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Title: Theory, Development, and Application of Quantitative Phase Imaging Modalities on Standard Microscope Platforms
Committee:
Dr. Gaylord, Advisor
Dr. Cai, Chair
Dr. Chang
Abstract:
The object of the proposed research is to develop and generalize quantitative phase imaging (QPI) methods to enable their more widespread use as well as application of QPI to new classes of objects. Microscopic qualitative phase imaging has already produced impressive progress in biological and medical research. QPI is now being used ever more wildly in these existing fields as well as in industrial applications such as optical fiber characterization. QPI is not only quantitative in nature, but also label-free and thus able to image live cells in their natural, unperturbed environment. However, the conventional approach for QPI typically involves expensive custom stand-alone systems. Therefore, to meet the growing QPI need and reduce the cost, the Optics Laboratory has developed several new QPI methods that can be implemented on existing standard commercial microscope platforms. However, these methods have some shortcomings and limitations, so my proposed research focuses on improving these methods. For example, developing a linear analytical nonparaxial partially coherent 3D imaging theory is needed. An existing 2D QPI method is proposed to be generalized to the nonparaxial condition without increasing computational load by using the newly developed 3D imaging theory. An iterative regularization algorithm is proposed to be developed for the 3D QPI method, so that the refractive index can be reconstructed with high accuracy and with fewer tomography angles, which will enable faster measurements. The effect of annular illumination is proposed to be investigated in both simulation and experiments. It is further proposed to apply 3D QPI to the simulation of and experimental measurement of biological cells and fiber Bragg gratings, which will necessitate significant modifications of existing experimental procedures and digital imaging processing algorithms.
