Autoregression and Structured Low-Rank Modeling of Sinogram Neighborhoods.

Sinograms are commonly used to represent the raw data from tomographic imaging experiments. Although it is already well-known that sinograms posess some amount of redundancy, in this work, we present novel theory suggesting that sinograms will often possess substantial additional redundancies that have not been explicitly exploited by previous methods. Specifically, we derive that sinograms will often satisfy multiple simple data-dependent autoregression relationships.

High-Throughput, High-Resolution Interferometric Light Microscopy of Biological Nanoparticles.

Label-free, visible light microscopy is an indispensable tool for studying biological nanoparticles (BNPs). However, conventional imaging techniques have two major challenges: (i) weak contrast due to low-refractive-index difference with the surrounding medium and exceptionally small size and (ii) limited spatial resolution. Advances in interferometric microscopy have overcome the weak contrast limitation and enabled direct detection of BNPs, yet lateral resolution remains as a challenge in studying BNP morphology.

Learning-Based Object Identification and Segmentation Using Dual-Energy CT Images for Security.

In recent years, baggage screening at airports has included the use of dual-energy X-ray computed tomography (DECT), an advanced technology for nondestructive evaluation. The main challenge remains to reliably find and identify threat objects in the bag from DECT data. This task is particularly hard due to the wide variety of objects, the high clutter, and the presence of metal, which causes streaks and shading in the scanner images.

Dictionary-based image reconstruction for superresolution in integrated circuit imaging.

Resolution improvement through signal processing techniques for integrated circuit imaging is becoming more crucial as the rapid decrease in integrated circuit dimensions continues. Although there is a significant effort to push the limits of optical resolution for backside fault analysis through the use of solid immersion lenses, higher order laser beams, and beam apodization, signal processing techniques are required for additional improvement. In this work, we propose a sparse image reconstruction framework which couples overcomplete dictionary-based representation with a physics-based forward model to improve resolution and localization accuracy in high numerical aperture confocal microscopy systems for backside optical integrated circuit analysis.

Graph-cut based discrete-valued image reconstruction.

Efficient graph-cut methods have been used with great success for labeling and denoising problems occurring in computer vision. Unfortunately, the presence of linear image mappings has prevented the use of these techniques in most discrete-amplitude image reconstruction problems. In this paper, we develop a graph-cut based framework for the direct solution of discrete amplitude linear image reconstruction problems cast as regularized energy function minimizations.

High fidelity system modeling for high quality image reconstruction in clinical CT.

Today, while many researchers focus on the improvement of the regularization term in IR algorithms, they pay less concern to the improvement of the fidelity term. In this paper, we hypothesize that improving the fidelity term will further improve IR image quality in low-dose scanning, which typically causes more noise. The purpose of this paper is to systematically test and examine the role of high-fidelity system models using raw data in the performance of iterative image reconstruction approach minimizing energy functional.

An interferometric reflectance imaging sensor for point of care viral diagnostics.

The use of in vitro diagnostic devices is transitioning from the laboratory to the primary care setting to address early disease detection needs. Time critical viral diagnoses are often made without support due to the experimental time required in today's standard tests. Available rapid point of care (POC) viral tests are less reliable, requiring a follow-on confirmatory test before conclusions can be drawn.

Sparsity driven ultrasound imaging.

An image formation framework for ultrasound imaging from synthetic transducer arrays based on sparsity-driven regularization functionals using single-frequency Fourier domain data is proposed. The framework involves the use of a physics-based forward model of the ultrasound observation process, the formulation of image formation as the solution of an associated optimization problem, and the solution of that problem through efficient numerical algorithms. The sparsity-driven, model-based approach estimates a complex-valued reflectivity field and preserves physical features in the scene while suppressing spurious artifacts.

Metabolite proving fungal cleavage of the aromatic core part of a fluoroquinolone antibiotic.

Liquid cultures of the basidiomycetous fungus Gloeophyllum striatum were employed to study the biodegradation of pradofloxacin, a new veterinary fluoroquinolone antibiotic carrying a CN group at position C-8. After 16 days of incubation, metabolites were purified by micro-preparative high-performance liquid chromatography. Four metabolites could be identified by co-chromatography with chemically synthesized standards.

A decomposition-based CT reconstruction formulation for reducing blooming artifacts.

Cardiac computed tomography represents an important advancement in the ability to assess coronary vessels. The accuracy of these non-invasive imaging studies is limited, however, by the presence of calcium, since calcium blooming artifacts lead to an over-estimation of the degree of luminal narrowing. To address this problem, we have developed a unified decomposition-based iterative reconstruction formulation, where different penalty functions are imposed on dense objects (i.

Extracellular serine proteases from Stenotrophomonas maltophilia: Screening, isolation and heterologous expression in E. coli.

A large strain collection comprising antagonistic bacteria was screened for novel detergent proteases. Several strains displayed protease activity on agar plates containing skim milk but were inactive in liquid media. Encapsulation of cells in alginate beads induced protease production.

C-terminal truncation of a metagenome-derived detergent protease for effective expression in E. coli.

Recently, a new alkaline protease named HP70 showing highest homology to extracellular serine proteases of Stenotrophomonas maltophilia and Xanthomonas campestris was found in the course of a metagenome screening for detergent proteases (Niehaus et al., submitted for publication). Attempts to efficiently express the enzyme in common expression hosts had failed.

Engineering of choline oxidase from Arthrobacter nicotianae for potential use as biological bleach in detergents.

