Fluorescence microscopy has become an indispensable technology to push fundamental neuroscience by recovering labeled neural structures with high resolution. To enable these studies, the field has adopted the use of low-cost widefield 1-photon epi-fluorescence microscopes to image fixed samples and miniaturized head-mounted miniscopes to monitor neural activity in freely behaving animals. However, fluorescence imaging platforms face a number of challenges such as a limited depth of field (DoF), lack of optical sectioning, and susceptibility to scattering and aberrations which compromises the image quality and signal fidelity. As a result, neural studies are often constrained to a shallow volume near the surface of the sample and are limited by high noise and background. To overcome these challenges, this thesis introduces two novel frameworks that combine pupil engineering with computational imaging to push the performance of miniaturized and tabletop fluorescence neural imaging platforms. These strategies will directly optimize and integrate custom phase elements on the often-vacant pupil plane to enable the encoding of extended fluorescence signals by designing a point spread function (PSF) that exhibits an extended depth of field (EDoF) in scattering media. Next, these strategies will use tailored post processing algorithms to recover that extended information from the resulting images. As a result, this strategy allows for the recovery of sources in an extended neural volume without compromising the optical resolution or imaging speed on the underlying platform. First, this thesis introduces EDoF-Miniscope, a miniaturized neural imaging platform which utilizes a novel physics-informed genetic algorithm to optimize a lightweight binary diffractive optical element (DOE) on the pupil plane. By integrating the binary DOE into a prototype platform, EDoF-Miniscope is able to achieve a 2.8x extension in the DoF between twin imaging foci in neural samples. To enable the recovery of the extended sources, this thesis utilizes a straightforward post-processing filter, which can recover neuronal signals with an SBR down to 1.08. Overall, this framework introduces a generalizable, compact and lightweight solution for augmenting miniscopes with a computational EDoF. Next, I improve upon the proposed framework by designing a flexible 1-photon widefield tabletop platform, entitled EDoF-Tabletop, that exhibits comparable field-of-view (FoV, FoV = 0.6x0.6mm), numerical aperture (NA, NA = 0.5) and aberrations to a miniscope. This platform utilizes a spatial light modulator (SLM) on the pupil plane to rapidly deploy optimized pupil phase profiles without the need of manufacturing, aligning and integrating miniaturized optics. EDoF-Tabletop incorporates a deep optics pipeline, which utilizes novel physical modeling, initialization and training strategies to simultaneously and reliably learn a user-defined EDoFs and a reconstruction using synthetic-only data. As a result, EDoF-Tabletop is able to encode and recover signals from EDoFs up to 140-microns deep in neural samples and 400-microns deep in non-scattering samples. By combining pupil engineering with computational imaging, EDoF-Miniscope and EDoF-Tabletop showcase the potential to enhance neural imaging platforms by extracting information from extended volumes in the brain. By focusing on flexible optimization algorithms and rapid prototyping capabilities, the advancements introduced in this thesis promise broader utility across fluorescence microscopy, where capturing detailed information from complex biological samples is essential for advancing scientific understanding.

There is an unprecedented need in neuroscience and medical research for the precise imaging of individual neurons and their interconnectivity in an effort to achieve a more complete understanding of neurological illness and cognitive growth. While several imaging architectures successfully detect active neural tissue, fluorescent imaging through head-mounted microscopes is becoming a standard method of imaging neural circuitry in freely behaving animals. At Boston University, the Gardner Group developed a miniaturized, open-source, single-photon ‘finch-scope’ to spur rapid prototyping in head-mounted miniscope technology. While experimentally convenient, the finch-scope and other miniscope platforms are limited by their native depth of field and may only detect a thin layer of active neurons in a neurological volume. In this Master’s Thesis Project, I will investigate utilizing optical phase masks integrated in the Fourier plane of the finch-scope to invoke a less-diffractive Bessel point spread function. Next, I will experimentally justify the extended depth of field nature of these phase masks by imaging the axial profile of a 10μm fluorescent pinhole object with a modified finch-scope.