Deep learning enhanced light sheet fluorescence microscopy for in vivo 4D imaging of zebrafish heart beating.

ABSTRACT: Time-resolved volumetric fluorescence imaging over an extended duration with high spatial/temporal resolution is a key driving force in biomedical research for investigating spatial-temporal dynamics at organism-level systems, yet it remains a major challenge due to the trade-off among imaging speed, light exposure, illumination power, and image quality. Here, we present a deep-learning enhanced light sheet fluorescence microscopy (LSFM) approach that addresses the restoration of rapid volumetric time-lapse imaging with less than 0.03% light exposure and 3.3% acquisition time compared to a typical standard acquisition. We demonstrate that the convolutional neural network (CNN)-transformer network developed here, namely U-net integrated transformer (UI-Trans), successfully achieves the miti

SUBMITTER: Zhang M 

PROVIDER: S-EPMC11850918 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature