Motion Compensated Ultrasound Imaging Allows Thermometry and Image Guided Drug Delivery Monitoring from Echogenic Liposomes.

Ultrasound imaging is widely used both for cancer diagnosis and to assess therapeutic success, but due to its weak tissue contrast and the short half-life of commercially available contrast agents, it is currently not practical for assessing motion compensated contrast-enhanced tumor imaging, or for determining time-resolved absolute tumor temperature while simultaneously reporting on drug delivery. The objectives of this study were to: 1) develop echogenic heat sensitive liposomes (E-LTSL) and non-thermosensitive liposomes (E-NTSL) to enhance half-life of contrast agents, and 2) measure motion compensated temperature induced state changes in acoustic impedance and Laplace pressure of liposomes to monitor temperature and doxorubicin (Dox) delivery to tumors. LTSL and NTSL containing Dox were co-loaded with an US contrast agent (perfluoropentane, PFP) using a one-step sonoporation method to create E-LTSL and E-NTSL.

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.

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.

Single nanoparticle detection for multiplexed protein diagnostics with attomolar sensitivity in serum and unprocessed whole blood.

Although biomarkers exist for a range of disease diagnostics, a single low-cost platform exhibiting the required sensitivity, a large dynamic-range and multiplexing capability, and zero sample preparation remains in high demand for a variety of clinical applications. The Interferometric Reflectance Imaging Sensor (IRIS) was utilized to digitally detect and size single gold nanoparticles to identify protein biomarkers in unprocessed serum and blood samples. IRIS is a simple, inexpensive, multiplexed, high-throughput, and label-free optical biosensor that was originally used to quantify biomass captured on a surface with moderate sensitivity.

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.