3D) morphology and elemental distribution within materials. Focusing on nanoporous stainless steel, we evaluated the 3D morphology of the dealloying front and established a quantitative processing-structure-property relationship at a later stage of dealloying. The morphological differences of samples created by liquid metal dealloying and aqueous dealloying methods were also discussed. We concluded that it is particularly important to consider the dealloying, coarsening, and densification mechanisms in influencing the performance-determining, critical 3D parameters, such as tortuosity, pore size, porosity, curvature, and interfacial shape.
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Affiliation(s)
- Chonghang Zhao
- Department of Materials Science and Chemical Engineering, Stony Brook University , Stony Brook, New York 11794, United States
| | - Takeshi Wada
- Institute for Materials Research, Tohoku University , Katahira, Sendai 980-8577, Japan
| | - Vincent De Andrade
- Advanced Photon Source, Argonne National Laboratory , Argonne, Illinois 60439, United States
| | - Garth J Williams
- National Synchrotron Light Source II, Brookhaven National Laboratory , Upton, New York 11973, United States
| | - Jeff Gelb
- ZEISS Group, Carl Zeiss X-ray Microscopy, Inc. , Pleasanton, California 94588, United States
| | - Li Li
- National Synchrotron Light Source II, Brookhaven National Laboratory , Upton, New York 11973, United States
| | - Juergen Thieme
- National Synchrotron Light Source II, Brookhaven National Laboratory , Upton, New York 11973, United States
| | - Hidemi Kato
- Institute for Materials Research, Tohoku University , Katahira, Sendai 980-8577, Japan
| | - Yu-Chen Karen Chen-Wiegart
- Department of Materials Science and Chemical Engineering, Stony Brook University , Stony Brook, New York 11794, United States
- National Synchrotron Light Source II, Brookhaven National Laboratory , Upton, New York 11973, United States
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25
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Dyer EL, Gray Roncal W, Prasad JA, Fernandes HL, Gürsoy D, De Andrade V, Fezzaa K, Xiao X, Vogelstein JT, Jacobsen C, Körding KP, Kasthuri N. Quantifying Mesoscale Neuroanatomy Using X-Ray Microtomography. eNeuro 2017; 4:ENEURO.0195-17.2017. [PMID: 29085899 PMCID: PMC5659258 DOI: 10.1523/eneuro.0195-17.2017] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Revised: 08/02/2017] [Accepted: 08/23/2017] [Indexed: 11/21/2022] Open
Abstract
Methods for resolving the three-dimensional (3D) microstructure of the brain typically start by thinly slicing and staining the brain, followed by imaging numerous individual sections with visible light photons or electrons. In contrast, X-rays can be used to image thick samples, providing a rapid approach for producing large 3D brain maps without sectioning. Here we demonstrate the use of synchrotron X-ray microtomography (µCT) for producing mesoscale (∼1 µm 3 resolution) brain maps from millimeter-scale volumes of mouse brain. We introduce a pipeline for µCT-based brain mapping that develops and integrates methods for sample preparation, imaging, and automated segmentation of cells, blood vessels, and myelinated axons, in addition to statistical analyses of these brain structures. Our results demonstrate that X-ray tomography achieves rapid quantification of large brain volumes, complementing other brain mapping and connectomics efforts.
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Affiliation(s)
- Eva L. Dyer
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30332
| | - William Gray Roncal
- The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, 20723
- Dept. of Computer Science, The Johns Hopkins University, Baltimore, MD, 21218
| | - Judy A. Prasad
- Dept. of Neurobiology, University of Chicago, Chicago, IL, 60637
| | - Hugo L. Fernandes
- Dept. of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, 60611
- Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, 60611
| | - Doga Gürsoy
- Advanced Photon Source, Argonne National Laboratory, Lemont, IL, 60439
| | | | - Kamel Fezzaa
- Advanced Photon Source, Argonne National Laboratory, Lemont, IL, 60439
| | - Xianghui Xiao
- Advanced Photon Source, Argonne National Laboratory, Lemont, IL, 60439
| | - Joshua T. Vogelstein
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, 21205
- Institute of Computational Medicine, The Johns Hopkins University, Baltimore, MD, 21218
| | - Chris Jacobsen
- Advanced Photon Source, Argonne National Laboratory, Lemont, IL, 60439
- Department of Physics and Astronomy, Northwestern University, Chicago, IL, 60208
| | - Konrad P. Körding
- Department of Biomedical Engineering, University of Pennsylvania, Philadelphia, PA, 19104
| | - Narayanan Kasthuri
- Dept. of Neurobiology, University of Chicago, Chicago, IL, 60637
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, 60439
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26
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Di ZW, Chen S, Hong YP, Jacobsen C, Leyffer S, Wild SM. Joint reconstruction of x-ray fluorescence and transmission tomography. OPTICS EXPRESS 2017; 25:13107-13124. [PMID: 28788848 PMCID: PMC5499635 DOI: 10.1364/oe.25.013107] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Accepted: 04/27/2017] [Indexed: 05/26/2023]
Abstract
X-ray fluorescence tomography is based on the detection of fluorescence x-ray photons produced following x-ray absorption while a specimen is rotated; it provides information on the 3D distribution of selected elements within a sample. One limitation in the quality of sample recovery is the separation of elemental signals due to the finite energy resolution of the detector. Another limitation is the effect of self-absorption, which can lead to inaccurate results with dense samples. To recover a higher quality elemental map, we combine x-ray fluorescence detection with a second data modality: conventional x-ray transmission tomography using absorption. By using these combined signals in a nonlinear optimization-based approach, we demonstrate the benefit of our algorithm on real experimental data and obtain an improved quantitative reconstruction of the spatial distribution of dominant elements in the sample. Compared with single-modality inversion based on x-ray fluorescence alone, this joint inversion approach reduces ill-posedness and should result in improved elemental quantification and better correction of self-absorption.
