Narrow Results

Selecting a suitable Multi Criteria Decision-Making (MCDM) method is a crucial step in selecting appropriate medical equipment. The aim of the research is to define the most appropriate tomography equipment through the integration of the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. A hybrid model is presented. The AHP is used to define the weights of each criterion and sub-criterion through qualitative comparisons. Then, TOPSIS is used to evaluate the purchase options. This research provides decision makers with a scientific and rigorous decision support system useful in strategic and complex decision. A numerical example is also presented.

Learning-based methods have been proved to perform well in a variety of areas in the biomedical field, such as biomedical image segmentation, and histopathological image analysis. Deep learning, as the most recently presented approach of learning-based methods, has attracted more and more attention. For instance, massive researches of deep learning methods for image reconstructions of computed tomography (CT) and magnetic resonance imaging (MRI) have been reported, indicating the great potential of deep learning for inverse problems. Optical technology-related medical imaging modalities including diffuse optical tomography (DOT), fluorescence molecular tomography (FMT), bioluminescence tomography (BLT), and photoacoustic tomography (PAT) are also dramatically innovated by introducing learning-based methods, in particular deep learning methods, to obtain better reconstruction results. This review depicts the latest researches on learning-based optical tomography of DOT, FMT, BLT, and PAT. According to the most recent studies, learning-based methods applied in the field of optical tomography are categorized as kernel-based methods and deep learning methods. In this review, the former are regarded as a sort of conventional learning-based methods and the latter are subdivided into model-based methods, post-processing methods, and end-to-end methods. Algorithm as well as data acquisition strategy are discussed in this review. The evaluations of these methods are summarized to illustrate the performance of deep learning-based reconstruction.

We present for the first time in vivo imaging of rat brain using microwave-induced thermoacoustic tomography (TAT). The in vivo imaging of rat brain was realized through an unconventional delivery of microwave energy from the front of rat brain (while the transducer was scanned along coronal plane of the animal brain), which maximized the microwave penetration into the brain. In addition, we found that the imaging contrast was highly dependent on the direction of the electric field polarization (EFP) and that more tissue structures/compositions could be revealed when both $X$- and $Y$-EFPs were used for TAT. The in vivo TAT images of rat brain obtained were compared with the 3.0 T MRI images and histological photographs, and numerous important brain anatomical structures were identified. An example of our TAT approach for imaging a foreign object embedded in a rat brain was also demonstrated. This study suggests that TAT has a great potential to be used in neuroscience studies and in noninvasive imaging of brain disorders.

The present work includes the analysis of the porosity at different scales using image characterization techniques. Porosities were determined and compared for reservoir rocks through the fractal dimensions obtained from two-dimensional (2D) image analysis. Studies were developed using Optical Microscopy (OM), Scanning Electron Microscopy (SEM) and X-ray Computed Tomography (XCT). In order to compare the images and analyze the similarities in the porosities, the box-counting method was used to extract the power-law distributions and to obtain the fractal dimensions. Results showed that fractal dimensions were similar for the three different techniques, which included different scale analysis, fact that demonstrates the fractal character of the porosity in the studied systems. The effectiveness of the use of 2D image analysis and the importance of the multiscale study of the porosity were also demonstrated.

In this paper, we consider the use of blind deconvolution for optoacoustic (photoacoustic) imaging and investigate the performance of the method as means for increasing the resolution of the reconstructed image beyond the physical restrictions of the system. The method is demonstrated with optoacoustic measurement obtained from six-day-old mice, imaged in the near-infrared using a broadband hydrophone in a circular scanning configuration. We find that estimates of the unknown point spread function, achieved by blind deconvolution, improve the resolution and contrast in the images and show promise for enhancing optoacoustic images.

