Article
Chemistry, Analytical
Bo Gao, Brecht Laforce, Laszlo Vincze, Luc Van Hoorebeke, Matthieu N. Boone
Summary: XFCT is a method capable of mapping elemental distribution within an object without destructive sectioning, but it faces challenges due to self-absorption effects. A novel reconstruction method has been proposed in this manuscript to address the issue of accurately reconstructing trace and low Z elements. The method has shown promising results in retrieving the density distribution of relatively low Z elements.
ANALYTICAL CHEMISTRY
(2021)
Article
Chemistry, Analytical
Frank Foerste, Leona Bauer, Korbinian Heimler, Bastian Hansel, Carla Vogt, Birgit Kanngiesser, Ioanna Mantouvalou
Summary: Confocal micro-X-ray fluorescence spectroscopy is a technique that can be used for elemental imaging with 3D resolution using laboratory spectrometers. Quantification techniques are important for interpreting data and reconstructing sample composition and geometry. This article presents an analytical routine for the quantitative investigation of 3D data sets obtained with laboratory spectrometers.
JOURNAL OF ANALYTICAL ATOMIC SPECTROMETRY
(2022)
Article
Computer Science, Interdisciplinary Applications
Chuanpeng Wu, Liang Li
Summary: X-ray fluorescence computed tomography (XFCT) is a promising method for imaging the distribution of high-Z elements in a target object. Using a Compton camera for XFCT can overcome the limitations of mechanical collimators and achieve higher photon collection efficiency. In this work, a Compton camera platform is demonstrated for XFCT imaging, showing its potential for handheld XRF imaging and image-guided interventional operation.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Optics
Ignacio O. Romero, Yile Fang, Michael Lun, Changqing Li
Summary: XFCT is a molecular imaging technique used for sensing elements or nanoparticles in deep samples. A benchtop XFCT imaging system with a novel algorithm was proposed to achieve accurate and fast reconstruction of XFCT images, outperforming traditional algorithms.
Article
Chemistry, Multidisciplinary
Mingjing Cao, Kai Zhang, Shuhan Zhang, Yaling Wang, Chunying Chen
Summary: The interaction between nanomaterials and biology plays a crucial role in the biological behavior and fate of nanoparticles. However, it has been challenging to analyze the transport and transformation of nanomedicines in real-time and label-free. The advancements in advanced light source (ALS) technologies offer new opportunities for studying the behavior and fate of nanomedicines in vivo.
ACS CENTRAL SCIENCE
(2022)
Article
Chemistry, Multidisciplinary
Mingjing Cao, Kai Zhang, Shuhan Zhang, Yaling Wang, Chunying Chen
Summary: Understanding the biological behavior and fate of nanomedicines is crucial for their design and clinical translation, and advanced light source (ALS) analytical technologies, with higher spatial and temporal resolution, multimodal data fusion, and intelligent prediction abilities, have the potential to unlock this knowledge. However, current approaches face challenges and limitations.
ACS CENTRAL SCIENCE
(2022)
Article
Engineering, Electrical & Electronic
Nathanael Six, Jens Renders, Jan Sijbers, Jan De Beenhouwer
Summary: The proposed method uses quasi-Newton methods to minimize a polychromatic objective function without the need to segment the image into different material regions. Experimental and simulated data were used to investigate reconstruction quality and projection error, showing that quasi-Newton methods outperform other statistical or algebraic reconstruction techniques. Among the considered quasi-Newton methods, Gauss-Newton-Krylov was found to perform the best. Compared to a recently proposed polychromatic algebraic reconstruction technique, quasi-Newton solvers achieved lower reconstruction error and increased convergence speed.
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
(2021)
Article
Engineering, Environmental
Ian Byrnes, Lisa Magdalena Rossbach, Jakub Jaroszewicz, Daniel Grolimund, Dario Ferreira Sanchez, Miguel A. Gomez-Gonzalez, Gert Nuyts, Estela Reinoso-Maset, Koen Janssens, Brit Salbu, Dag Anders Brede, Ole Christian Lind
Summary: Micro- and nanoscopic X-ray techniques were used to investigate the relationship between uranium tissue distributions and adverse effects on the digestive tract of Daphnia magna. The study showed that exposure to uranium nanoparticles (UNPs) resulted in adverse morphological changes to the midgut and hepatic ceca, as well as abnormal intestinal epithelial cells. High-resolution nano-XRF identified U particulates throughout the midgut and within hepatic ceca cells, coinciding with tissue damages. These findings highlight the importance of disrupted intestinal function as a mode of acute U toxicity in D. magna.
