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. To determine temperature induced intensity variation with respect to the state change of E-LTSL and E-NTSL in mouse colon tumors, cine acquisition of 20 frames/second for about 20 min (or until wash out) at temperatures of 42°C, 39.5°C, and 37°C was performed. A rigid rotation and translation was applied to each of the "key frames" to adjust for any gross motion that arose due to motion of the animal or the transducer. To evaluate the correlation between ultrasound (US) intensity variation and Dox release at various temperatures, treatment (5 mg Dox/kg) was administered via a tail vein once tumors reached a size of 300-400 mm(3), and mean intensity within regions of interest (ROIs) defined for each sample was computed over the collected frames and normalized in the range of [0,1]. When the motion compensation technique was applied, a > 2-fold drop in standard deviation in mean image intensity of tumor was observed, enabling a more robust estimation of temporal variations in tumor temperatures for 15-20 min. due to state change of E-LTSL and E-NTSL. Consequently, a marked increase in peak intensity at 42°C compared to 37°C that corresponded with enhanced Dox delivery from E-LTSL in tumors was obtained. Our results suggest that echogenic liposomes provide a predictable change in tumor vascular contrast with temperature, and this property could be applicable to nanomonitoring of drug delivery in real time.

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. Image noise and artifacts are generally much more severe than in medical CT and can lead to splitting of objects and inaccurate object labeling. The conventional approach performs object segmentation and material identification in two decoupled processes. Dual-energy information is typically not used for the segmentation, and object localization is not explicitly used to stabilize the material parameter estimates. We propose a novel learning-based framework for joint segmentation and identification of objects directly from volumetric DECT images, which is robust to streaks, noise and variability due to clutter. We focus on segmenting and identifying a small set of objects of interest with characteristics that are learned from training images, and consider everything else as background. We include data weighting to mitigate metal artifacts and incorporate an object boundary field to reduce object splitting. The overall formulation is posed as a multilabel discrete optimization problem and solved using an efficient graph-cut algorithm. We test the method on real data and show its potential for producing accurate labels of the objects of interest without splits in the presence of metal and clutter.

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. The effectiveness of the framework is demonstrated on experimental data.

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. We first analyze the structure of discrete linear inverse problem cost functions to show that the obstacle to the application of graph-cut methods to their solution is the variable mixing caused by the presence of the linear sensing operator. We then propose to use a surrogate energy functional that overcomes the challenges imposed by the sensing operator yet can be utilized efficiently in existing graph-cut frameworks. We use this surrogate energy functional to devise a monotonic iterative algorithm for the solution of discrete valued inverse problems. We first provide experiments using local convolutional operators and show the robustness of the proposed technique to noise and stability to changes in regularization parameter. Then we focus on nonlocal, tomographic examples where we consider limited-angle data problems. We compare our technique with state-of-the-art discrete and continuous image reconstruction techniques. Experiments show that the proposed method outperforms state-of-the-art techniques in challenging scenarios involving discrete valued unknowns.

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. The development of a reliable POC viral test for the primary care setting would decrease the time for diagnosis leading to a lower chance of transmission and improve recovery. The single particle interferometric reflectance imaging sensor (SP-IRIS) has been shown to be a sensitive and specific-detection platform in serum and whole blood. This paper presents a step towards a POC viral assay through a SP-IRIS prototype with automated data acquisition and analysis and a simple, easy-to-use software interface. Decreasing operation complexity highlights the potential of SP-IRIS as a sensitive and specific POC diagnostic tool. With the integration of a microfluidic cartridge, this automated instrument will allow an untrained user to run a sample-to-answer viral assay in the POC setting.

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. Here we demonstrate detection of ß-lactoglobulin, a cow's milk whey protein spiked in serum (>10 orders of magnitude) and whole blood (>5 orders of magnitude), at attomolar sensitivity. The clinical utility of IRIS was demonstrated by detecting allergen-specific IgE from microliters of characterized human serum and unprocessed whole blood samples by using secondary antibodies against human IgE labeled with 40 nm gold nanoparticles. To the best of our knowledge, this level of sensitivity over a large dynamic range has not been previously demonstrated. IRIS offers four main advantages compared to existing technologies: it (i) detects proteins from attomolar to nanomolar concentrations in unprocessed biological samples, (ii) unambiguously discriminates nanoparticles tags on a robust and physically large sensor area, (iii) detects protein targets with conjugated very small nanoparticle tags (~40 nm diameter), which minimally affect assay kinetics compared to conventional microparticle tagging methods, and (iv) utilizes components that make the instrument inexpensive, robust, and portable. These features make IRIS an ideal candidate for clinical and diagnostic applications.

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. It also provides robust reconstructions in the case of sparse and reduced observation apertures. The effectiveness of the proposed imaging strategy is demonstrated using experimental data.