Summary
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.
Currently, commercial solutions for bioluminescence imaging suffer from low spatiotemporal resolution, due to photon-starved samples. LowLiteScope aims to overcome these limitations by radically redesigning the optical path, data acquisition and post processing based on artificial intelligence.
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 microscope, this technique records three-dimensional images with high spatial resolution and a large depth of field. To econstruct 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. 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. Ultimately, success is measured by the ease to adopt our technology. To facilitate the adoption of LowLiteScope by the enduser, we propose a new lens design, which can be used as a modular add-on to any conventional, fluorescence microscopes (WP3).
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.
Currently, commercial solutions for bioluminescence imaging suffer from low spatiotemporal resolution, due to photon-starved samples. LowLiteScope aims to overcome these limitations by radically redesigning the optical path, data acquisition and post processing based on artificial intelligence.
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 microscope, this technique records three-dimensional images with high spatial resolution and a large depth of field. To econstruct 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. 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. Ultimately, success is measured by the ease to adopt our technology. To facilitate the adoption of LowLiteScope by the enduser, we propose a new lens design, which can be used as a modular add-on to any conventional, fluorescence microscopes (WP3).
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.
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| Web resources: | https://cordis.europa.eu/project/id/101138041 |
| Start date: | 01-01-2024 |
| End date: | 30-06-2025 |
| Total budget - Public funding: | - 150 000,00 Euro |
Cordis data
Original description
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.Currently, commercial solutions for bioluminescence imaging suffer from low spatiotemporal resolution, due to photon-starved samples. LowLiteScope aims to overcome these limitations by radically redesigning the optical path, data acquisition and post processing based on artificial intelligence.
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 microscope, this technique records three-dimensional images with high spatial resolution and a large depth of field. To econstruct 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. 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. Ultimately, success is measured by the ease to adopt our technology. To facilitate the adoption of LowLiteScope by the enduser, we propose a new lens design, which can be used as a modular add-on to any conventional, fluorescence microscopes (WP3).
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.
Status
SIGNEDCall topic
ERC-2023-POCUpdate Date
12-03-2024
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