ENVIRONMENTAL SCIENCE & TECHNOLOGY
(2023)
Article
Optics
Xiaoli Luo, Qianqian Ren, Heng Zhang, Cheng Chen, Tao Yang, Xiaowei He, Wu Zhao
Summary: Fluorescence molecular tomography (FMT) is a promising noninvasive imaging technique for detecting tumors in vivo, but its reconstruction is challenged by limited surface fluorescence. This paper proposes a novel reconstruction strategy combining two different emission fluorescent probes and L1-L2 regularization, along with a half-quadratic splitting alternating optimization (HQSAO) iterative algorithm. The results demonstrate that the HQSAO method achieves improved positioning accuracy and morphology distribution in a shorter time, making it advantageous in preserving light source information and suppressing artifacts. Moreover, the introduction of two dominant fluorescent probes facilitates the high-quality reconstruction of dual light sources and enhances the clinical transformation of FMT.
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION
(2023)
Article
Chemistry, Physical
Julien Gonthier, Tilman Rilling, Ernesto Scoppola, Fabian Zemke, Aleksander Gurlo, Peter Fratzl, Wolfgang Wagermaier
Summary: This study used in operando X-ray micro-computed tomography (mu CT) to monitor the progression of the liquid, gaseous, and solid phases of silica gels during ambient pressure drying and spring-back. The findings challenge the common assumption about the penetration of gas during the spring-back effect and show that the emergence of the spring-back effect is correlated to an equal volume fraction of solid, liquid, and gas in the gels.
CHEMISTRY OF MATERIALS
(2023)
Article
Computer Science, Interdisciplinary Applications
Peng Zhang, Chenbin Ma, Fan Song, Tianyi Zhang, Yangyang Sun, Youdan Feng, Yufang He, Fei Liu, Daifa Wang, Guanglei Zhang
Summary: This study proposes a novel dual-domain joint strategy for accurate and robust fluorescence molecular tomography (FMT) reconstruction. The proposed method outperforms traditional and cutting-edge methods in terms of positioning accuracy, image contrast, robustness, and target morphological recovery. It has great potential to facilitate precise localization and 3D visualization of tumors in in vivo animal experiments.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Hadley DeBrosse, Talon Chandler, Ling Jian Meng, Patrick La Riviere
Summary: X-ray fluorescence emission tomography (XFET) is an emerging imaging modality that images the spatial distribution of metal without requiring biochemical modification or radioactivity. This research investigates the joint estimation of metal and attenuation maps with a pencil-beam XFET system. Two image reconstruction methods for joint estimation are developed and compared using simulated data. The alternating approach outperforms the linearized approach for reconstructing iron and gold numerical phantoms.
IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES
(2023)
Article
Mechanics
Alp Karakoc, Arttu Miettinen, Jussi Virkajarvi, Roberts Joffe
Summary: This study proposes a 3D computational homogenization method based on X-ray microcomputed tomography to investigate the effective elastic properties of regenerated cellulose fiber-polylactic acid biocomposites. The method uses Euclidean bipartite matching technique to eliminate artificial periodic boundaries and a reconstruction algorithm to reduce the time and labor required for fiber labeling. The study presents a case study to compare and validate the method with experimental investigations, providing insights into the influences of individual fibers and their networks on the effective elastic properties.
COMPOSITE STRUCTURES
(2021)
Article
Materials Science, Multidisciplinary
Hima Haridevan, Deborah Barkauskas, Kamil A. Sokolowski, David A. C. Evans, Darren J. Martin, Pratheep K. Annamalai
Summary: This study utilizes X-ray computed microtomographic (X-ray μCT) analysis to investigate the microstructural changes induced by the dispersion of kraft lignin in rigid polyurethane foam. The results show that lignin has a significant impact on the microstructure of the foam. The methodology used in this study can provide valuable insights into the microstructure-property relationship in cellular plastics.