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Affiliation(s)
- Zichao Wendy Di
- Mathematics and Computer Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439,
USA
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439,
USA
| | - Si Chen
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439,
USA
| | - Young Pyo Hong
- Department of Physics & Astronomy, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208,
USA
| | - Chris Jacobsen
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439,
USA
- Department of Physics & Astronomy, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208,
USA
- Chemistry of Life Processes Institute, Northwestern University, 2170 Campus Drive, Evanston, IL 60208,
USA
| | - Sven Leyffer
- Mathematics and Computer Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439,
USA
| | - Stefan M. Wild
- Mathematics and Computer Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439,
USA
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27
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Yang X, De Carlo F, Phatak C, Gürsoy D. A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography. JOURNAL OF SYNCHROTRON RADIATION 2017; 24:469-475. [PMID: 28244442 DOI: 10.1107/s1600577516020117] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Accepted: 12/18/2016] [Indexed: 06/06/2023]
Abstract
This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential for reducing or removing other artifacts caused by instrument instability, detector non-linearity, etc. An open-source toolbox, which integrates the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided.
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Affiliation(s)
- Xiaogang Yang
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA
| | - Francesco De Carlo
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA
| | - Charudatta Phatak
- Materials Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA
| | - Dogˇa Gürsoy
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA
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28
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Marone F, Studer A, Billich H, Sala L, Stampanoni M. Towards on-the-fly data post-processing for real-time tomographic imaging at TOMCAT. ACTA ACUST UNITED AC 2017; 3:1. [PMID: 28261539 PMCID: PMC5313565 DOI: 10.1186/s40679-016-0035-9] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Accepted: 12/07/2016] [Indexed: 11/27/2022]
Abstract
Sub-second full-field tomographic microscopy at third-generation synchrotron sources is a reality, opening up new possibilities for the study of dynamic systems in different fields. Sustained elevated data rates of multiple GB/s in tomographic experiments will become even more common at diffraction-limited storage rings, coming in operation soon. The computational tools necessary for the post-processing of raw tomographic projections have generally not experienced the same efficiency increase as the experimental facilities, hindering optimal exploitation of this new potential. We present here a fast, flexible, and user-friendly post-processing pipeline overcoming this efficiency mismatch and delivering reconstructed tomographic datasets just few seconds after the data have been acquired, enabling fast parameter and image quality evaluation as well as efficient post-processing of TBs of tomographic data. With this new tool, also able to accept a stream of data directly from a detector, few selected tomographic slices are available in less than half a second, providing advanced previewing capabilities paving the way to new concepts for on-the-fly control of dynamic experiments.