The momentum and position observables in an $n$-mode boson Fock space $\mathrm{\Gamma }({ℂ}^n)$ have the whole real line $ℝ$ as their spectrum. But the total number operator $N$ has a discrete spectrum ${\mathbb{Z}}_{+}=\{0,1,2,\dots \}$. An $n$-mode Gaussian state in $\mathrm{\Gamma }({ℂ}^n)$ is completely determined by the mean values of momentum and position observables and their covariance matrix which together constitute a family of $n(2n+3)$ real parameters. Starting with $N$ and its unitary conjugates by the Weyl displacement operators and operators from a representation of the symplectic group $\mathrm{\text{Sp}}(2n)$ in $\mathrm{\Gamma }({ℂ}^n)$, we construct $n(2n+3)$ observables with spectrum ${\mathbb{Z}}_{+}$ but whose expectation values in a Gaussian state determine all its mean and covariance parameters. Thus measurements of discrete-valued observables enable the tomography of the underlying Gaussian state and it can be done by using five one-mode and four two-mode Gaussian symplectic gates in single and pair mode wires of $\mathrm{\Gamma }({ℂ}^n)=\mathrm{\Gamma }{(ℂ)}^{\otimes n}$. Thus the tomography protocol admits a simple description in a language similar to circuits in quantum computation theory. Such a Gaussian tomography applied to outputs of a Gaussian channel with coherent input states permit a tomography of the channel parameters. However, in our procedure the number of counting measurements exceeds the number of channel parameters slightly. Presently, it is not clear whether a more efficient method exists for reducing this tomographic complexity.

As a byproduct of our approach an elementary derivation of the probability generating function of $N$ in a Gaussian state is given. In many cases the distribution turns out to be infinitely divisible and its underlying Lévy measure can be obtained. However, we are unable to derive the exact distribution in all cases. Whether this property of infinite divisibility holds in general is left as an open problem.

Particle beam radiography, which uses a variety of particle probes (neutrons, protons, electrons, gammas and potentially other particles) to study the structure of materials and objects noninvasively, is reviewed, largely from an accelerator perspective, although the use of cosmic rays (mainly muons but potentially also high-energy neutrinos) is briefly reviewed. Tomography is a form of radiography which uses multiple views to reconstruct a three-dimensional density map of an object. There is a very wide range of applications of radiography and tomography, from medicine to engineering and security, and advances in instrumentation, specifically the development of electronic detectors, allow rapid analysis of the resultant radiographs. Flash radiography is a diagnostic technique for large high-explosive-driven hydrodynamic experiments that is used at many laboratories. The bremsstrahlung radiation pulse from an intense relativistic electron beam incident onto a high-Z target is the source of these radiographs. The challenge is to provide radiation sources intense enough to penetrate hundreds of g/cm² of material, in pulses short enough to stop the motion of high-speed hydrodynamic shocks, and with source spots small enough to resolve fine details. The challenge has been met with a wide variety of accelerator technologies, including pulsed-power-driven diodes, air-core pulsed betatrons and high-current linear induction accelerators. Accelerator technology has also evolved to accommodate the experimenters' continuing quest for multiple images in time and space. Linear induction accelerators have had a major role in these advances, especially in providing multiple-time radiographs of the largest hydrodynamic experiments.

We propose searching for deep underground cavities of different densities in the Earth’s crust using a long-baseline ${\bar{\nu }}_e$ disappearance experiment, realized through a low-energy $\beta$-beam with highly-enhanced luminosity. We focus on four cases: cavities with densities close to that of water, iron-banded formations, heavier mineral deposits, and regions of abnormal charge accumulation that have been posited to appear prior to the occurrence of an intense earthquake. The sensitivity to identify cavities attains confidence levels (C.L.s) higher than $3\sigma$ and $5\sigma$ for exposure times of three months and 1.5 years, respectively, and cavity densities below ${\mathrm{\text{1 g cm}}}^{-3}$ or above ${\mathrm{\text{5 g cm}}}^{-3}$, with widths greater than 200 km. We reconstruct the cavity density, width, and position, assuming one of them known while keeping the other two free. We obtain large allowed regions that improve as the cavity density differs more from the Earth’s mean density. Furthermore, we demonstrate that the knowledge of the cavity density is important to obtain O(10%) error on the width. Finally, we introduce an observable to quantify the presence of a cavity by changing the orientation of the ${\bar{\nu }}_e$ beam, with which we are able to identify the presence of a cavity at the $2\sigma$ to $5\sigma$ C.L.

The efficiency of quantum state tomography is discussed from the point of view of quantum parameter estimation theory, in which the trace of the weighted covariance is to be minimized. It is shown that tomography is optimal only when a special weight is adopted.

