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LowLiteScope

Financiado

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

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 fluore... ver más

30/06/2025

FUNDACIO INSTITUT...

150K€

Presupuesto del proyecto: 150K€

Líder del proyecto

FUNDACIO INSTITUT DE CIENCIES FOTONIQUES Otra investigación y desarrollo experimental en ciencias naturales y técnicas asociacion

TRL 4-5 | 50K€

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Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-09-20

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2023-POC: ERC PROOF OF CONCEPT GRANTS

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FUNDACIO INSTITUT DE CIENCIE...

150.00K€ | Lider

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Últimas noticias

27-11-2024: DGIPYME En las últimas 48 horas el Organismo DGIPYME ha otorgado 1 concesiones

27-11-2024: FUNDACION-EOI En las últimas 48 horas el Organismo FUNDACION EOI ha otorgado 2 concesiones

25-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 52 concesiones

Duración del proyecto: 21 meses Fecha Inicio: 2023-09-20
Fecha Fin: 2025-06-30

Líder del proyecto

FUNDACIO INSTITUT DE CIENCIES FOTONIQUES

TRL 4-5 | 50K€

Presupuesto del proyecto 150K€

Descripción del proyecto 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.

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