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Affiliation(s)
- Federica Marone
- Swiss Light Source, Paul Scherrer Institute, Villigen, Switzerland
| | - Alain Studer
- Information Technology Division, AIT, Paul Scherrer Institute, Villigen, Switzerland
| | - Heiner Billich
- Information Technology Division, AIT, Paul Scherrer Institute, Villigen, Switzerland
| | - Leonardo Sala
- Information Technology Division, AIT, Paul Scherrer Institute, Villigen, Switzerland
| | - Marco Stampanoni
- Swiss Light Source, Paul Scherrer Institute, Villigen, Switzerland.,Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
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29
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Yuan K, Lee SS, De Andrade V, Sturchio NC, Fenter P. Replacement of Calcite (CaCO 3) by Cerussite (PbCO 3). ENVIRONMENTAL SCIENCE & TECHNOLOGY 2016; 50:12984-12991. [PMID: 27767299 DOI: 10.1021/acs.est.6b03911] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The mobility of toxic elements, such as lead (Pb) can be attenuated by adsorption, incorporation, and precipitation on carbonate minerals in subsurface environments. Here, we report a study of the bulk transformation of single-crystal calcite (CaCO3) into polycrystalline cerussite (PbCO3) through reaction with acidic Pb-bearing solutions. This reaction began with the growth of a cerussite shell on top of calcite surfaces followed by the replacement of the remaining calcite core. The external shape of the original calcite was preserved by a balance between calcite dissolution and cerussite growth controlled by adjusting the Pb2+ concentration and pH. The relation between the rounded calcite core and the surrounding lath-shaped cerussite aggregates was imaged by transmission X-ray microscopy, which revealed preferentially elongated cerussite crystals parallel to the surface and edge directions of calcite. The replacement reaction involved concurrent development of ∼100 nm wide pores parallel to calcite c-glide or (12̅0) planes, which may have provided permeability for chemical exchange during the reaction. X-ray reflectivity measurements showed no clear epitaxial relation of cerussite to the calcite (104) surface. These results demonstrate Pb sequestration through mineral replacement reactions and the critical role of nanoporosity (3% by volume) on the solid phase transformation through a dissolution-recrystallization mechanism.
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Affiliation(s)
| | | | | | - Neil C Sturchio
- Department of Geological Sciences, University of Delaware , Newark, Delaware 19716, United States
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30
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Matěj Z, Mokso R, Larsson K, Hardion V, Spruce D. The MAX IV imaging concept. ADVANCED STRUCTURAL AND CHEMICAL IMAGING 2016; 2:16. [PMID: 28003953 PMCID: PMC5133273 DOI: 10.1186/s40679-016-0029-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 11/24/2016] [Indexed: 11/13/2022]
Abstract
The MAX IV Laboratory is currently the synchrotron X-ray source with the beam of highest brilliance. Four imaging beamlines are in construction or in the project phase. Their common characteristic will be the high acquisition rates of phase-enhanced images. This high data flow will be managed at the local computing cluster jointly with the Swedish National Computing Infrastructure. A common image reconstruction and analysis platform is being designed to offer reliable quantification of the multidimensional images acquired at all the imaging beamlines at MAX IV.
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31
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Bicer T, Gürsoy D, Kettimuthu R, De Carlo F, Foster IT. Optimization of tomographic reconstruction workflows on geographically distributed resources. JOURNAL OF SYNCHROTRON RADIATION 2016; 23:997-1005. [PMID: 27359149 PMCID: PMC5315096 DOI: 10.1107/s1600577516007980] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2016] [Accepted: 05/16/2016] [Indexed: 05/26/2023]
Abstract
New technological advancements in synchrotron light sources enable data acquisitions at unprecedented levels. This emergent trend affects not only the size of the generated data but also the need for larger computational resources. Although beamline scientists and users have access to local computational resources, these are typically limited and can result in extended execution times. Applications that are based on iterative processing as in tomographic reconstruction methods require high-performance compute clusters for timely analysis of data. Here, time-sensitive analysis and processing of Advanced Photon Source data on geographically distributed resources are focused on. Two main challenges are considered: (i) modeling of the performance of tomographic reconstruction workflows and (ii) transparent execution of these workflows on distributed resources. For the former, three main stages are considered: (i) data transfer between storage and computational resources, (i) wait/queue time of reconstruction jobs at compute resources, and (iii) computation of reconstruction tasks. These performance models allow evaluation and estimation of the execution time of any given iterative tomographic reconstruction workflow that runs on geographically distributed resources. For the latter challenge, a workflow management system is built, which can automate the execution of workflows and minimize the user interaction with the underlying infrastructure. The system utilizes Globus to perform secure and efficient data transfer operations. The proposed models and the workflow management system are evaluated by using three high-performance computing and two storage resources, all of which are geographically distributed. Workflows were created with different computational requirements using two compute-intensive tomographic reconstruction algorithms. Experimental evaluation shows that the proposed models and system can be used for selecting the optimum resources, which in turn can provide up to 3.13× speedup (on experimented resources). Moreover, the error rates of the models range between 2.1 and 23.3% (considering workflow execution times), where the accuracy of the model estimations increases with higher computational demands in reconstruction tasks.
