Robust 3D Quantitative Phase Imaging

Biomedical research relies upon quantitative imaging methods to measure functional and structural data about microscopic organisms. Recently-developed quantitative phase imaging (QPI) methods use jointly designed optical and computational systems to recover structural quantitative phase information for biological samples. However, these methods have not seen wide adoption in biological research because the optical systems can be difficult to use and the computational algorithms often require expert operation for consistently high-quality results. QPI systems are usually developed under a computational imaging framework, where the optical measurement system is jointly designed with the computational reconstruction algorithm. Designing QPI systems for robust and practical real-world use is often difficult, however, because each imaging and computational configuration has unique and difficult-to-quantify practical implications for the end-user./nIn this dissertation, I present three frameworks for increasing the robustness and practicality of computational imaging systems, and I demonstrate the usefulness of these three frameworks by applying them to 2D and 3D quantitative phase imaging systems. First, algorithmic self-calibration directly recovers imaging system parameters from data measurements, doing away with the need for extensive pre-calibration steps and ensuring greater calibration accuracy for non-ideal, real-world systems. I present a robust and efficient self-calibration algorithm for angled coherent illumination, which has enabled new QPI system designs for 2D Fourier ptychographic microscopy (FPM) and 3D intensity optical diffraction tomography (ODT) that would have otherwise been infeasible. Second, increased measurement diversity better encodes useful information across measurements, which can reduce imaging system complexity, data requirements, and computation time. I present a novel pupil-coded intensity ODT system designed to increase measurement diversity of 3D refractive index (RI) information by including joint illumination- and detection-side coding for improved volumetric RI reconstructions. Finally, physics-based machine learning uses a data-driven approach to directly optimize imaging system parameters, which can improve imaging reconstructions and build intuition for better designs of complicated computational imaging systems. I show results from a physics-based machine learning algorithm to optimize pupil coding masks for 3D RI reconstructions of thick cell clusters in the pupil-coded intensity ODT system./nIn addition, I provide practical methods for the design, calibration, and operation of Fourier ptychography, intensity-only ODT, and pupil-coded intensity ODT microscopes to aid in the future development of robust QPI systems. I additionally present a validation of joint system pupil recovery using FPM and a comparison of the accuracy and computational complexity of coherent light propagation models that are commonly used in 3D quantitative phase imaging. I also compare field-based 3D RI reconstructions to intensity-based RI reconstructions, concluding that the proposed pupil-coded intensity ODT system captures similarly diverse phase information to field-based ODT microscopes./nThroughout this work, I demonstrate that by using the frameworks of algorithmic self-calibration, increased system measurement diversity, and physics-based machine learning for computational imaging system design, we can develop more robust quantitative phase imaging systems that are practical for real-world use.

Advisor: Waller, Laura