Fluorescence molecular tomography (FMT) allows the detection and quantification of various biological processes in small animals in vivo, which expands the horizons of pre-clinical research and drug development. Efficient three-dimensional (3D) reconstruction algorithm is the key to accurate localization and quantification of fluorescent target in FMT. In this paper, 3D reconstruction of FMT is regarded as a sparse signal recovery problem and the compressive sampling matching pursuit (CoSaMP) algorithm is adopted to obtain greedy recovery of fluorescent signals. Moreover, to reduce the modeling error, the simplified spherical harmonics approximation to the radiative transfer equation (RTE), more specifically $S{P}_3$, is utilized to describe light propagation in biological tissues. The performance of the proposed reconstruction method is thoroughly evaluated by simulations on a 3D digital mouse model by comparing it with three representative greedy methods including orthogonal matching pursuit (OMP), stagewise OMP(StOMP), and regularized OMP (ROMP). The CoSaMP combined with $S{P}_3$ shows an improvement in reconstruction accuracy and exhibits distinct advantages over the comparative algorithms in multiple targets resolving. Stability analysis suggests that CoSaMP is robust to noise and performs stably with reduction of measurements. The feasibility and reconstruction accuracy of the proposed method are further validated by phantom experimental data.

Image reconstruction in fluorescence molecular tomography involves seeking stable and meaningful solutions via the inversion of a highly under-determined and severely ill-posed linear mapping. An attractive scheme consists of minimizing a convex objective function that includes a quadratic error term added to a convex and nonsmooth sparsity-promoting regularizer. Choosing ${\ell }_1$-norm as a particular case of a vast class of nonsmooth convex regularizers, our paper proposes a low per-iteration complexity gradient-based first-order optimization algorithm for the ${\ell }_1$-regularized least squares inverse problem of image reconstruction. Our algorithm relies on a combination of two ideas applied to the nonsmooth convex objective function: Moreau–Yosida regularization and inertial dynamics-based acceleration. We also incorporate into our algorithm a gradient-based adaptive restart strategy to further enhance the practical performance. Extensive numerical experiments illustrate that in several representative test cases (covering different depths of small fluorescent inclusions, different noise levels and different separation distances between small fluorescent inclusions), our algorithm can significantly outperform three state-of-the-art algorithms in terms of CPU time taken by reconstruction, despite almost the same reconstructed images produced by each of the four algorithms.

A new micro-particle induced X-ray emission-computed tomography (PIXE-CT) system was developed at Takasaki Ion Accelerators for Advanced Radiation Application in Japan Atomic Energy Agency. In this system, scanning transmission ion microscopy-CT was performed as well as PIXE-CT for three-dimensional (3D) measurement of major elements' distributions, which are required for corrections of X-ray yields due to energy losses of projectiles and absorption of X-rays. Moreover, maximum likelihood expectation maximization algorithm has been introduced to image reconstruction because higher spatial resolution can be obtained even with less X-ray yields. Consequently, 3D distribution of trace elements in a minute biological cell less than 100 μm has been successfully obtained.

Micromegas-based detectors are used in a wide variety of neutron experiments. Their fast response meets the needs of time-of-flight facilities in terms of time resolution. The possibility of constructing low mass Micromegas detectors makes them appropriate for beam imaging and monitoring without affecting the beam quality or inducing background in parallel measurements. The good particle discrimination capability allows using Micromegas for neutron induced fission and (n, α) cross-section measurements. Their high radiation resistance make them suitable for working as flux monitors in the core of fission nuclear reactors as well as in the proximity of fusion chambers. New studies underlined the possibility of performing neutron computed tomography (CT) with Micromegas as neutron detectors, but also of exploiting its performances in experiments of fundamental nuclear physics.

