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.

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. No spatial constraints exist for the sources of the first two; they have been inferred only from modelling the patterns seen in the trace gas sodium observed far from Io. Here we report observations that reveal a spatially confined stream that ejects sodium only from the wake of the Io-torus interaction, together with a visually distinct, spherically symmetrical outflow region arising from atmospheric sputtering. The spatial extent of the ionospheric wake that feeds the stream is more than twice that observed by the Galileo spacecraft and modelled successfully. This implies considerable variability, and therefore the need for additional modelling of volcanically-driven, episodic states of the great jovian nebula.

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.

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. This kind of autoregressive structure enables missing/degraded sinogram samples to be linearly predicted using a simple shift-invariant linear combination of neighboring samples. Our theory also further implies that if sinogram samples are assembled into a structured Hankel/Toeplitz matrix, then the matrix will be expected to have low-rank characteristics. As a result, sinogram restoration problems can be formulated as structured low-rank matrix recovery problems. Illustrations of this approach are provided using several different (real and simulated) X-ray imaging datasets, including comparisons against a state-of-the-art deep learning approach. Results suggest that structured low-rank matrix methods for sinogram recovery can have comparable performance to state-of-the-art approaches. Although our evaluation focuses on competitive comparisons against other approaches, we believe that autoregressive constraints are actually complementary to existing approaches with strong potential synergies.

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. Here, we introduce a wide-field interferometric microscopy technique augmented by computational imaging to demonstrate a 2-fold lateral resolution improvement over a large field-of-view (>100 × 100 µm2), enabling simultaneous imaging of more than 104 BNPs at a resolution of ∼150 nm without any labels or sample preparation. We present a rigorous vectorial-optics-based forward model establishing the relationship between the intensity images captured under partially coherent asymmetric illumination and the complex permittivity distribution of nanoparticles. We demonstrate high-throughput morphological visualization of a diverse population of Ebola virus-like particles and a structurally distinct Ebola vaccine candidate. Our approach offers a low-cost and robust label-free imaging platform for high-throughput and high-resolution characterization of a broad size range of BNPs.

Improved vessel morphology measurements in contrast-enhanced multi-detector computed tomography coronary angiography with non-linear post-processing.

Multi-detector computed tomography (MDCT) permits detection of coronary plaque. However, noise and blurring impair accuracy and precision of plaque measurements. The aim of the study was to evaluate MDCT post-processing based on non-linear image deblurring and edge-preserving noise suppression for measurements of plaque size. Contrast-enhanced MDCT coronary angiography was performed in four subjects (mean age 55 +/- 5 years, mean heart rate 54 +/- 5 bpm) using a 16-slice scanner (Siemens Sensation 16, collimation 16 x 0.75 mm, gantry rotation 420 ms, tube voltage 120 kV, tube current 550 mAs, 80 mL of contrast). Intravascular ultrasound (IVUS; 40 MHz probe) was performed in one vessel in each patient and served as a reference standard. MDCT vessel cross-sectional images (1 mm thickness) were created perpendicular to centerline and aligned with corresponding IVUS images. MDCT images were processed using a deblurring and edge-preserving noise suppression algorithm. Then, three independent blinded observers segmented lumen and outer vessel boundaries in each modality to obtain vessel cross-sectional area and wall area in the unprocessed MDCT cross-sections, post-processed MDCT cross-sections and corresponding IVUS. The wall area measurement difference for unprocessed and post-processed MDCT images relative to IVUS was 0.4 +/- 3.8 mm2 and -0.2 +/- 2.2 mm2 (p < 0.05), respectively. Similarly, Bland-Altman analysis of vessel cross-sectional area from unprocessed and post-processed MDCT images relative to IVUS showed a measurement difference of 1.0 +/- 4.4 and 0.6 +/- 4.8 mm2, respectively. In conclusion, MDCT permitted accurate in vivo measurement of wall area and vessel cross-sectional area as compared to IVUS. Post-processing to reduce blurring and noise reduced variability of wall area measurements and reduced measurement bias for both wall area and vessel cross-sectional area.

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.

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.

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.e. calcium) and soft tissue. The result is a quantifiable reduction in blooming artifacts without the introduction of new distortions away from the blooming observed in other methods. Results are shown for simulations, phantoms, ex vivo, and in vivo studies.

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. In this paper, we present a formulation which incorporates both spatial and temporal correlations, and show through simulations that this new formulation yields higher accuracy and greater robustness with respect to image noise. We also show using rectal cancer tumor images that this new formulation results in better segregation of normal and cancerous voxels.