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Compressed sensing (CS) is a signal processing technique that provides a theoretical framework for accurately reconstructing discrete signals from fewer samples than traditionally dictated by the Shannon-Nyquist theorem. In the context of imaging, CS enables the recovery of images with more resolved points than pixels in the physical sensor. This capability is appealing for minimally invasive biomedical imaging applications that suffer from poor image quality due to inherent constraints on the size and type of hardware that can be used. The goal of this dissertation is to adapt the CS framework for use in endoscopy platforms, providing a path toward higher resolution minimally invasive imaging.Endoscopes commonly use coherent fiber optic bundles to facilitate in vivo imaging. The image quality with these fiber-bundle-based endoscopes is limited because of manufacturing challenges that restrict achievable fiber density and spacing. Chapter 1 reviews endoscopy technologies and current fiber-bundle- based imaging techniques. The general field of computational imaging is then discussed, including a specific focus on CS-based and spectral imaging approaches that may overcome limitations in fiber bundle imaging.Chapter 2 identifies and addresses some practical challenges that are not anticipated by CS theory or simulations. Computational imaging based on the CS framework, or compressive imaging (CI), was evaluated with a test platform that introduced intensity modulation at a conjugate image plane. It was demonstrated that a CS model accounting for system-specific practical limitations, like optical aberration, is an efficient way to implement highly parallel CI.An imaging architecture with intensity modulation at a conjugate image plane is one approach for CI, but the development of different CS mathematical models has given rise to various different CI architectures. Chapter 3 provides a comparison of different architectures with the application of endoscopy in mind. An experimental comparison of two candidate architectures was performed, and it was determined that an architecture with coded masks at a conjugate image plane is a good option for translation to endoscopy.Chapters 2 and 3 developed imaging methods for CI in test platforms where image quality was limited by the number of pixels in a low-resolution sensor. Chapter 4 translates these methods for fiber-bundle-based imaging where image quality is limited by the number of fibers in an imaging bundle. The fiber bundle was considered as a low-resolution sensor array where the number of resolved points in an image is limited by the number of fibers in the bundle. CI was evaluated in a fiber-bundle-based imaging platform for compressive endoscopy, which was demonstrated for fluorescence imaging and resolved 17 points for each fiber in the bundle.While CI resolves multiple pixels within the diameter of each fiber in thebundle, inter-fiber cladding that binds fibers together blocks information that is not recovered by traditional CI methods. Chapter 4 presents a solution to this missing information problem using spectral coding with a distal prism to ensure information from all sample positions is transmitted through the fibers and captured by proximal color camera. Integrating spectral coding into the compressive endoscopy platform allowed for image reconstruction that filled in 80% of the missing information. Additionally, spectral coding implemented as a standalone technology without CI generated images with three times more resolved points than images from traditional fiber-bundle-based imaging methods. This snapshot spectral coding approach improves image quality in fiber-bundle-based imaging without requiring distal electromechanical components. The computational imaging methods developed here serve as an initial step toward analyzing tissue morphology using CS principles and spectral coding for in vivo optical biopsy.

Radiofrequency (RF) ablation has emerged as a promising curative therapy for atrial fibrillation and other supraventricular tachyarrhythmias. Most RF ablation procedures create a pattern of linear lesions that either isolate ectopic focal triggers or divide the atria into functional regions too small to sustain macroreentry. It is thought that these linear lesions must be complete (i.e. both continuous and transmural) in order to block arrhythmic conduction and to avoid creation of a more proarrhythmic substrate. Currently, most ablation endpoints employ pacing-based metrics of lesion completeness that rely upon active tissue properties and are therefore limited by transient or delayed effects of RF on conduction. Myocardial electrical impedance is a passive tissue property that may provide an alternative intraoperative metric that overcomes certain limitations of pacing-based endpoints. Because RF ablation markedly alters the tissue, it was hypothesized that linear ablation lesions change myocardial impedance. Custom instrumentation and data acquisition software were developed to measure impedance using the four-electrode method. Computer simulations were performed to determine the effect of electrode size on four-electrode impedance measurement. Receiver operating characteristic analysis was used to assess the diagnostic accuracy of both resistivity and translesion stimulus-excitation delay to predict completeness of individual lesions. Lesions (n=50) were created in the ventricles of 18 Langendorff-perfused rabbit hearts using a linear epicardial ablation probe. Lesions were either continuous and transmural (n=25), noncontinuous (n=18), or nontransmural (n=7), which was verified histologically after staining with 2,3,5-triphenyltetrazolium chloride. Subthreshold AC current (10 μA, 1 kHz) and four electrodes in a linear array across the lesion were used to measure magnitude and phase of the tissue impedance. When a continuous and transmural lesion was produced, the resistivity increased 26-58 Ωcm (p

The U.S. Navy's requirement for a computer prediction system for marine fog and stratus dates back to the 1970s when meteorological models were being introduced to the fleet. The Naval Research Laboratory's Coupled Ocean/ Atmosphere Mesoscale Prediction System (COAMPS) is a leap forward in the Navy's numerical modeling ability but it still does not show great skill in fog forecasting. COAMPS has been 'tuned', or adjusted for certain constants and parameterizations, so that it has the minimum error for the maximum area. This tuning is a common practice for all numerical models. The objective of this thesis is to determine if changes can be made to the existing COAMPS code based on reasonable physical experiments for a specific location to help solve the numerical fog forecasting problem. The effectiveness of these experiments was first measured by comparing a modeled cloud edge to satellite imagery of Monterey, California taken during a week in August 2000 under a variety of foggy conditions. Comparisons were also made with observations taken from an aircraft, land stations and a vertical profiler. The experiments, specifically those regarding changes to the autoconversion and turbulent kinetic energy schemes, showed that while a perfect solution has not been found, it is possible to modify the model physics codes and optimize its performance in a specific region. http://archive.org/details/numericalpredict1094510859