Scatter Reduction By Exploiting Behaviour of Convolutional Neural Networks in Frequency Domain

In X-ray imaging, scattered radiation can produce a number of artifacts that greatly undermine the image quality. There are hardware solutions, such as anti-scatter grids. However, they are costly. A software-based solution is a better option because it is cheaper and can achieve a higher scatter reduction. Most of the current software-based approaches are model-based. The main issues with them are the lack of flexibility, expressivity, and the requirement of a model. In consideration of this, we decided to apply Convolutional Neural Networks (CNNs), since they do not have any of the previously mentioned issues. In our approach we split the image into three frequency bands: low, high low and high high and process each of them separately with a CNN. Then, we downsample the low frequency band and upsample the high frequency band, so that the frequency is increased and decreased respectively. Finally, we train three CNNs with each of the components and put them back together to have the reconstruction of the image. We demonstrate theoretically that doing this leads to better results, and provide comprehensive empirical evidence of the capability of our algorithm for doing scatter correction

Invisibility and Cloaking: Origins, Present, and Future Perspectives

The development of metamaterials, i.e., artificially structured materials that interact with waves in unconventional ways, has revolutionized our ability to manipulate the propagation of electromagnetic waves and their interaction with matter. One of the most exciting applications of metamaterial science is related to the possibility of totally suppressing the scattering of an object using an invisibility cloak. Here, we review the available methods to make an object undetectable to electromagnetic waves, and we highlight the outstanding challenges that need to be addressed in order to obtain a fully functional coating capable of suppressing the total scattering of an object. Our outlook discusses how, while passive linear cloaks are fundamentally limited in terms of bandwidth of operation and overall scattering suppression, active and/or nonlinear cloaks hold the promise to overcome, at least partially, some of these limitations.AFOSR Award FA9550-13-1-0204NSF CAREER Award ECCS-0953311DTRA YIP Award HDTRA1-12-1-0022Electrical and Computer Engineerin

A Spectral CT Method to Directly Estimate Basis Material Maps From Experimental Photon-Counting Data

The proposed spectral CT method solves the constrained one-step spectral CT reconstruction (cOSSCIR) optimization problem to estimate basis material maps while modeling the nonlinear X-ray detection process and enforcing convex constraints on the basis map images. In order to apply the optimization-based reconstruction approach to experimental data, the presented method empirically estimates the effective energy-window spectra using a calibration procedure. The amplitudes of the estimated spectra were further optimized as part of the reconstruction process to reduce ring artifacts. A validation approach was developed to select constraint parameters. The proposed spectral CT method was evaluated through simulations and experiments with a photon-counting detector. Basis material map images were successfully reconstructed using the presented empirical spectral modeling and cOSSCIR optimization approach. In simulations, the cOSSCIR approach accurately reconstructed the basis map images

Fullwave design of cm-scale cylindrical metasurfaces via fast direct solvers

Large-scale metasurfaces promise nanophotonic performance improvements to macroscopic optics functionality, for applications from imaging to analog computing. Yet the size scale mismatch of centimeter-scale chips versus micron-scale wavelengths prohibits use of conventional full-wave simulation techniques, and has necessitated dramatic approximations. Here, we show that tailoring "fast direct" integral-equation simulation techniques to the form factor of metasurfaces offers the possibility for accurate and efficient full-wave, large-scale metasurface simulations. For cylindrical (two-dimensional) metasurfaces, we demonstrate accurate simulations whose solution time scales \emph{linearly} with the metasurface diameter. Moreover, the solver stores compressed information about the simulation domain that is reusable over many design iterations. We demonstrate the capabilities of our solver through two designs: first, a high-efficiency, high-numerical-aperture metalens that is 20,000 wavelengths in diameter. Second, a high-efficiency, large-beam-width grating coupler. The latter corresponds to millimeter-scale beam design at standard telecommunications wavelengths, while the former, at a visible wavelength of 500 nm, corresponds to a design diameter of 1 cm, created through full simulations of Maxwell's equations.Comment: 11 pages, 6 figure