ACS APPLIED POLYMER MATERIALS
(2023)
Article
Materials Science, Characterization & Testing
Kyle M. Champley, Trevor M. Willey, Hyojin Kim, Karina Bond, Steven M. Glenn, Jerel A. Smith, Jeffrey S. Kallman, William D. Brown, Isaac M. Seetho, Lionel Keene, Stephen G. Azevedo, Larry D. McMichael, George Overturf, Harry E. Martz
Summary: This paper introduces Livermore Tomography Tools (LTT), a customizable scientific software package for computed tomography (CT) research. LTT is capable of processing various CT data accurately and rapidly, with support for multiple CPUs and GPUs. It offers 88 algorithms for pre-processing, reconstruction, post-processing, and simulation, and supports different scanner geometries. Several applications demonstrate LTT's accuracy, speed, and flexibility compared to other solutions.
NDT & E INTERNATIONAL
(2022)
Article
Engineering, Electrical & Electronic
Dago Gursoy, Yu-chen Karen Chen-Wiegart, Chris Jacobsen
Summary: This research introduces the scientific problems, imaging approaches, and reconstruction methods in lensless X-ray nanoimaging, highlighting opportunities for future advances.
IEEE SIGNAL PROCESSING MAGAZINE
(2022)
Article
Instruments & Instrumentation
Everett Vacek, Chris Jacobsen
Summary: Accurate center of rotation localization is crucial for high-quality tomographic reconstruction. A simple method based on Fourier transform symmetry is proposed in this study, which is fast and robust against noise and slight deviations in projection angles.
JOURNAL OF SYNCHROTRON RADIATION
(2022)
Article
Chemistry, Physical
Jonathan Schwartz, Zichao Wendy Di, Yi Jiang, Alyssa J. Fielitz, Don-Hyung Ha, Sanjaya D. Perera, Ismail El Baggari, Richard D. Robinson, Jeffrey A. Fessler, Colin Ophus, Steve Rozeveld, Robert Hovden
Summary: Efforts to map atomic-scale chemistry at low doses with minimal noise using electron microscopes are fundamentally limited by inelastic interactions. Fused multi-modal electron microscopy offers a solution to recover high signal-to-noise ratio (SNR) of material chemistry at nano- and atomic-resolution, enabling imaging of the chemical distribution within nanomaterials at significantly lower doses.
NPJ COMPUTATIONAL MATERIALS
(2022)
Article
Optics
Everett Vacek, Curt Preissner, Junjing Deng, Chris Jacobsen
Summary: Scanning of lightweight circular diffractive optics, separate from central stops and apertures, can achieve larger scan ranges in synchrotron x-ray sources with only about a 10% increase in focal spot width. Criteria for the working distance between the last aperture and the specimen are presented for large scanning ranges.
Article
Engineering, Multidisciplinary
Jeffrey S. Eldred, Jeffrey Larson, Misha Padidar, Eric Stern, Stefan M. Wild
Summary: We develop and solve a constrained optimization model for designing an integrable optics rapid-cycling synchrotron lattice that performs well in various capacities. We detail the difficulties of optimizing in a 32-dimensional decision space and use a derivative-free manifold sampling algorithm for optimization.
OPTIMIZATION AND ENGINEERING
(2023)
Article
Operations Research & Management Science
Jeffrey Larson, Misha Padidar, Stefan M. Wild
Summary: In this study, we propose novel approaches to solve nonlinear optimization problems with unrelaxable bound constraints. We reformulate the problem with bound constraints into an unconstrained optimization problem, allowing the use of existing unconstrained optimization methods. We introduce a domain warping to create a merit function, where the choice of the warping determines the accuracy with which the unconstrained problem can find solutions to the bound-constrained problem. Additionally, we develop an algorithm that guarantees finding a stationary point to the desired tolerance by exploiting the structure of the sigmoidal warping.
OPTIMIZATION LETTERS
(2023)
Article
Computer Science, Software Engineering
Raghu Bollapragada, Stefan M. M. Wild
Summary: In this paper, we study unconstrained stochastic optimization problems without available gradient information and propose an adaptive sampling quasi-Newton method. We estimate gradients using finite differences of stochastic function evaluations within a common random number framework. We improve norm test and inner product quasi-Newton test to control the sample sizes used in the stochastic approximations and provide global convergence results to the neighborhood of a locally optimal solution. Numerical experiments on simulation optimization problems show that our algorithm outperforms classical zeroth-order stochastic gradient methods in terms of the number of stochastic function evaluations required.
