A light-efficient microscope for fast volumetric imaging of photon starved samples

Introduction

Bioluminescence microscopy offers a powerful tool for background-free imaging of biological samples without an excitation laser. This enabling technology would afford a wide range of applications in the life sciences, where fluorescence microscopy is prohibitive.

Current Limitations

Currently, commercial solutions for bioluminescence imaging suffer from low spatiotemporal resolution due to photon-starved samples.

Project Goals

LowLiteScope aims to overcome these limitations by radically redesigning the optical path, data acquisition, and post-processing based on artificial intelligence.

Innovative Approach

LowliteScope leverages a new light field approach to capture the spatial and angular information of light rays that pass through the sample. In contrast to conventional light field microscopes, this technique records three-dimensional images with high spatial resolution and a large depth of field.

Deep Learning Models

To reconstruct the 3D volume from single exposure light field images, we will use new deep learning models based on artificial intelligence (WP1). The use of generalized and optics-informed deep learning techniques will also increase the spatial resolution beyond conventional light field microscopes.

Performance Testing

We will test the performance of the LowLiteScope prototype using photosensitive samples and samples with high intrinsic autofluorescence (WP2) - two properties that often render long-term, high-resolution imaging via fluorescence microscopy difficult.

Adoption Strategy

Ultimately, success is measured by the ease of adopting our technology. To facilitate the adoption of LowLiteScope by the end user, we propose a new lens design, which can be used as a modular add-on to any conventional fluorescence microscope (WP3).

Conclusion

In summary, LowLiteScope marks a significant breakthrough in bioluminescence microscopy. Its ability to non-invasively capture 3D images of live cells and tissues with high precision will be an invaluable asset for the advancement of biomedical research.