Optimizing image capture for low-light widefield quantitative fluorescence microscopy
Low-light optical imaging refers to the use of cameras to capture images with minimal photon flux. This area has broad application to diverse fields, including optical microscopy for biological studies. In such studies, it is important to reduce the intensity of illumination to reduce adverse effect...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2025-03-01
|
| Series: | APL Photonics |
| Online Access: | http://dx.doi.org/10.1063/5.0245239 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Low-light optical imaging refers to the use of cameras to capture images with minimal photon flux. This area has broad application to diverse fields, including optical microscopy for biological studies. In such studies, it is important to reduce the intensity of illumination to reduce adverse effects such as photobleaching and phototoxicity that may perturb the biological system under study. The challenge when minimizing illumination is to maintain image quality that reflects the underlying biology and can be used for quantitative measurements. An example is the optical redox ratio, which is computed from autofluorescence intensity to measure metabolism. In all such cases, it is critical for researchers to optimize the selection and application of scientific cameras to their microscopes, but few resources discuss performance in the low-light regime. In this tutorial, we address the challenges in optical fluorescence imaging at low-light levels for quantitative microscopy, with an emphasis on live biological samples. We analyze the performance of low-light scientific cameras including electron-multiplying charge-coupled device, scientific complementary metal oxide semiconductor (sCMOS), and the photon-counting sCMOS architecture, termed quantitative CMOS, while considering the differences in platform architecture and the contribution of various sources of noise. The tutorial covers a detailed discussion of user-controllable parameters, as well as the application of post-processing algorithms for denoising. We illustrate these concepts using autofluorescence images of live mammalian embryos captured with a two-photon light sheet fluorescence microscope. |
|---|---|
| ISSN: | 2378-0967 |