CLASH: Mass Distribution in and around MACS J1206.2-0847 from a Full Cluster Lensing Analysis

We derive an accurate mass distribution of the galaxy cluster MACS J1206.2-0847 (z=0.439) from a combined weak-lensing distortion, magnification, and strong-lensing analysis of wide-field Subaru BVRIz' imaging and our recent 16-band Hubble Space Telescope observations taken as part of the Cluster Lensing And Supernova survey with Hubble (CLASH) program. We find good agreement in the regions of overlap between several weak and strong lensing mass reconstructions using a wide variety of modeling methods, ensuring consistency. The Subaru data reveal the presence of a surrounding large scale structure with the major axis running approximately north-west south-east (NW-SE), aligned with the cluster and its brightest galaxy shapes, showing elongation with a \sim 2:1 axis ratio in the plane of the sky. Our full-lensing mass profile exhibits a shallow profile slope dln\Sigma/dlnR\sim -1 at cluster outskirts (R>1Mpc/h), whereas the mass distribution excluding the NW-SE excess regions steepens further out, well described by the Navarro-Frenk-White form. Assuming a spherical halo, we obtain a virial mass M_{vir}=(1.1\pm 0.2\pm 0.1)\times 10^{15} M_{sun}/h and a halo concentration c_{vir} = 6.9\pm 1.0\pm 1.2 (\sim 5.7 when the central 50kpc/h is excluded), which falls in the range 4 <7 of average c(M,z) predictions for relaxed clusters from recent Lambda cold dark matter simulations. Our full lensing results are found to be in agreement with X-ray mass measurements where the data overlap, and when combined with Chandra gas mass measurements, yield a cumulative gas mass fraction of 13.7^{+4.5}_{-3.0}% at 0.7Mpc/h (\approx 1.7r_{2500}), a typical value observed for high mass clusters.Comment: Accepted by ApJ (30 pages, 17 figures), one new figure (Figure 10) added, minor text changes; a version with high resolution figures available at http://www.asiaa.sinica.edu.tw/~keiichi/upfiles/MACS1206/ms_highreso.pd

Multi-Energy Blended CBCT Spectral Imaging Using a Spectral Modulator with Flying Focal Spot (SMFFS)

Cone-beam CT (CBCT) spectral imaging has great potential in medical and industrial applications, but it is very challenging as scatter and spectral effects are seriously twisted. In this work, we present the first attempt to develop a stationary spectral modulator with flying focal spot (SMFFS) technology as a promising, low-cost approach to accurately solving the X-ray scattering problem and physically enabling spectral imaging in a unified framework, and with no significant misalignment in data sampling of spectral projections. Based on an in-depth analysis of optimal energy separation from different combinations of modulator materials and thicknesses, we present a practical design of a mixed two-dimensional spectral modulator that can generate multi-energy blended CBCT spectral projections. To deal with the twisted scatter-spectral challenge, we propose a novel scatter-decoupled material decomposition (SDMD) method by taking advantage of a scatter similarity in SMFFS. A Monte Carlo simulation is conducted to validate the strong similarity of X-ray scatter distributions across the flying focal spot positions. Both numerical simulations using a clinical abdominal CT dataset, and physics experiments on a tabletop CBCT system using a GAMMEX multi-energy CT phantom, are carried out to demonstrate the feasibility of our proposed SDMD method for CBCT spectral imaging with SMFFS. In the physics experiments, the mean relative errors in selected ROI for virtual monochromatic image (VMI) are 0.9\% for SMFFS, and 5.3\% and 16.9\% for 80/120 kV dual-energy cone-beam scan with and without scatter correction, respectively. Our preliminary results show that SMFFS can effectively improve the quantitative imaging performance of CBCT.Comment: 10 pages, 13 figure

Peridynamic Approaches for Damage Prediction in Carbon Fiber and Carbon Nanotube Yarn Reinforced Polymer Composites