MATHEMATICAL PROGRAMMING COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
A. Ciprijanovic, A. Lewis, K. Pedro, S. Madireddy, B. Nord, G. N. Perdue, S. M. Wild
Summary: Artificial intelligence methods have potential in improving work efficiency with large astronomical datasets, but they struggle with non-robust features due to the complexity of the methods, resulting in poor generalization across different datasets. To overcome this challenge, we propose DeepAstroUDA, a universal domain adaptation method, which performs semi-supervised domain adaptation for datasets with different distributions and class overlaps. This method bridges the gap between different astronomical surveys, improving classification accuracy and consistency across domains.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2023)
Article
Engineering, Multidisciplinary
Sebastien Le Digabel, Stefan M. Wild
Summary: This article introduces a characterization of constraints to address the differences in constraints encountered in black-box simulation-based optimization problems from those in nonlinear programming. The authors provide formal definitions for several constraint classes and give illustrative examples within the resulting taxonomy. Named KARQ, this taxonomy is useful for modeling, problem formulation, optimization software development and deployment, as well as for facilitating dialogue with practitioners to solve optimization problems.
OPTIMIZATION AND ENGINEERING
(2023)
Article
Statistics & Probability
Ozge Surer, Matthew Plumlee, Stefan M. Wild
Summary: This article presents a sequential framework and a novel criterion for parameter selection in the calibration of simulation models for critical systems. The proposed method improves the efficiency of the calibration process by using intelligent and adaptive selection of parameters to build an emulator. It has been validated through several simulation experiments and a nuclear physics reaction model.
Editorial Material
Instruments & Instrumentation
Junjing Deng, Antonino Miceli, Chris Jacobsen
JOURNAL OF SYNCHROTRON RADIATION
(2023)
Article
Optics
Lucas Slattery, Ruslan Shaydulin, Shouvanik Chakrabarti, Marco Pistoia, Sami Khairy, Stefan M. Wild
Summary: Quantum machine learning techniques, especially quantum kernel methods, are considered promising for achieving practical quantum advantage. However, the flattening spectrum issue in quantum kernels as the number of qubits grows hinders their generalization, requiring the control of hyperparameters to adjust the inductive bias. Our research shows that the hyperparameter-tuning techniques used to improve quantum kernel generalization actually approximate the kernel with a classical kernel, eliminating the possibility of quantum advantage. Extensive numerical evidence using various quantum feature maps and both synthetic and real data supports our findings.
Article
Physics, Nuclear
Dananjaya Liyanage, Ozge Surer, Matthew Plumlee, Stefan M. Wild, Ulrich Heinz
Summary: Due to large pressure gradients in early relativistic heavy-ion collisions, standard hydrodynamic model simulations become reliable only after a certain period of time. In order to address this issue, a prehydrodynamic stage can be introduced to model the early evolution microscopically. Alternatively, the recently developed viscous anisotropic hydrodynamics (VAH) can be used to handle fluids with large anisotropic pressure gradients. This study presents a Bayesian calibration of the VAH model using experimental data from Pb-Pb collisions, demonstrating its unique capability to constrain the specific viscosities of the quark-gluon plasma at higher temperatures compared to other models previously used.
Article
Instruments & Instrumentation
Manuel B. Valentin, Giuseppe Di Guglielmo, Danny Noonan, Priyanka Dilip, Panpan Huang, Adam Quinn, Thomas Zimmerman, Davide Braga, Seda Ogrenci, Chris Jacobsen, Nhan Tran, Farah Fahim
Summary: Integrating neural networks for data compression in ROICs can overcome the I/O bottleneck. Comparing two compression engines, PCA and AE, in our test chip, both achieve high compression rates but introduce latency and increase pixel area.
NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
(2023)
Article
Materials Science, Multidisciplinary
Sajid Ali, Matthew Otten, Z. W. Di
Summary: This paper presents a model-driven approach that optimizes the reconstructed specimen and sinogram alignment as a single optimization problem for tomographic reconstruction with center of rotation error correction. The algorithm uses an adaptive regularizer and has shown robustness to noise and experimental drifts in large-scale synthetic problems.
COMMUNICATIONS MATERIALS
(2022)
