TAG‐SPARK: Empowering High‐Speed Volumetric Imaging With Deep Learning and Spatial Redundancy

Abstract

Abstract Two‐photon high‐speed fluorescence calcium imaging stands as a mainstream technique in neuroscience for capturing neural activities with high spatiotemporal resolution. However, challenges arise from the inherent tradeoff between acquisition speed and image quality, grappling with a low signal‐to‐noise ratio (SNR) due to limited signal photon flux. Here, a contrast‐enhanced video‐rate volumetric system, integrating a tunable acoustic gradient (TAG) lens‐based high‐speed microscopy with a TAG‐SPARK denoising algorithm is demonstrated. The former facilitates high‐speed dense z‐sampling at sub‐micrometer‐scale intervals, allowing the latter to exploit the spatial redundancy of z‐slices for self‐supervised model training. This spatial redundancy‐based approach, tailored for 4D (xyzt) dataset, not only achieves >700% SNR enhancement but also retains fast‐spiking functional profiles of neuronal activities. High‐speed plus high‐quality images are exemplified by in vivo Purkinje cells calcium observation, revealing intriguing dendritic‐to‐somatic signal convolution, i.e., similar dendritic signals lead to reverse somatic responses. This tailored technique allows for capturing neuronal activities with high SNR, thus advancing the fundamental comprehension of neuronal transduction pathways within 3D neuronal architecture.

Keywords

• deep‐learning noise reduction
• high‐speed volumetric image
• neural networks
• Purkinje cells
• two‐photon microscopy