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Affiliation(s)
- Tekin Bicer
- Mathematics and Computer Science Division, Argonne National Laboratory, USA
| | - Doǧa Gürsoy
- Advanced Photon Source, X-ray Science Division, Argonne National Laboratory, USA
| | - Rajkumar Kettimuthu
- Mathematics and Computer Science Division, Argonne National Laboratory, USA
- Computation Institute, University of Chicago and Argonne National Laboratory, USA
| | - Francesco De Carlo
- Advanced Photon Source, X-ray Science Division, Argonne National Laboratory, USA
| | - Ian T. Foster
- Mathematics and Computer Science Division, Argonne National Laboratory, USA
- Computation Institute, University of Chicago and Argonne National Laboratory, USA
- Department of Computer Science, University of Chicago, USA
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32
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Agostino R, Donato S, Caruso T, Colavita E, Zanini F, D'Alessio A, Pisarra D, Taliano Grasso A. Microtomographic studies as a tool in the identification of a new ceramic class: The metal-imitating pottery as grave goods among Brettians and Lucanians. Microchem J 2016. [DOI: 10.1016/j.microc.2015.12.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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33
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Pelt DM, Gürsoy D, Palenstijn WJ, Sijbers J, De Carlo F, Batenburg KJ. Integration of TomoPy and the ASTRA toolbox for advanced processing and reconstruction of tomographic synchrotron data. JOURNAL OF SYNCHROTRON RADIATION 2016; 23:842-9. [PMID: 27140167 PMCID: PMC5315009 DOI: 10.1107/s1600577516005658] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Accepted: 04/05/2016] [Indexed: 05/20/2023]
Abstract
The processing of tomographic synchrotron data requires advanced and efficient software to be able to produce accurate results in reasonable time. In this paper, the integration of two software toolboxes, TomoPy and the ASTRA toolbox, which, together, provide a powerful framework for processing tomographic data, is presented. The integration combines the advantages of both toolboxes, such as the user-friendliness and CPU-efficient methods of TomoPy and the flexibility and optimized GPU-based reconstruction methods of the ASTRA toolbox. It is shown that both toolboxes can be easily installed and used together, requiring only minor changes to existing TomoPy scripts. Furthermore, it is shown that the efficient GPU-based reconstruction methods of the ASTRA toolbox can significantly decrease the time needed to reconstruct large datasets, and that advanced reconstruction methods can improve reconstruction quality compared with TomoPy's standard reconstruction method.
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Affiliation(s)
- Daniël M. Pelt
- Centrum Wiskunde and Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands
| | - Doǧa Gürsoy
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439-4837, USA
| | - Willem Jan Palenstijn
- Centrum Wiskunde and Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands
| | - Jan Sijbers
- iMinds–Vision Lab, University of Antwerp, Universiteitsplein 1, B-2610 Antwerp, Belgium
| | - Francesco De Carlo
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439-4837, USA
| | - Kees Joost Batenburg
- Centrum Wiskunde and Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands
- iMinds–Vision Lab, University of Antwerp, Universiteitsplein 1, B-2610 Antwerp, Belgium
- Mathematical Institute, Leiden University, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
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Hong YP, Chen S, Jacobsen C. A New Workflow for x-ray fluorescence tomography: MAPSToTomoPy. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2015; 9592. [PMID: 27103755 DOI: 10.1117/12.2194162] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
X-ray fluorescence tomography involves the acquisition of a series of 2D x-ray fluorescence datasets between which a specimen is rotated. At the Advanced Photon Source at Argonne National Laboratory, the workflow at beamlines 2-ID-E and 21-ID-D (the Bionanoprobe, a cryogenic microscope system) has included the use of the program MAPS for obtaining elemental concentrations from 2D images, and the program TomoPy which was developed to include several tomographic reconstruction methods for x-ray transmission data. In the past, fluorescence projection images from an individual chemical element were hand-assembled into a 3D dataset for reconstruction using interactive tools such as ImageJ. We describe here the program MAPSToTomoPy, which provides a graphical user interface (GUI) to control a workflow between MAPS and TomoPy, with tools for visualizing the sinograms of projection image sequences from particular elements and to use these to help correct misalignments of the rotation axis. The program also provides an integrated output of the 3D distribution of the detected elements for subsequent 3D visualization packages.
