UCLA

Optical Microscopy has become an indispensible tool for many scientific disciplines especially in biomedical sciences. Although rapid advancements in modern microscopy techniques allow us to visualize the microscopic structures and processes in unprecedented details, they are still relatively bulky and low-throughput, necessitating a tedious mechanical scanning to image large-area micro-systems. In this dissertation, I demonstrate an on-chip computational microscopy platform as an alternative high-throughput screening tool that can rapidly monitor fluorescently labeled cells or small animal models over an ultra-wide field-of-view (FOV) of e.g., ≥9–18 cm² without the use of any lenses, thin-film filters or mechanical scanners. In this technique, fluorescent excitation is achieved through a prism or hemispherical-glass interface illuminated by an incoherent source such as a light emitting diode (LED). After interacting with the entire object volume, this excitation light is rejected by total-internal-reflection (TIR) process that is occurring at the bottom of the sample micro-fluidic chip. The fluorescent emission from the excited objects, which does not entirely obey TIR, is then collected by a planar or tapered fiber-optic faceplate and is delivered to an optoelectronic sensor array such as a charge-coupled-device (CCD). By using a compressive sensing/sampling based decoding algorithm, the acquired lensfree raw fluorescent images of the sample can then be rapidly processed to yield e.g., ≤3-4μm spatial resolution over the entire FOV. Moreover, vertically stacked micro-channels that are separated by e.g., 50-100 μm can also be successfully imaged using the same lensfree on-chip microscopy platform, further increasing the overall throughput of this modality. Such a computational fluorescent microscopy technique, with a rapid compressive decoder behind it, could pave the way toward rapid diagnostic systems for biomedical applications, including on-chip cytometry, rare-cell analysis, as well as small animal research.