Spectral imaging in preclinical research and clinical pathology.

Spectral imaging methods are attracting increased interest from researchers and practitioners in basic science, pre-clinical and clinical arenas. A combination of better labeling reagents and better optics creates opportunities to detect and measure multiple parameters at the molecular and cellular level. These tools can provide valuable insights into the basic mechanisms of life, and yield diagnostic and prognostic information for clinical applications. There are many multispectral technologies available, each with its own advantages and limitations. This chapter will present an overview of the rationale for spectral imaging, and discuss the hardware, software and sample labeling strategies that can optimize its usefulness in clinical settings

Doctor of Philosophy

dissertationOptics is an old topic in physical science and engineering. Historically, bulky materials and components were dominantly used to manipulate light. A new hope arrived when Maxwell unveiled the essence of electromagnetic waves in a micro perspective. On the other side, our world recently embraced a revolutionary technology, metasurface, which modifies the properties of matter-interfaces in subwavelength scale. To complete this story, diffractive optic fills right in the gap. It enables ultrathin flat devices without invoking the concept of nanostructured metasurfaces when only scalar diffraction comes into play. This dissertation contributes to developing a new type of digital diffractive optic, called a polychromat. It consists of uniform pixels and multilevel profile in micrometer scale. Essentially, it modulates the phase of a wavefront to generate certain spatial and spectral responses. Firstly, a complete numerical model based on scalar diffraction theory was developed. In order to functionalize the optic, a nonlinear algorithm was then successfully implemented to optimize its topography. The optic can be patterned in transparent dielectric thin film by single-step grayscale lithography and it is replicable for mass production. The microstructures are 3?m wide and no more than 3?m thick, thus do not require slow and expensive nanopatterning techniques, as opposed to metasurfaces. Polychromat is also less demanding in terms of fabrication and scalability. The next theme is focused on demonstrating unprecedented performances of the diffractive optic when applied to address critical issues in modern society. Photovoltaic efficiency can be significantly enhanced using this optic to split and concentrate the solar spectrum. Focusing through a lens is no news, but we transformed our optic into a flat lens that corrects broadband chromatic aberrations. It can also serve as a phase mask for microlithography on oblique and multiplane surfaces. By introducing the powerful tool of computation, we devised two imaging prototypes, replacing the conventional Bayer filter with the diffractive optic. One system increases light sensitivity by 3 times compared to commercial color sensors. The other one renders the monochrome sensor a new function of high-resolution multispectral video-imaging

Optimum constrained image restoration filters

The filter was developed in Hilbert space by minimizing the radius of gyration of the overall or composite system point-spread function subject to constraints on the radius of gyration of the restoration filter point-spread function, the total noise power in the restored image, and the shape of the composite system frequency spectrum. An iterative technique is introduced which alters the shape of the optimum composite system point-spread function, producing a suboptimal restoration filter which suppresses undesirable secondary oscillations. Finally this technique is applied to multispectral scanner data obtained from the Earth Resources Technology Satellite to provide resolution enhancement. An experimental approach to the problems involving estimation of the effective scanner aperture and matching the ERTS data to available restoration functions is presented

High-dynamic-range imaging optical detectors

Imaging spectrometers allowing spatially resolved targets to be spectrally discriminated are valuable for remote sensing and defense applications. The drawback of such instruments is the need to quickly process very large amounts of data. In this paper we demonstrate two imaging systems which detect a dim target in a bright background, using the coherence contrast between them, generating much less data but only operating over a limited optical bandwidth. Both systems use a passband filter, a Michelson interferometer, coupling optics and a CCD camera. The first uses the interferometer in a spatial mode, by tilting one of the mirrors to create a set of line fringes on the CCD array. The visibility of these fringes is proportional to the degree of coherence. The interferogram is displayed spatially on the CCD array, as a function of the path differences. The second system uses the interferometer in a temporal mode. A coherent point target and an extended background are imaged through the interferometer onto the CCD array, and one of the interferometer's mirrors is scanned longitudinally to vary the path difference in time. In both cases the coherent target is detected over a large dynamic range down to negative signal-to-background power ratios (in dB). The paper describes an averaging technique to improve the signal-to-noise ratio and correction techniques required to extract interferograms from the images. The spatial technique developed has the advantage of using no moving parts

InSPECtor: an end-to-end design framework for compressive pixelated hyperspectral instruments

Classic designs of hyperspectral instrumentation densely sample the spatial and spectral information of the scene of interest. Data may be compressed after the acquisition. In this paper we introduce a framework for the design of an optimized, micro-patterned snapshot hyperspectral imager that acquires an optimized subset of the spatial and spectral information in the scene. The data is thereby compressed already at the sensor level, but can be restored to the full hyperspectral data cube by the jointly optimized reconstructor. This framework is implemented with TensorFlow and makes use of its automatic differentiation for the joint optimization of the layout of the micro-patterned filter array as well as the reconstructor. We explore the achievable compression ratio for different numbers of filter passbands, number of scanning frames, and filter layouts using data collected by the Hyperscout instrument. We show resulting instrument designs that take snapshot measurements without losing significant information while reducing the data volume, acquisition time, or detector space by a factor of 40 as compared to classic, dense sampling. The joint optimization of a compressive hyperspectral imager design and the accompanying reconstructor provides an avenue to substantially reduce the data volume from hyperspectral imagers.Comment: 23 pages, 12 figures, published in Applied Optic