In order to engineer the choline oxidase from Arthrobacter nicotianae (An_CodA) for the potential application as biological bleach in detergents, the specific activity of the enzyme toward the synthetic substrate tris-(2-hydroxyethyl)-methylammonium methylsulfate (MTEA) was improved by methods of directed evolution and rational design. The best mutants (up to 520% wt-activity with MTEA) revealed mutations in the FAD- (A21V, G62D, I69V) and substrate-binding site (S348L, V349L, F351Y). In a separate screening of a library comprising of randomly mutagenised An_CodA, with the natural substrate choline, four mutations were identified, which were further combined in one clone.

A spatio-temporal deconvolution method to improve perfusion CT quantification.

Perfusion imaging is a useful adjunct to anatomic imaging in numerous diagnostic and therapy-monitoring settings. One approach to perfusion imaging is to assume a convolution relationship between a local arterial input function and the tissue enhancement profile of the region of interest via a "residue function" and subsequently solve for this residue function. This ill-posed problem is generally solved using singular-value decomposition based approaches, and the hemodynamic parameters are solved for each voxel independently.

Heterologous expression and characterization of choline oxidase from the soil bacterium Arthrobacter nicotianae.

In the course of a microbial screening of soil samples for new oxidases, different enrichment strategies were carried out. With choline as the only carbon source, a microorganism was isolated and identified as Arthrobacter nicotianae. From this strain, a gene coding for a choline oxidase was isolated from chromosomal DNA.

Unified anomaly suppression and boundary extraction in laser radar range imagery based on a joint curve-evolution and expectation-maximization algorithm.

In this paper, we develop a new unified approach for laser radar range anomaly suppression, range profiling, and segmentation. This approach combines an object-based hybrid scene model for representing the range distribution of the field and a statistical mixture model for the range data measurement noise. The image segmentation problem is formulated as a minimization problem which jointly estimates the target boundary together with the target region range variation and background range variation directly from the noisy and anomaly-filled range data.

A real-time algorithm for the approximation of level-set-based curve evolution.

In this paper, we present a complete and practical algorithm for the approximation of level-set-based curve evolution suitable for real-time implementation. In particular, we propose a two-cycle algorithm to approximate level-set-based curve evolution without the need of solving partial differential equations (PDEs). Our algorithm is applicable to a broad class of evolution speeds that can be viewed as composed of a data-dependent term and a curve smoothness regularization term.

A multiscale hypothesis testing approach to anomaly detection and localization from noisy tomographic data.

In this paper, we investigate the problems of anomaly detection and localization from noisy tomographic data. These are characteristic of a class of problems that cannot be optimally solved because they involve hypothesis testing over hypothesis spaces with extremely large cardinality. Our multiscale hypothesis testing approach addresses the key issues associated with this class of problems.

Multiscale methods for the segmentation and reconstruction of signals and images.

This paper addresses the problem of both segmenting and reconstructing a noisy signal or image. The work is motivated by large problems arising in certain scientific applications, such as medical imaging. Two objectives for a segmentation and denoising algorithm are laid out: it should be computationally efficient and capable of generating statistics for the errors in the reconstruction and estimates of the boundary locations.

An efficient region of interest acquisition method for dynamic magnetic resonance imaging.

Motivated by work in the area of dynamic magnetic resonance imaging (MRI), we develop a new approach to the problem of reduced-order MRI acquisition. Efforts in this field have concentrated on the use of Fourier and singular value decomposition (SVD) methods to obtain low-order representations of an entire image plane. We augment this work to the case of imaging an arbitrarily-shaped region of interest (ROI) embedded within the full image.

Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization.

We develop a method for the formation of spotlight-mode synthetic aperture radar (SAR) images with enhanced features. The approach is based on a regularized reconstruction of the scattering field which combines a tomographic model of the SAR observation process with prior information regarding the nature of the features of interest. Compared to conventional SAR techniques, the method we propose produces images with increased resolution, reduced sidelobes, reduced speckle and easier-to-segment regions.

A curve evolution approach to object-based tomographic reconstruction.

In this paper, we develop a new approach to tomographic reconstruction problems based on geometric curve evolution techniques. We use a small set of texture coefficients to represent the object and background inhomogeneities and a contour to represent the boundary of multiple connected or unconnected objects. Instead of reconstructing pixel values on a fixed rectangular grid, we then find a reconstruction by jointly estimating these unknown contours and texture coefficients of the object and background.

4Pi spectral self-interference microscopy.

Spectral self-interference microscopy (SSM) relies on the balanced collection of light traveling two different paths from the sample to the detector, one direct and the other indirect from a reflecting substrate. The resulting spectral interference effects allow nanometer-scale axial localization of isolated emitters. To produce spectral fringes the difference between the two optical paths must be significant.

Spectral self-interference microscopy for low-signal nanoscale axial imaging.

A theoretical and numerical analysis of spectral self-interference microscopy (SSM) is presented with the goal of expanding the realm of SSM applications. In particular, this work is intended to enable SSM imaging in low-signal applications such as single-molecule studies. A comprehensive electromagnetic model for SSM is presented, allowing arbitrary forms of the excitation field, detection optics, and tensor sample response.

The sources of sodium escaping from Io revealed by spectral high definition imaging.

On Jupiter's moon Io, volcanic plumes and evaporating lava flows provide hot gases to form an atmosphere that is subsequently ionized. Some of Io's plasma is captured by the planet's strong magnetic field to form a co-rotating torus at Io's distance; the remaining ions and electrons form Io's ionosphere. The torus and ionosphere are also depleted by three time-variable processes that produce a banana-shaped cloud orbiting with Io, a giant nebula extending out to about 500 Jupiter radii, and a jet close to Io.