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Affiliation(s)
- Young Pyo Hong
- Department of Physics and Astronomy, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA
| | - Si Chen
- Advanced Photon Source, Argonne National Laboratory, 9700 S. Cass Ave., Argonne IL 60439-4837 USA
| | - Chris Jacobsen
- Advanced Photon Source, Argonne National Laboratory, 9700 S. Cass Ave., Argonne IL 60439-4837 USA; Department of Physics and Astronomy, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA
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Gürsoy D, Biçer T, Almer JD, Kettimuthu R, Stock SR, De Carlo F. Maximum a posteriori estimation of crystallographic phases in X-ray diffraction tomography. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2015; 373:rsta.2014.0392. [PMID: 25939627 PMCID: PMC4424486 DOI: 10.1098/rsta.2014.0392] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/13/2015] [Indexed: 05/29/2023]
Abstract
A maximum a posteriori approach is proposed for X-ray diffraction tomography for reconstructing three-dimensional spatial distribution of crystallographic phases and orientations of polycrystalline materials. The approach maximizes the a posteriori density which includes a Poisson log-likelihood and an a priori term that reinforces expected solution properties such as smoothness or local continuity. The reconstruction method is validated with experimental data acquired from a section of the spinous process of a porcine vertebra collected at the 1-ID-C beamline of the Advanced Photon Source, at Argonne National Laboratory. The reconstruction results show significant improvement in the reduction of aliasing and streaking artefacts, and improved robustness to noise and undersampling compared to conventional analytical inversion approaches. The approach has the potential to reduce data acquisition times, and significantly improve beamtime efficiency.
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Affiliation(s)
- Doĝa Gürsoy
- X-ray Science Division, Advanced Photon Source, Argonne National Laboratory, Lemont, IL, USA
| | - Tekin Biçer
- Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA
| | - Jonathan D Almer
- X-ray Science Division, Advanced Photon Source, Argonne National Laboratory, Lemont, IL, USA
| | - Raj Kettimuthu
- Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA
| | - Stuart R Stock
- Department of Cell and Molecular Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Francesco De Carlo
- X-ray Science Division, Advanced Photon Source, Argonne National Laboratory, Lemont, IL, USA
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Gürsoy D, Biçer T, Lanzirotti A, Newville MG, De Carlo F. Hyperspectral image reconstruction for x-ray fluorescence tomography. OPTICS EXPRESS 2015; 23:9014-23. [PMID: 25968737 DOI: 10.1364/oe.23.009014] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
A penalized maximum-likelihood estimation is proposed to perform hyperspectral (spatio-spectral) image reconstruction for X-ray fluorescence tomography. The approach minimizes a Poisson-based negative log-likelihood of the observed photon counts, and uses a penalty term that has the effect of encouraging local continuity of model parameter estimates in both spatial and spectral dimensions simultaneously. The performance of the reconstruction method is demonstrated with experimental data acquired from a seed of arabidopsis thaliana collected at the 13-ID-E microprobe beamline at the Advanced Photon Source. The resulting element distribution estimates with the proposed approach show significantly better reconstruction quality than the conventional analytical inversion approaches, and allows for a high data compression factor which can reduce data acquisition times remarkably. In particular, this technique provides the capability to tomographically reconstruct full energy dispersive spectra without compromising reconstruction artifacts that impact the interpretation of results.
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Bicer T, Gursoy D, Kettimuthu R, De Carlo F, Agrawal G, Foster IT. Rapid Tomographic Image Reconstruction via Large-Scale Parallelization. LECTURE NOTES IN COMPUTER SCIENCE 2015. [DOI: 10.1007/978-3-662-48096-0_23] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Gürsoy D, De Carlo F, Xiao X, Jacobsen C. TomoPy: a framework for the analysis of synchrotron tomographic data. JOURNAL OF SYNCHROTRON RADIATION 2014. [PMID: 25178011 DOI: 10.1117/12.2061373] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Analysis of tomographic datasets at synchrotron light sources (including X-ray transmission tomography, X-ray fluorescence microscopy and X-ray diffraction tomography) is becoming progressively more challenging due to the increasing data acquisition rates that new technologies in X-ray sources and detectors enable. The next generation of synchrotron facilities that are currently under design or construction throughout the world will provide diffraction-limited X-ray sources and are expected to boost the current data rates by several orders of magnitude, stressing the need for the development and integration of efficient analysis tools. Here an attempt to provide a collaborative framework for the analysis of synchrotron tomographic data that has the potential to unify the effort of different facilities and beamlines performing similar tasks is described in detail. The proposed Python-based framework is open-source, platform- and data-format-independent, has multiprocessing capability and supports procedural programming that many researchers prefer. This collaborative platform could affect all major synchrotron facilities where new effort is now dedicated to developing new tools that can be deployed at the facility for real-time processing, as well as distributed to users for off-site data processing.
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Affiliation(s)
- Dogˇa Gürsoy
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439-4837, USA
| | - Francesco De Carlo
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439-4837, USA
| | - Xianghui Xiao
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439-4837, USA
| | - Chris Jacobsen
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439-4837, USA
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