Minimal Informationally Complete quantum measurements, or MICs, illuminate the structure of quantum theory and how it departs from the classical. Central to this capacity is their role as tomographically complete measurements with the fewest possible number of outcomes for a given finite dimension. Despite their advantages, little is known about them. We establish general properties of MICs, explore constructions of several classes of them, and make some developments to the theory of MIC Gram matrices. These Gram matrices turn out to be a rich subject of inquiry, relating linear algebra, number theory and probability. Among our results are some equivalent conditions for unbiased MICs, a characterization of rank-1 MICs through the Hadamard product, several ways in which immediate properties of MICs capture the abandonment of classical phase space intuitions, and a numerical study of MIC Gram matrix spectra. We also present, to our knowledge, the first example of an unbiased rank-1 MIC which is not group covariant. This work provides further context to the discovery that the symmetric informationally complete quantum measurements (SICs) are in many ways optimal among MICs. In a deep sense, the ideal measurements of quantum physics are not orthogonal bases.

The following sections are included:

• Introduction

• Basic Approaches
  • Synchrotron X-ray Production
  • X-ray Cross Sections
  • Synchrotron Radiation Induced X-ray Emission (SRIXE)
  • Extended X-ray Absorption Fine Structure (EXAFS) and Extended X-ray Absorption Near Edge Structure (XANES)
  • Computed Microtomography (CMT)
  • Sample Radiation Damage Effects

• Applications
  • Biological Applications
    • Reversible Effect of Low Lead Levels on Neuronal Growth
    • Analysis of Zinc Binding Capabilities of a Protein Domain
    • Stroke and Calcium Concentrations in the Brain
    • Studies of Bone and Cartilage
  • Geological and Extraterrestrial Materials
    • Microstructure of Shock-recovered Berea Sandstone
    • Internal Structure of Two Type-I Deep Sea Spheres by X-ray Computed Microtomography
    • Hydrothermal Vents
    • Investigation of New York/New Jersey Harbor Dredged Material
  • Environmental Chemistry
    • Secondary Ion Mass Spectroscopy and Synchrotron X-ray Fluorescence in the Study of the Qualitative Variation in Metal Content with Time in Tree Rings
  • Materials Analysis
    • Thermal Spray
    • Study of Supported Catalysts
    • Investigation of Integrated Circuit Structures Using CMT and Microdiffraction

• Summary

• Acknowledgments

• References

Recent advances in the technique of terahertz time-domain spectroscopy have led to the development of the first fiber-coupled room-temperature broadband terahertz sources and detectors. The fiber coupling permits the repositioning of the emitter and receiver antennas without loss of temporal calibration or alignment, thus enabling multistatic imaging. We describe a new imaging method which exploits this new capability. This technique emulates the data collection and image processing procedures developed for geophysical prospecting. We use a migration procedure to solve the inverse problem; this permits us to reconstruct the location, shape, and refractive index of targets. We show examples for both metallic and dielectric model targets, and we perform velocity analysis on dielectric targets to estimate the refractive indices of imaged components. These results broaden the capabilities of terahertz imaging systems, and also demonstrate the viability of the THz system as a test bed for the exploration of new seismic processing methods.

Particle beam radiography, which uses a variety of particle probes (neutrons, protons, electrons, gammas and potentially other particles) to study the structure of materials and objects noninvasively, is reviewed, largely from an accelerator perspective, although the use of cosmic rays (mainly muons but potentially also high-energy neutrinos) is briefly reviewed. Tomography is a form of radiography which uses multiple views to reconstruct a three-dimensional density map of an object. There is a very wide range of applications of radiography and tomography, from medicine to engineering and security, and advances in instrumentation, specifically the development of electronic detectors, allow rapid analysis of the resultant radiographs. Flash radiography is a diagnostic technique for large high-explosive-driven hydrodynamic experiments that is used at many laboratories. The bremsstrahlung radiation pulse from an intense relativistic electron beam incident onto a high-Z target is the source of these radiographs. The challenge is to provide radiation sources intense enough to penetrate hundreds of g/cm² of material, in pulses short enough to stop the motion of high-speed hydrodynamic shocks, and with source spots small enough to resolve fine details. The challenge has been met with a wide variety of accelerator technologies, including pulsed-power-driven diodes, air-core pulsed betatrons and high-current linear induction accelerators. Accelerator technology has also evolved to accommodate the experimenters' continuing quest for multiple images in time and space. Linear induction accelerators have had a major role in these advances, especially in providing multiple-time radiographs of the largest hydrodynamic experiments.