Aerospace structures are increasingly utilizing advanced composites because of their high specific modulus and specific strength. While the introduction of these material systems can dramatically decrease weight, they pose unique certification challenges, often requiring extensive experimental testing in each stage of the design cycle. The expensive and time-consuming nature of experimental testing necessitates the advancement of simulation methodologies to both aid in the certification process and assist in the exploration of the microstructure design space. Peridynamic (PD) theory, originating from Sandia National Lab’s in the early 2000’s, is a nonlocal continuum-based method that reformulates the equation of motion into an integral equivalent form. The integral form, on which the theory is based, is well suited to explore discontinuity rich phenomena such as damage and material failure. This dissertation develops PD-based simulation approaches to investigate two polymer based composite material systems of different maturity: carbon fiber and carbon nanotube (CNT) yarn. For carbon fiber reinforced composites, simulation approaches were developed to predict damage resulting from low-velocity impact, an important part of the certification process because often damage associated with this loading goes undetected leading to premature structural failure. In contrast to the more established carbon fiber, CNT yarn is a promising constituent material still very much in the developmental process. With this in mind, PD simulation approaches were developed with a different objective, which was to systematically explore microstructure property relationships, providing early feedback in the material design process

Regularized Multigrid Optimization for Material Reconstruction from Single Medical X-ray Images

This thesis presents a novel technique for the estimation of 3D structural and material composition of anatomies imaged with X-rays. These estimates are produced from a single image with associated X-ray detector data. This method is made possible with access to software for X-ray simulation and segmentation, both developed by and provided to us by IBEX Innovations. This work combines existing concepts from optimization and multi-grid methods to present a novel concept for using domain knowledge to sufficiently constrain an otherwise unsolvable problem to produce valuable output. Specifically, it is shown that by transforming knowledge about the shape and composition of anatomies into regularizing functions, we can produce models of their internal structure that are accurate enough to simulate X-ray scatter, and thereby remove noise from the final images in a physics-guided way. By introducing weighted penalties for results that do not conform to expectations from domain knowledge, which are informed by IBEX’s neural network for X-ray segmentation, we can estimate the shape and material composition of a 3D object from a single image which - in theory - does not contain enough information to produce such a model. This work makes use of an X-ray simulation tool and associated data created by IBEX innovations and provided to us. We have created an optimization algorithm that iteratively processes this data with the IBEX simulation tool, then updates the estimated material composition of the imaged anatomy by imposing regularizing functions that penalize models that do not conform to our expectations about real anatomies. This is implemented on multi-grid, showing improved reconstruction quality and speed by producing coarse models first, followed by a custom algorithm for optimally selecting coarsening and refining of the model to produce the most accurate model. By using IBEX’s simulation algorithm, we show that we can constrain an otherwise ill-posed problem. These novel tools allow us to solve the problem of estimating 3D material composition from a single image, by considering simple features of organic shapes such as continuity and smoothness. We demonstrate that with access to sufficiently powerful simulation tools, even simple assumptions about our target facilitate intuitive material estimations. The algorithm presented in this thesis has certain limitations. We are only able to produce models of anatomies at low resolutions, constructed of just two distinct materials, without fully capturing the 3D structure of the anatomy. Nonetheless, we demonstrate that it is possible to capture enough structural information to produce an accurate scatter estimate, which would not be possible without the research we present here. These limitations are imposed to simplify our solutions such that they can be found using conventional hardware, and to constrain our problem into the scope of feasibility. Furthermore, the choice to limit our models to 2.5D and just two materials reflects the models used by IBEX Innovations and their X-ray simulation method, which we require for our optimization. To our understanding, no other published work in this field has applied an approach like ours to X-ray image reconstruction. Inferring from a single image not just depth information, but also an abstraction of information about the internal structure, in a way that is physically motivated. We hope that this concept could be applied to other problems in future, where systems are well understood but hindered by limited data availability or high capture costs