UCLA

The continuous advancement in microscopy has been unveiling the hidden world of tissues, cells, and molecules. In the quest for deeper spatiotemporal insights into biological processes, light field microscopy (LFM) has emerged as a powerful and intriguing tool. Unlike traditional imaging systems that capture focused images, LFM records multiplexed signals with single snapshot that encodes information within a three-dimensional (3D) volume. By leveraging computational reconstruction algorithms, this approach enables the observation of transient volumetric dynamics with remarkable efficiency and speed. This thesis presents a series of efforts to apply LFM in functional imaging, enabling researchers to monitor real-time changes in live organisms, including ion fluxes, electrical signaling, and cells interactions. The exceptional temporal resolution makes LFM a unique tool to visualize and analyze rapid processes that are difficult to capture with conventional 3D microscopy. We demonstrated calcium imaging of motor neurons in freely moving C. elegans and tracked flowing blood cells in-vivo within a beating zebrafish heart. The excessive and unpredictable motion observed in these processes requires capturing hundreds of 3D volumes per second, a demanding but necessary task that provides insights into the underlying mechanisms of neural and cardiac functions. We further moved forward to voltage imaging, a frontier in neuroscience, which directly measures the neural action potential as well as sub-thresholding activities. Our LFM provides kilohertz volumetric imaging on leech ganglion and mouse hippocampus. It measures 7.3 gigavoxels per second in a 3D field of view of 550×550×300 μm^3, which makes it capable of recording the accurate timing and waveform of neural spikes across entire volume. These demonstrations are achieved through several innovative redesigns of LFM, detailed in Chapter 3 to 5. The first approach, VCD-LFM, addresses the inherent trade-off between spatial resolution and depth information in light field imaging by introducing a learning-based reconstruction algorithm. By incorporating data priors and constraints, this method aims to mitigate the issues of low spatial resolution and artifacts in conventional LFM without compromising imaging speed. The second approach, Squeezed Light Field Microscopy (SLIM), leverages data redundancy in light fields and revises the optical hardware to achieve kilohertz volume rate. Designed to meet the high-speed demands of voltage imaging, SLIM offers a powerful and robust imaging tool for sparse volumetric processes. Lastly, the third approach, Light Field Tomography (LIFT), adapts LFM for one-dimensional (1D) measurements through optical Radon transformation. This method enables the use of low-dimensional detectors, such as line sensor, to capture high-dimensional light fields, resulting in enhanced sensitivity, reduced cost and even greater temporal resolution.